The Definitive Guide to Claims Management Software

Introduction: Why claims is being rebuilt

Claims is the moment insurance stops being a promise and becomes a product. Everything before it — pricing, distribution, underwriting, brand — is preparation. Everything after it is consequence.

That has always been true. What has changed is that the gap between a well-run claims operation and a poorly-run one has become measurable, visible and financially decisive in a way it was not fifteen years ago. Four forces are driving that.

1. Customer expectations have been reset by other industries

Nobody compares their claim experience to another claim. They compare it to the last thing that went well: a parcel tracked to the doorstep, a bank transfer that cleared instantly, a refund that arrived before the return did. Against that baseline, “we’ll write to you within ten working days” reads as failure, regardless of how technically correct the outcome is.

The practical consequence for claims teams is that communication cadence now matters as much as claim outcome. Two claimants receiving identical settlements will report wildly different satisfaction depending on whether they had to chase. Silence is interpreted as inaction, and inaction is interpreted as bad faith.

2. Costs are rising faster than the tools used to control them

Claims inflation is not one problem; it is several stacked on top of each other. Repair complexity (sensors, ADAS calibration, EV battery packs), building materials and labour, medical and care costs, third-party legal costs, and — in several markets — litigation funding and claims-farming behaviour. Meanwhile the indemnity spend is only part of the picture: loss adjusting expense, handler time, supplier management and rework all sit on top.

Note on evidence. Claims inflation figures vary enormously by market, class and year, and industry-published numbers are often not comparable. Where you need a figure for a board paper, cite a primary source for your own market — e.g. your national regulator’s claims data, ABI/Insurance Europe/NAIC statistics, or your own portfolio experience — rather than a generic industry average.

3. Regulation has moved from “process” to “outcomes”

The regulatory direction of travel across most developed markets is the same: supervisors are less interested in whether you followed a documented procedure and more interested in whether the customer ended up in a fair place. In the UK, the FCA’s Consumer Duty makes this explicit — firms must be able to evidence good outcomes, not merely assert them. Comparable pressures exist elsewhere: conduct-of-business supervision under IDD in the EU, market conduct examinations and unfair claims settlement practice acts in the US, and vulnerability and accessibility expectations almost everywhere.

The operational translation is blunt: if your system cannot produce the evidence, you do not have the evidence. Outcome monitoring, complaint root-cause analysis and vulnerable-customer identification all now depend on structured data that older claims systems were never designed to capture.

4. AI has changed what “automatable” means

Until recently, automation in claims meant rules: deterministic if/then logic operating on structured data. That handled a narrow slice of the work — validation, routing, simple payments — and left the bulk of a handler’s day untouched, because the bulk of a handler’s day is unstructured. Reading emails. Reading reports. Reading photographs. Summarising a file. Drafting a response.

Large language models and modern computer vision have made that unstructured layer tractable for the first time. This is a genuine step change, and it is also the single most over-claimed area in insurance technology.

What this guide argues

Three positions run through everything below. They are stated up front so you can disagree with them knowingly:

• Claims technology is an operating model decision, not a software decision. Buying a platform without redesigning the process typically automates the existing inefficiency at greater expense.

• Configurability beats feature count. Claims processes differ genuinely and legitimately between organisations, classes and jurisdictions. The capability that matters most over a ten-year horizon is how cheaply the system can be changed — not how many features it shipped with.

• Data structure is the compounding asset. Automation, analytics and AI all sit on top of the data model. Organisations that invest in clean, structured, well-governed claims data get better at everything else, cumulatively. Those that do not spend a decade generating PDFs.

Chapter 1
What claims technology actually is

1.1 The plain-English definition

Claims management software is the system of record and system of work for handling claims. It holds everything known about a claim, controls what happens next, and produces the evidence that it happened correctly.

Three verbs, and it is worth separating them because most buying failures come from conflating them:

• Record — a single, permanent, auditable version of the truth for each claim: parties, policy, cover, events, documents, communications, financials, decisions.

• Work — the machinery that moves a claim forward: tasks, queues, diaries, SLAs, approvals, routing, automation, notifications.

• Evidence — the audit trail, reporting and analytics that let you prove to a regulator, reinsurer, auditor, client or board what you did, when, why and with what result.

A system that does the first but not the second is a filing cabinet. One that does the second but not the third is a liability.

More formally, and in the language used in RFPs: a claims management system is an application that supports the end-to-end claims lifecycle — from first notification of loss through validation, investigation, reserving, settlement, payment and recovery to closure — providing case management, workflow orchestration, financial control, document and communication management, and reporting, with configurable rules that enforce the organisation’s claims philosophy consistently at scale.

1.2 What it is not

1.3 A short history, and why the layers still matter

Understanding the evolution explains most of what is strange about claims IT estates today, because in the majority of organisations all four generations are still running simultaneously.

Generation 1 — Ledger systems (1960s–1980s). Mainframe applications built to record claim numbers, reserves and payments. Their purpose was financial control and reinsurance reporting, not handler productivity. Many are still in production. They are usually accurate, extremely fast at what they do, and almost impossible to change.

Generation 2 — Client-server claims systems (1990s). Desktop applications on internal networks, adding screens for handler workflow, notes and simple diaries. Introduced the idea of the claim file as software rather than paper. Also introduced the problem that has haunted the industry since: heavy customisation via code, producing an unupgradeable instance.

Generation 3 — Web-based suites (2000s–2010s). Browser-delivered, integrated with policy and billing, often sold as part of a core suite. Genuinely more capable — configurable workflow, better reporting, portals — but frequently deployed as multi-year, multi-million programmes with high customisation. This era created both the modern claims platform category and the modern fear of claims platform projects.

Generation 4 — Cloud, API-first, composable (2015–present). Multi-tenant or single-tenant cloud delivery, continuous updates, configuration over code, open APIs, low-code workflow design, embedded analytics, and now AI services. The economic shift is important: upgrades stop being projects and become background events, which changes what a ten-year total cost of ownership looks like.

    1970s              1995              2010                2020s
      |                 |                  |                   |
  [ Ledger/mainframe ]  |                  |                   |
    financial control   |                  |                   |
                  [ Client-server ]        |                   |
                    handler screens        |                   |
                                    [ Web suites ]             |
                              integrated, configurable         |
                                                     [ Cloud / API-first ]
                                                continuous, composable, AI-assisted

In most real estates, all four layers are still live and integrated.

1.4 Who uses it

1.5 Six common misconceptions

“It’s just a database with a workflow on top.” The database part is comparatively easy. The hard parts are the financial model (reserves, movements, recoveries, deductibles, multi-currency, multi-party), the configurability of process, and the audit integrity of every change.

“We can build this ourselves.” Organisations regularly can build a v1. The difficulty is the fifteen-year maintenance obligation: regulatory change, integration churn, security patching, and the AI capabilities you will want in three years. Build is sometimes right — particularly for genuinely unique, non-insurance-like processes — but it should be costed over a decade, not a project.

“Automation means replacing handlers.” In practice, the sustainable pattern is redistribution rather than replacement: automation absorbs the low-value repetitive work, and handler capacity moves to complex, high-value, and vulnerable-customer claims where human judgement changes the outcome. Organisations that treat automation purely as a headcount exercise typically hit a service quality wall.

“A big-name platform guarantees success.” Implementation quality, process design and data migration explain more variance in outcome than vendor choice does. Well-run implementations of mid-tier products routinely outperform badly-run implementations of market leaders.

“We’ll fix the data later.” Data quality is not a phase; it is a property of the design. Anything you allow to be captured as free text will be free text forever, and no amount of downstream analytics will recover the structure you chose not to capture.

“Our process is too unusual for standard software.” Sometimes true, usually not. Roughly 70–80% of most claims processes are recognisable variants of a common pattern; the genuinely distinctive part is smaller than teams expect. The right question is not “does it do it our way out of the box?” but “how expensive is it to make it do it our way, and to change that again in two years?”

1.6 The business value, stated honestly

Claims technology creates value in five places. Chapter 7 quantifies each; here is the shape of it:

• Indemnity accuracy — paying the right amount. Reducing leakage is usually the single largest financial lever, because it acts on the biggest number on the page.

• Operational efficiency — cost per claim, handler capacity, rework, administrative overhead.

• Cycle time — faster closure reduces cost (fewer touches, less escalation, less litigation) and improves satisfaction simultaneously. It is one of the rare metrics with no trade-off, up to the point where speed compromises accuracy.

• Customer and client outcomes — retention, complaint volumes, NPS, and for TPAs and MGAs, contract renewal.

• Control and evidence — regulatory defensibility, audit efficiency, reinsurance and capacity-provider confidence.

                    THE CLAIMS TECHNOLOGY ECOSYSTEM
  ┌──────────────────────────── CHANNELS IN ────────────────────────────┐
  │  Phone   Web form   Mobile app   Broker/MGA   Email   Telematics    │
  │  Portal  WhatsApp   API partner  IoT sensor   Post/scan  Aggregator │
  └───────────────────────────────┬─────────────────────────────────────┘
                                  ▼
  ┌─────────────────────────────────────────────────────────────────────┐
  │                      CLAIMS MANAGEMENT PLATFORM                     │
  │                                                                     │
  │   ┌───────────┐  ┌───────────┐  ┌───────────┐  ┌────────────────┐   │
  │   │  System   │  │ Workflow  │  │  Rules &  │  │  Financials    │   │
  │   │ of record │  │  engine   │  │ decisions │  │ reserves/pay   │   │
  │   └───────────┘  └───────────┘  └───────────┘  └────────────────┘   │
  │   ┌───────────┐  ┌───────────┐  ┌───────────┐  ┌────────────────┐   │
  │   │ Documents │  │  Comms    │  │ AI / ML   │  │  Audit & BI    │   │
  │   │ & evidence│  │ orchestr. │  │ services  │  │  reporting     │   │
  │   └───────────┘  └───────────┘  └───────────┘  └────────────────┘   │
  └───┬──────────────┬───────────────┬──────────────┬───────────────────┘
      │              │               │              │
      ▼              ▼               ▼              ▼
  ┌────────┐   ┌───────────┐   ┌───────────┐   ┌──────────────┐
  │ Policy │   │ Suppliers │   │  Finance  │   │  Data / BI   │
  │  admin │   │ & panels  │   │  ERP/pay  │   │  warehouse   │
  ├────────┤   ├───────────┤   ├───────────┤   ├──────────────┤
  │Underwr.│   │ Adjusters │   │ Bank/APM  │   │ Actuarial    │
  │ CRM    │   │ Repairers │   │ Sanctions │   │ Reinsurance  │
  │ Fraud  │   │ Medical   │   │ Tax       │   │ Regulator    │
  │ bureaux│   │ Legal     │   │           │   │ returns      │
  └────────┘   └───────────┘   └───────────┘   └──────────────┘
      ▲              ▲               ▲              ▲
      └──────────────┴───────────────┴──────────────┘
                  Bi-directional APIs / webhooks / events

Chapter 2
The complete claims lifecycle

This chapter walks the entire journey a claim takes, stage by stage. For each stage: what actually happens, what good looks like, what the software should do, and where things typically go wrong.

Not every claim passes through every stage. A simple motor glass claim may go incident → FNOL → validation → settlement → payment → closure in under an hour. A complex construction liability claim may spend four years cycling through investigation, expert assessment and negotiation. The lifecycle below is the superset.

   INCIDENT ──► FNOL ──► VALIDATION ──► ASSIGNMENT ──► INVESTIGATION
                                                            │
     ┌──────────────────────────────────────────────────────┘
     ▼
   COVERAGE ──► RESERVING ──► COMMUNICATION ──► DOCUMENTS ──► EXPERT
   VERIFICATION                    ▲                          ASSESSMENT
                                   │                              │
     ┌─────────────────────────────┴──────────────────────────────┘
     ▼                    (communication runs continuously)
   FRAUD ──► NEGOTIATION ──► SETTLEMENT ──► PAYMENT ──► RECOVERY
   DETECTION                                               │
     ┌────────────────────────────────────────────────────-┘
     ▼
   SUBROGATION ──► CLOSURE ──► REPORTING & LEARNING
                                       │
                                       └──► feeds pricing, underwriting,
                                            risk management, prevention

Stage 1 — Incident

The loss event itself, before anyone tells the insurer. Historically invisible to the claims function; increasingly not.

What’s changing: telematics, connected home devices, fleet dash-cams, building sensors and health & safety systems can now signal an incident before a human reports it. For fleets and self-insured organisations in particular, the “incident” is often already recorded in an internal system (an accident report form, a H&S near-miss log) days before it becomes a claim.

Software capabilities: incident capture separate from claim creation; incident-to-claim conversion with lineage preserved; IoT/telematics event ingestion; duplicate detection across incident sources; risk-management linkage so incidents that never become claims still inform prevention.

Where it goes wrong: incidents recorded in a spreadsheet or safety system that never reaches claims, producing late notification, evidence loss and reserve surprises.

Stage 2 — First Notification of Loss (FNOL)

The moment the claim enters your world. Disproportionately important: research and operational experience consistently show that decisions and data quality at FNOL shape the entire claim’s cost and duration.

What good looks like:

• Multi-channel capture (phone, web, app, broker/partner API, email, WhatsApp) writing into one structure
• Dynamic questioning: the questions asked depend on peril, class and answers already given
• Immediate policy and cover pre-check
• Immediate triage decision: fast-track, standard, complex, or suspicious
• Evidence capture at the point of first contact — photos, video, geolocation, third-party details — while the claimant is still at the scene and still cooperative
• A reference number, expectation-setting and next-step communication before the call ends

Software capabilities: configurable dynamic FNOL forms per product and peril; guided scripting for contact-centre staff; policy lookup at point of notification; automatic claim number generation; photo and document upload from the claimant’s own device via secure link; automatic triage scoring; duplicate/repeat claim detection; out-of-hours capture with structured handover.

The economics of FNOL data: a piece of information that costs thirty seconds to capture at FNOL frequently costs several days and multiple letters to obtain three weeks later. Front-load ruthlessly — but balance against abandonment. Long forms fail. The resolution is conditional logic: ask fewer, smarter questions.

Common mistakes: identical forms for all perils; free-text-heavy capture; asking for information that already exists in the policy record; failing to capture third-party details at the only moment they are readily available.

Stage 3 — Validation

Confirming the claim is real, in force, correctly identified and properly notified.

Checks performed: policy in force at date of loss; premium paid/not in arrears; policyholder identity; insured item/property/person matches; peril reported is a peril the product contemplates; notification within any condition precedent time limits; jurisdiction; sanctions and PEP screening; duplicate claim check; prior claims history.

Software capabilities: automated policy validation via PAS integration; sanctions/watchlist screening (usually via a specialist provider); identity verification integration; automated arithmetic on notification deadlines; instant flagging of anomalies for human review rather than silent rejection.

Best practice: validation should produce three outcomes, not two — pass, fail, and refer. Systems that force a binary decision push handlers into unrecorded workarounds.

Stage 4 — Assignment and triage

Getting the claim to the right person, team or supplier, at the right level of intensity.

Segmentation is one of the highest-leverage decisions in claims. The classic model:

Software capabilities: rules-based and model-based routing; skills-based allocation; caseload balancing and capacity awareness; geographic allocation for field adjusters; automatic panel/supplier instruction; delegated authority checks; re-triage when new information changes complexity.

Common mistake: static triage. A claim triaged as simple at FNOL that later reveals an injury must be able to move tracks automatically — and most operations only discover the misclassification weeks later during a file review.

Stage 5 — Investigation

Establishing what happened, whether the policy responds, who is liable, and what the loss is worth.

Investigation is where handler skill matters most and where technology has historically helped least — largely because the work is reading, asking and judging. That is now shifting (Chapter 6).

Software capabilities: structured investigation checklists by peril and class; evidence register linking each fact to its source document; witness and third-party contact management; site visit scheduling; body-worn/mobile evidence capture; interview note templating; automatic chase cycles for outstanding information; timeline reconstruction views.

What good looks like: a handler can open a claim they have never seen and understand the position in under two minutes, because the file has structure rather than being a chronological pile of notes.

Stage 6 — Coverage verification

The formal determination of whether, and to what extent, the policy responds.

This is a legal analysis, not an administrative step: policy wording, endorsements, warranties, conditions precedent, exclusions, deductibles/excesses, limits and sub-limits, aggregation, other insurance, and the date-of-loss version of the wording.

Software capabilities: version-controlled policy wording accessible from the claim; endorsement history; automatic excess/deductible application; limit and sub-limit tracking including erosion across linked claims; aggregation logic for event-linked claims; structured coverage position recording; reservation-of-rights and declinature letter generation with mandatory reasoning fields.

Best practice: record coverage positions as structured data (responds / partially responds / does not respond / under investigation, plus reason codes), not as a paragraph in a file note. Everything downstream — declinature analysis, complaint root cause, product feedback to underwriting — depends on it.

Stage 7 — Reserve setting

The estimate of what the claim will ultimately cost. This is where claims meets the balance sheet.

Reserves drive financial reporting, reinsurance recoveries, capital requirements and pricing feedback. Under-reserving creates unpleasant surprises and understates the true cost of business written; over-reserving locks up capital and distorts pricing signals.

Software capabilities: multi-head reserving by category; full reserve movement history with reason codes and author; authority limits enforced by role and value; automatic initial reserves for defined claim types; alerting on stale reserves (unchanged for X days on an open claim); reserve adequacy reporting; large-loss notification triggers to reinsurers; currency handling.

Common mistakes: placeholder reserves of £1 or £1,000 that persist for months; reserve movements without reasons, which destroys the ability to analyse reserve accuracy later; and authority structures that are documented but not enforced by the system.

Stage 8 — Communication

Not a stage so much as a continuous obligation running the length of the claim.

The single most common complaint theme in claims across markets is not the decision — it is the silence around it.

Software capabilities: inbound and outbound email threaded to the claim automatically; SMS and push notifications; claimant self-service portal showing status, outstanding items and documents; template library with mandatory data merge; scheduled proactive updates (“no news” updates); communication preference and accessibility handling; vulnerability flags that change tone, channel and pace; full communication history in the audit trail; multi-language support.

Best practice: define a contact promise per claim track (e.g. fast-track: update every 5 working days; complex: every 10) and have the system generate the update automatically if a handler has not made contact. Proactive “nothing has changed but here is where we are” messages measurably reduce inbound chase volume — which in turn frees handler capacity.

Stage 9 — Document collection

Claims run on evidence, and evidence arrives as documents: invoices, receipts, medical reports, police reports, engineer reports, photographs, statements, schedules, quotes.

Software capabilities: secure upload links (no account required for third parties); email attachment auto-filing; scanning/post digitisation; OCR and data extraction; automatic document classification; required-document checklists per claim type with automated chasing; version control; redaction tools; retention policy enforcement; digital signature capture.

What good looks like: the claimant knows exactly what is outstanding at all times, can supply it from a phone in under a minute, and never sends the same document twice.

Stage 10 — Expert assessment

Engineers, surveyors, loss adjusters, medical experts, forensic accountants, building consultants, vets, agronomists — the specialists who convert “something happened” into a costed, defensible position.

Software capabilities: panel management with capability, coverage and capacity data; automated instruction with claim context pre-populated; supplier portals for two-way document exchange; fee agreement and cost control per instruction; SLA tracking on report turnaround; report templates that return structured data, not just a PDF; supplier performance scoring; mobile apps for field capture (photos, measurements, voice notes, offline working, geotagging).

Best practice — the structured report: a surveyor’s PDF is a dead end for analytics. Where the supplier submits findings as structured fields (cause code, damage scope, estimated cost by element, recommended action), you gain the ability to benchmark suppliers, detect anomalies and feed pricing. This is one of the most under-exploited opportunities in claims data.

Stage 11 — Fraud detection

Fraud detection is not a stage that happens once; it is a screen applied continuously, most valuably at FNOL and again at any point where the claim’s shape changes.

Layers of defence:

• Rules and red flags — deterministic indicators (recent policy inception, prior claims pattern, bank account reuse, round-number losses, notification delay)

• Network/link analysis — connections between claimants, addresses, phone numbers, vehicles, bank accounts, suppliers and prior claims

• Predictive models — statistical scoring on historical outcomes

• Document and image forensics — metadata analysis, duplicate image detection, manipulation detection, AI-generated image detection

• Industry data sharing — national fraud registers and databases (e.g. IFB/CUE/MIAFTR in the UK, comparable bodies elsewhere)

• Human investigation — counter-fraud specialists, interviews, surveillance where proportionate

Software capabilities: configurable red-flag rules; scoring at multiple lifecycle points; automatic referral routing to counter-fraud teams; investigation case management separate from the main claim; industry database integration; audit trail of fraud decisions.

Critical caution: fraud models carry real regulatory and ethical risk. A score should trigger investigation, never automatic repudiation. Models must be tested for bias across protected characteristics and proxies for them (postcode is a well-known proxy). Document your model governance before a regulator asks. See Chapter 6 and Chapter 13.

Stage 12 — Negotiation

Reaching agreement on quantum and, where disputed, liability. Applies to claimant negotiation, third-party and TP insurer negotiation, and supplier cost negotiation.

Software capabilities: offer and counter-offer history with dates, amounts and rationale; settlement authority enforcement by value band and role; comparable settlement data for benchmarking (the “what have similar claims settled at?” question); Part 36 / formal offer tracking where applicable; litigation status and hearing date management; negotiation position notes visible to supervisors.

Common mistake: negotiation history recorded in email only. When a handler leaves mid-negotiation, the successor inherits a number with no reasoning — and typically pays more.

Stage 13 — Settlement

The agreed outcome: how much, to whom, in what form.

Settlement is not always cash. Increasingly it isn’t: repair, replacement, cash-in-lieu, reinstatement, rehabilitation, periodical payment orders, or in-kind service delivery. Supply-chain settlement generally reduces indemnity cost and improves the customer experience simultaneously — one of the few genuine win-wins in claims — provided supplier quality is actively managed.

Software capabilities: settlement type recording; breakdown by head of damage; VAT/tax treatment; deductible/excess netting; multi-party apportionment; contribution and betterment; settlement agreement generation; e-signature; discharge form management; structured settlement scheduling.

Stage 14 — Payment

Moving the money, correctly, once, to the right person.

Software capabilities: payment approval workflow with segregation of duties; bank detail verification (including confirmation-of-payee-style checks); duplicate payment detection; sanctions re-screening at payment; payment file generation or direct banking/payment gateway API integration; multiple payment methods (bank transfer, card, digital wallet, cheque, prepaid card, instant payment rails); multi-currency; payment status tracking back into the claim; recovery of overpayments; full financial audit trail.

Fraud control note: payment diversion fraud — where a criminal intercepts communication and substitutes bank details — is a persistent and growing threat. Bank detail changes should always require independent verification through a channel not used to make the request, enforced by the system rather than by policy alone.

Stage 15 — Recovery

Reclaiming money the business should not ultimately bear: salvage, excess collection, uninsured loss recovery, betterment contributions, overpayment recovery, and reinsurance recoveries.

Software capabilities: recovery reserve tracking; salvage disposal management and proceeds recording; excess collection workflow; reinsurance treaty and facultative allocation with automatic cession identification; recovery ageing and chasing; write-off approval workflow.

Under-exploited lever: recovery discipline is one of the quietest sources of value in claims. Recoveries that are not tracked systematically are frequently not pursued at all, and the loss never appears in any report because nobody knows it was recoverable.

Stage 16 — Subrogation

Pursuing a third party legally responsible for the loss, standing in the shoes of your insured. Related to recovery but legally and operationally distinct.

Software capabilities: subrogation potential flagging at FNOL and triage (early identification is decisive); liability evidence pack assembly; third-party insurer identification and correspondence; limitation-period diary management with hard alerts; inter-insurer agreement handling; recovery agent instruction and performance tracking; partial-recovery apportionment.

The single biggest failure mode: late identification. Subrogation potential spotted at month six has usually already lost the evidence — scene photographs, witness recollection, the damaged component — that would have made it winnable. Systems should prompt the subrogation question at FNOL, every time.

Stage 17 — Closure

Confirming there is nothing further to do, and locking the file.

Software capabilities: closure checklists (all payments made, all recoveries pursued or written off, reserves zeroed, documents filed, complaint status resolved); reason-for-closure coding; automatic reserve release on closure; reopening workflow with reason capture and separate reporting; retention clock start; post-closure survey trigger.

Watch: reopen rate is one of the most diagnostic and least monitored metrics in claims. High reopen rates usually signal premature closure driven by cycle-time targets — a classic case of a metric distorting the behaviour it was meant to improve.

Stage 18 — Reporting and learning

The stage most organisations under-invest in, and the one that compounds.

Operational reporting: caseloads, ageing, SLA compliance, touch counts, backlog. Financial reporting: paid, outstanding, incurred, movements, IBNR feed, reserve adequacy. Regulatory reporting: market conduct returns, complaint data, outcome monitoring. Client reporting: for TPAs and MGAs — bordereaux, SLA packs, cost analysis. Strategic learning: which perils are trending, which suppliers perform, which policy wordings generate disputes, which products leak, which handling decisions correlate with cost.

The feedback loop that most organisations never close: claims data flowing back into underwriting and pricing. Claims knows, in granular detail, which risks turn bad and why. Where that information stays in claims, the business keeps writing the same losses.

Chapter 3
How modern claims platforms work

This chapter is written for two audiences at once: claims leaders who need to understand enough architecture to ask good questions, and technical readers who need the claims-specific detail. Concepts are explained in plain English first.

3.1 Cloud architecture

In plain English: the software runs in a data centre operated by the vendor (usually on top of AWS, Azure or Google Cloud) and you reach it through a browser. You do not buy servers, patch them, or plan upgrades.

Three delivery models still exist in the market:

Questions worth asking: Which cloud region hosts our data? Can we require a specific region for data residency? What is the update cadence, and can we test releases in a sandbox before they hit production? What is the contractual uptime, and what is excluded from it (planned maintenance usually is)?

3.2 Multi-tenancy and data isolation

Multi-tenancy is efficient but requires trust. For TPAs, the equivalent question applies internally: can you prove that Client A’s users cannot see Client B’s claims? This is not a checkbox — ask for the mechanism (separate schemas, row-level security, tenant-scoped tokens), evidence of penetration testing against tenant isolation, and how it holds for reporting and data exports, which is where isolation most often leaks.

3.3 The data model

The data model is the single most consequential design decision in a claims platform, and the one buyers examine least.

A workable claims data model separates:

POLICY ─────────┐
                ├──► CLAIM ──┬──► CLAIMANT(S) / PARTIES
COVER SECTION ──┘            │
                             ├──► FEATURE / HEAD OF DAMAGE
                             │       └──► RESERVE(S) ──► TRANSACTION(S)
                             │
                             ├──► TASK(S) / DIARY
                             ├──► DOCUMENT(S)
                             ├──► COMMUNICATION(S)
                             ├──► SUPPLIER INSTRUCTION(S)
                             ├──► DECISION(S) / COVERAGE POSITION
                             └──► RECOVERY / SUBROGATION ACTION(S)

The key concept many buyers meet for the first time during implementation is the feature (sometimes “claim line”, “head of damage”, “sub-claim”, “exposure”): a distinct exposure within a single claim. One motor accident might generate features for own-vehicle damage, third-party vehicle damage, third-party injury, credit hire and legal costs — each with its own reserve, its own handler, potentially its own settlement and its own reinsurance treatment.

Why it matters: systems that model a claim as a single flat record with one reserve cannot represent this properly. Organisations then compensate by creating multiple linked claims, which corrupts frequency counts, cycle-time metrics and reinsurance reporting for years afterwards. Ask every vendor to demonstrate a multi-feature claim.

Other model questions worth pressing on:

• Can custom fields be added without vendor development, and do they flow into reporting and the API automatically?

• How are parties modelled — as claim-specific text, or as reusable entities with their own history? (The latter enables link analysis, repeat-claimant detection and single-customer view.)

• How is time modelled? Can you reconstruct what the record looked like on a given past date — the “as at” question that actuaries, auditors and litigators all eventually ask?

3.4 Workflow engines

In plain English: the workflow engine is what decides “what happens next” and makes sure it actually happens.

A claims workflow engine typically provides:

• Process definitions — the map of stages, tasks, decisions and paths for a claim type

• Task generation — creating work items with owner, due date, priority and instructions

• Queues — shared pools of work with allocation rules

• Diaries — scheduled future actions on a claim

• SLAs and escalation — timers, warnings and automatic escalation on breach

• Parallel paths — investigation and reserving proceeding simultaneously

• Sub-processes — reusable fragments (e.g. “obtain medical evidence”) invoked from many processes

• Human tasks vs system tasks — the same engine handles both

Configuration vs code: the crucial commercial question. In a well-designed modern platform, a trained business analyst inside your organisation can change a workflow — add a step, alter an SLA, insert an approval — in hours, in a sandbox, without a vendor change request. In older or heavily-customised systems, the same change is a project. Over ten years the difference between those two worlds is enormous, and it rarely appears in a feature comparison matrix.

Test this in the demo. Ask the vendor to make a live change: “add a mandatory supervisor approval when the reserve exceeds £25,000 on property claims.” Watch who does it, in what tool, and how long it takes.

3.5 Rules engines and decision management

In plain English: rules are the “if this, then that” logic that removes judgement from decisions that shouldn’t require it.

Claims rules typically fall into categories:

Design principles that separate maintainable rule sets from unmaintainable ones:

• Rules should live in a business-readable repository, versioned, with an owner and an effective date range.

• Every automated decision must be explainable — the system should record which rule fired and on what data.

• Rules should be testable against historical claims before deployment (“if this rule had been live last year, what would have changed?”).

• Retire rules deliberately. Rule sets accrete; a portfolio of 4,000 rules that nobody understands is a genuine operational risk.

3.6 Event-driven automation

In plain English: instead of the system checking periodically whether something has happened, the system reacts the instant it does.

Modern platforms emit events — claim.created, reserve.changed, document.received, payment.approved, sla.breached — that other components and external systems subscribe to. This is what makes real-time behaviour possible: a document arriving triggers OCR, which triggers classification, which satisfies a checklist item, which clears a blocked task, which notifies a handler, which sends the claimant an update — in seconds, with no polling and no batch window.

For buyers, the practical questions are: does the platform publish events externally via webhooks? Can we subscribe to them from our own systems? Is there a replay mechanism if our endpoint is down?

3.7 Permissions and access control

Claims data is sensitive: health data, financial data, criminal allegations, vulnerable individuals, sometimes employees of the organisation itself.

A credible permission model includes:

• Role-based access control (RBAC) — permissions attached to roles, not individuals

• Attribute-based rules — access depending on claim value, class, client, or geography

• Record-level restriction — sensitive claims (employee claims, VIPs, high-profile matters) visible only to a named list

• Field-level restriction — e.g. medical details hidden from finance users

• Client segregation — essential for TPAs

• Delegated authority limits — financial authority enforced in the system, not just in a policy document

• Segregation of duties — the person who approves a payment cannot be the person who created it

• Break-glass access — emergency elevated access that is logged, time-limited and reviewed

3.8 Audit trails

An audit trail is not a change log; it is a legal artefact.

Requirements for a defensible audit trail:

• Immutable — appended, never edited or deleted, including by administrators
• Complete — captures field-level before/after values, not just “record updated”
• Attributed — user, timestamp (with timezone), source (UI, API, automation, which rule)
• Inclusive of reads where sensitive data is involved (who viewed this medical report?)
• Queryable and exportable for auditors and regulators
• Retained per your retention policy, independently of the record’s own lifecycle

The AI addition: where AI contributes to a claim — a summary, a recommendation, an extraction — the audit trail must record that it was AI-generated, which model and version, what inputs it saw, and which human reviewed or overrode it. This will be an examination point for regulators, and it is a reasonable thing to require contractually today.

3.9 Portals

Portal design principles worth insisting on: no account creation required for one-off third-party actions (use secure tokenised links); mobile-first, because the majority of claimant interaction is on a phone; accessible to WCAG 2.2 AA at minimum, which is a legal requirement for many public sector bodies and good practice everywhere; and always show what is outstanding and what happens next.

3.10 Mobile

Two distinct mobile needs, often conflated:

Claimant mobile — usually best served by responsive web plus secure links, not a downloadable app. Nobody installs an app to make one claim every seven years.

Field/handler mobile — genuinely benefits from a native app: offline working (basements, rural sites, industrial premises), camera and video with automatic geotagging and timestamping, voice notes with transcription, measurement tools, digital signature capture, and structured form completion that syncs when connectivity returns.

3.11 Security foundations

Covered fully in Chapter 13; architecturally, expect as baseline: encryption in transit (TLS 1.2+) and at rest (AES-256), SSO via SAML 2.0 or OIDC, enforced MFA, key management with documented rotation, network segregation, regular independent penetration testing with summaries available to customers, secure SDLC with code scanning and dependency management, and a documented, tested incident response process.

3.12 Integration architecture

The realistic view: a claims platform is a hub, not an island. A typical deployment integrates with between eight and thirty external systems. Integration effort routinely exceeds configuration effort in implementation plans, and is the most common source of overrun.

Patterns you will encounter:

Chapter 4
Essential capabilities

This chapter is a reference inventory. Use it to build a requirements list, but resist the temptation to demand everything — every capability you specify adds configuration, testing, training and maintenance cost. The discipline is in deciding what you genuinely need.

4.1 Capability map

INTAKE            HANDLING           FINANCIAL         EVIDENCE
─────────────     ─────────────      ─────────────     ─────────────
FNOL forms        Case management    Reserves          Documents
Multi-channel     Workflow           Payments          OCR / extraction
Triage            Tasks & diary      Recoveries        Photo / video
Validation        SLA tracking       Authority         Digital signature
Duplicate check   Notes & timeline   Multi-currency    Evidence register

COMMUNICATION     SUPPLY CHAIN       INSIGHT           CONTROL
─────────────     ─────────────      ─────────────     ─────────────
Email integration Panel management   Dashboards        Audit trail
SMS / push        Instructions       Operational MI    Permissions
Portals           Supplier portal    Analytics         Compliance rules
Templates         SLA & performance  Ad-hoc reporting  Retention
Preferences       Invoice control    Data export/API   Vulnerability

4.2 The capabilities, explained

FNOL and intake. Configurable dynamic forms per product and peril; multi-channel ingestion into a single structure; guided scripts; policy pre-fill; instant triage; evidence capture at first contact; out-of-hours handling.

Case management. The claim file: parties, timeline, notes, documents, financials, tasks, communications, decisions — presented so that context is graspable in under two minutes. Watch for: the number of clicks to complete a routine action. Handler efficiency is destroyed by navigation, not by feature gaps.

Workflow automation. Configurable process definitions, conditional branching, parallel paths, sub-processes, automatic task creation, escalation. See Chapter 5.

Task management. Assignment by role, skill or individual; priority and due dates; bulk actions; personal and team views; reassignment on absence; workload visibility for supervisors.

SLA tracking. Multiple concurrent SLAs (regulatory, client-contractual, internal), business-hours-aware calculation, holiday calendars per jurisdiction, pause conditions (e.g. awaiting claimant information), warning thresholds and breach escalation, and SLA performance reporting by client, team and claim type. For TPAs this is contractual revenue protection, not an operational nicety.

Diary management. Future-dated actions with reminders — the core discipline of long-tail claims handling. Look for: bulk diary review screens, diary handover on reassignment, and automatic diary creation from workflow.

Reserve management. Multi-head reserves, movement history with reason codes, authority enforcement, automatic initial reserves, stale-reserve alerting, adequacy reporting, large-loss notification.

Payments. Approval workflow with segregation of duties, bank verification, duplicate detection, sanctions re-screening, multiple methods and currencies, gateway/banking integration, status feedback, overpayment recovery.

Supplier and panel management. Supplier records with capabilities, coverage areas, capacity, rates and compliance status (insurance certificates, accreditations, DBS/background checks where relevant); automated instruction; SLA and quality scoring; invoice validation against agreed rates; automatic re-tendering or rotation rules.

OCR and AI extraction. Converting documents into data: invoice line items, medical report findings, police report details, engineer estimates. See Chapter 5.

Document management. Version control, classification, required-document checklists, redaction, retention enforcement, bulk export for litigation or subject access requests.

Email integration. Two-way sync with Microsoft 365 or Google Workspace; inbound auto-filing against the correct claim; outbound sent from the platform but appearing in the handler’s sent items; attachment handling; shared mailbox management with allocation rules. This is one of the highest-value integrations and one of the most commonly under-delivered — test it hard.

SMS and messaging. Status updates, document requests, appointment confirmations, satisfaction surveys, two-way replies threaded to the claim.

Dashboards and analytics. Role-appropriate views: handler (my work), supervisor (team performance and exceptions), manager (portfolio), executive (financial and strategic), client (contractual). Plus ad-hoc query capability for the analyst who will inevitably need something nobody anticipated.

Audit logs. As Chapter 3.8.

Compliance features. Vulnerability identification and handling, complaint capture and root-cause coding, regulatory timeline enforcement, outcome monitoring data capture, consent and preference management, retention and deletion, subject access request tooling.

Search. Underrated. Full-text search across notes, documents (post-OCR) and communications; faceted filtering; saved searches; permission-aware results. A handler who cannot find the engineer’s report from four months ago will ask the engineer for it again.

Permissions. As Chapter 3.7.

APIs and extensibility. Documented REST APIs covering all core objects, webhooks, sandbox environment, versioning policy with deprecation notice periods, rate limits stated clearly, and ideally a scripting or low-code extension layer for logic the standard configuration doesn’t cover.

Mobile inspection and photo capture. Offline-capable field apps, geotagged and timestamped media, structured inspection forms, voice-to-text notes, and immediate sync to the claim file.

Digital signatures. For discharge forms, settlement agreements, authority forms and statements — with legal validity appropriate to jurisdiction (eIDAS in the EU/UK, ESIGN/UETA in the US) and full audit evidence of the signing process.

Reporting. Scheduled distribution, self-service report building, export in usable formats, and — importantly — a documented data dictionary so that people building reports agree on what “closed” means.

4.3 Prioritisation: the MoSCoW starting point

Most organisations over-specify. This is a defensible starting position for a mid-sized general claims operation; adjust for your context.

Chapter 5
Claims automation

Automation in claims is not one technology. It is a stack, and the layers behave very differently in terms of accuracy, governance and value.

 ┌───────────────────────────────────────────────────────────────┐
 │ 5. AGENTIC / ORCHESTRATED AI     multi-step, tool-using       │  emerging
 ├───────────────────────────────────────────────────────────────┤
 │ 4. GENERATIVE AI                 summarise, draft, explain    │  probabilistic
 ├───────────────────────────────────────────────────────────────┤
 │ 3. PREDICTIVE MODELS             score, forecast, rank        │  probabilistic
 ├───────────────────────────────────────────────────────────────┤
 │ 2. DOCUMENT INTELLIGENCE         OCR, extraction, classify    │  mixed
 ├───────────────────────────────────────────────────────────────┤
 │ 1. RULES & WORKFLOW              deterministic if/then        │  deterministic
 └───────────────────────────────────────────────────────────────┘
      ▲ Start here. Most unrealised value still sits at layers 1–2.

The most important strategic point in this chapter: organisations rush to layer 4 while layer 1 remains half-built. Deterministic automation is cheaper, more reliable, easier to govern and easier to explain to a regulator. Exhaust it first.

5.1 Rules engines in practice

Covered architecturally in 3.5. Operationally, the questions are: who owns the rules, how are they changed, and how do you know they still work?

A workable governance model:

5.2 Workflow automation

The distinction from rules: rules decide, workflow sequences. In practice they interleave — a rule determines the track, the workflow executes it.

Highest-return workflow automations, roughly in order of value-to-effort:

• Automatic acknowledgement and expectation-setting at FNOL

• Automatic chasing of outstanding documents on a defined cadence

• Automatic proactive status updates when no contact has occurred within the promised window

• Automatic supplier instruction on triage decision

• Automatic task creation on document receipt

• Automatic escalation on SLA warning (not breach — warning)

• Automatic reserve review prompts at defined intervals and value thresholds

• Automatic closure checklist enforcement

• Automatic reopen alerting to supervisors

• Automatic survey dispatch post-closure

None of these require AI. All of them are typically absent or partial in operations that describe themselves as “quite manual”.

5.3 Straight-through processing (STP)

Definition: a claim that progresses from notification to settlement and payment with no human intervention.

STP is the headline metric of claims automation and the most frequently misrepresented. A vendor’s “80% STP” may mean 80% of claims within one narrowly-defined product where cover is trivially verifiable. Always ask: STP of what, as a percentage of what denominator?

Realistic candidates for STP:

The design pattern that works: define narrow, high-confidence STP eligibility criteria and expand them incrementally based on measured outcomes. Every STP rule set needs a sampling regime — a percentage of automatically-settled claims reviewed by humans — to detect drift, over-payment and abuse.

The failure mode to avoid: STP as a leakage machine. If the automated path pays more than a handler would have, you have optimised speed at the cost of accuracy. Measure average settlement value on the STP path against a matched manual cohort.

5.4 OCR and document intelligence

Plain English: turning a picture of a document into text, then turning that text into data fields.

Modern document intelligence is a pipeline:

Document arrives → Classify (what is it?) → Extract (what does it say?)
   → Validate (does it make sense?) → Post (write to claim) → Route (what next?)
                                          │
                                          └──► Confidence below threshold → human review queue

Realistic accuracy expectations: typed, structured documents (invoices, standard forms) extract very well. Handwritten forms, poor-quality photographs of documents, and unusual layouts extract far less reliably. Any implementation that does not include a confidence threshold and a human review queue will produce silent errors — which are worse than visible failures because nobody is looking for them.

Highest-value extraction targets in claims: invoice line items and totals, repair estimates by element, medical report key findings and prognosis, police report incident details and reference numbers, bank details (with mandatory verification), and vehicle/property identifiers.

5.5 NLP and text understanding

Beyond extraction, natural language processing supports:

• Classification of inbound email intent (chase, new information, complaint, escalation) for routing

• Sentiment and distress detection to flag potential vulnerability or complaint risk early

• Entity recognition across notes to build the party and link picture

• Similarity search — “find claims like this one” for benchmarking settlements

• Complaint root-cause coding at scale, which is otherwise a manual and inconsistent task

5.6 Predictive analytics

Statistical models that estimate an outcome from claim characteristics. The main claims applications:

Governance requirements that apply to all of them: documented purpose, training data provenance, performance monitoring for drift, fairness testing across protected characteristics and proxies, human decision authority for adverse outcomes, and explainability sufficient to answer a complaint. Under the EU AI Act, certain insurance uses — notably pricing and risk assessment in life and health — fall into the high-risk category with corresponding obligations; claims applications should be assessed case by case with legal input.

5.7 Fraud scoring

The distinctive design constraint: precision matters more than recall at the referral point. A model that flags 30% of claims is worse than useless, because investigation capacity is finite and false positives create genuine harm — delayed payments to honest customers, complaints, and regulatory exposure.

Practical approach: tune to the capacity of your investigation team, measure the conversion rate of referrals to established fraud, and review declined-and-then-overturned cases as a governance metric.

5.8 Recommendation engines

Rather than deciding, these suggest: recommended reserve, recommended supplier, recommended next action, recommended settlement range based on comparable claims. This “human-in-the-loop” pattern is often the highest-value and lowest-risk deployment of predictive capability, because it improves consistency without removing accountability.

Design tip: capture the override. When a handler rejects a recommendation, capturing why is the most valuable training data you will ever generate — and the best early warning that the model is drifting.

5.9 Claims routing

Routing sophistication ladder:

• Round robin — even distribution, ignores skill and complexity

• Rules-based — by class, value, geography

• Skills-based — matching claim requirements to handler competencies

• Capacity-aware — accounting for current workload and complexity mix, not just claim count

• Outcome-optimised — assigning based on which handler profile historically achieves better outcomes on this claim type

Most operations sit at 2. Moving to 4 is usually achievable with existing technology and delivers meaningful gains, because caseload counts are a poor proxy for workload: twenty simple claims and twenty complex ones are not the same job.

5.10 Document classification and auto-filing

Unglamorous, and among the highest-ROI automations available. Inbound post and email arriving as unclassified attachments consumes enormous administrative time across the industry. Automatic classification and filing against the correct claim — with a review queue for low-confidence cases — typically pays back quickly and improves handler experience immediately.

Chapter 6
AI in claims

This chapter separates what is working now from what is plausible soon and what is being oversold. It is written to be usable in a board paper.

6.1 What changed

Earlier machine learning in claims required structured data and predicted a number. Large language models operate on unstructured language and produce language. Because claims work is overwhelmingly language work — reading, summarising, corresponding, deciding, documenting — the addressable surface expanded dramatically.

Alongside that, computer vision has become genuinely capable of damage assessment in constrained domains, and speech models have made call transcription and analysis economically viable at full volume rather than sample.

6.2 The maturity picture

6.3 Where AI is delivering today

Summarisation. A 400-page claim file reduced to a structured brief: parties, chronology, coverage position, financials, outstanding actions, key risks. The value is concentrated at handovers — reassignment, escalation, supervisor review, litigation instruction — where a handler currently spends 30–60 minutes reconstructing context.

Design requirement: every statement in a summary should link back to its source document and page. Unverifiable summaries are a governance problem, not a productivity gain.

Drafting. Correspondence, file notes from call transcripts, instruction letters, settlement rationale. The pattern that works: AI drafts, handler edits and takes accountability, system records both the draft and the final version. Quality improves markedly when the drafting is constrained by your own approved templates and tone-of-voice rules rather than generated freely.

Copilots. An assistant embedded in the claim that answers questions grounded in this claim’s documents and your policy wordings, procedures and precedents. Retrieval-augmented generation (RAG) — where the model is given the relevant source text rather than relying on training memory — is what makes this safe enough to deploy. Ask vendors directly whether their assistant is grounded in your content or generating from general knowledge.

Call and communication analysis. Historically, quality assurance sampled perhaps 1–2% of calls. Transcription plus analysis makes 100% coverage feasible: compliance adherence, vulnerability indicators, complaint risk, tone, and — genuinely useful — identifying which handler behaviours correlate with better outcomes.

Image analysis. Vehicle damage severity and repair-vs-total-loss triage; glass and device damage; property damage scoping in constrained scenarios; duplicate and manipulated image detection, including detection of AI-generated images, which is now a live fraud vector.

Policy lookup. Handlers frequently interpret wordings from memory. A grounded assistant that returns the applicable clause, from the correct version at the date of loss, with the text shown, improves both speed and consistency.

6.4 Limitations you must design around

Hallucination. Models can produce fluent, confident, incorrect statements. Mitigation: retrieval grounding, source citation, confidence signalling, and human review of anything customer-facing or decision-bearing.

Non-determinism. The same input can yield different outputs. This complicates testing and regulatory explanation. Mitigation: constrain outputs (structured formats, templates), log inputs and outputs, and treat AI output as a draft artefact rather than a decision.

Bias. Models reflect their training data. In claims, the risk concentrates in fraud scoring, settlement recommendation and triage. Mitigation: fairness testing across protected characteristics and proxies, outcome monitoring by demographic segment where lawful, and human authority over adverse decisions.

Data protection. Claims data includes special category data under GDPR (health, in some cases criminal allegations). Before any deployment: confirm whether data leaves your tenancy, whether it is used for vendor model training (it should not be, and this should be contractual), where processing occurs geographically, and complete a DPIA.

Explainability. “The model said so” fails as a response to an ombudsman. Any AI-influenced adverse decision needs a human-comprehensible rationale recorded independently of the model output.

Over-trust. The most underrated risk. When AI output is usually right, humans stop checking. Mitigation: mandatory review checkpoints, randomised audit of accepted outputs, and metrics on override rate — a sudden fall in overrides may indicate rubber-stamping rather than improved quality.

6.5 Human oversight: a workable framework

6.6 An AI adoption sequence that works

• Internal, non-customer-facing first — summarisation, search, internal drafting. Low risk, immediate value, builds organisational literacy.

• Assisted customer-facing second — drafts reviewed by humans before send.

• Grounded knowledge assistants third — once your documentation is organised enough to retrieve from.

• Automated decisions last, narrowly scoped, heavily sampled, with clear reversal paths.

Alongside this, establish an AI governance group before deployment, not after: representation from claims, compliance, data protection, IT security and legal; a register of AI use cases with risk classification; defined review cadence; and documented human accountability for each use.

6.7 Questions to ask any vendor about AI

• Which specific models do you use, hosted where, and can we require regional processing?

• Is our data used to train your models or any third party’s? (Require “no” contractually.)

• Is your assistant grounded in our documents via retrieval, or generating from general training?

• How do you measure accuracy, and what accuracy figures can you evidence for our claim types?

• What happens when confidence is low — is there a defined human path?

• How is AI involvement recorded in the audit trail?

• What is your model change policy — will behaviour change under us without notice?

• Can we turn specific AI features off entirely, by team or claim type?

• How do you test for bias, and will you share the methodology?

• Who is contractually liable if an AI-generated communication causes customer harm?

Chapter 7
Business benefits

Each benefit below follows the same structure: the problem, the technology response, the KPI that proves it, the business outcome, and a worked example. The examples are illustrative composites based on common operational patterns, not audited case studies — treat the arithmetic as a modelling template for your own numbers, not as a claim about typical results.

7.1 Reduced claims leakage

Problem. Leakage is the difference between what a claim should have cost and what it did cost, caused by process failure rather than legitimate indemnity. Sources: paying outside cover, failing to apply excesses or limits correctly, missed recoveries and subrogation, over-settlement without benchmarking, unnecessary supplier use, paying invoices above agreed rates, and duplicate payment.

Solution. Automated cover and limit checks; mandatory excess application; settlement benchmarking against comparable claims; invoice validation against contracted rates; recovery flagging at FNOL; duplicate payment detection; authority enforcement.

KPI. Leakage rate (%) measured through structured file audit; average settlement variance vs benchmark; recovery rate.

Outcome. Direct margin improvement. Because leakage acts on indemnity — the largest number in the operation — a small percentage improvement typically outweighs large efficiency gains.

Worked example. An operation handling 20,000 claims a year at £3,200 average incurred spends £64m. If a structured audit identifies leakage at 4% and systematic controls recover a quarter of it, that is £640k annually — usually more than the entire cost of the platform.

7.2 Faster cycle times

Problem. Claims sit waiting — for documents, for allocation, for approval, for a supplier, for someone to notice. Wait time, not work time, dominates most claim durations.

Solution. Automatic routing on receipt; automated chasing; event-driven task creation; SLA warnings before breach; parallel rather than sequential workflow; supplier instruction at triage.

KPI. Average cycle time (notification to closure), by claim type; touch time vs elapsed time ratio; ageing profile.

Outcome. Lower handling cost per claim, higher satisfaction, less escalation and litigation, and faster reserve release.

Worked example. Reducing property claim average duration from 45 to 32 days across 6,000 claims removes roughly 78,000 claim-days of open exposure. The direct saving is in handler touches (each avoided chase cycle is typically 10–15 minutes of handler and administrative time); the indirect benefit is fewer complaints, which are themselves expensive to handle.

7.3 Lower cost per claim

Problem. Administrative overhead — rekeying, filing, chasing, searching, reporting — consumes handler capacity that should go to judgement work.

Solution. Auto-filing of email and post; document classification; pre-populated correspondence; integrated telephony and email; single-screen claim context; automated reporting.

KPI. Cost per claim (fully loaded); claims per handler FTE; administrative time as a percentage of handler time.

Outcome. Capacity released without headcount reduction — typically absorbed by volume growth or redeployed to complex claims.

Worked example. If administrative tasks consume 30% of a 35-hour handler week and automation removes a third of that, each handler recovers roughly 3.5 hours weekly. Across 40 handlers that is 140 hours — approximately four FTE of capacity, without redundancy.

7.4 Improved customer experience

Problem. Uncertainty and chasing, not outcomes, drive dissatisfaction.

Solution. Proactive updates by cadence; self-service portal; multi-channel access; document upload from phone; instant acknowledgement; realistic expectation setting.

KPI. NPS/CSAT at closure; complaint volume per 1,000 claims; inbound chase contact rate.

Outcome. Retention, renewal, referral; and for TPAs and MGAs, contract retention with clients whose own customers are affected.

Worked example. Inbound chase calls are pure cost with no indemnity benefit. An operation receiving 0.8 chase calls per claim across 20,000 claims fields 16,000 calls; halving that through proactive updates releases meaningful contact-centre capacity and simultaneously improves the metric that caused the calls.

7.5 Better fraud detection

Problem. Manual detection depends on individual handler experience and is inconsistent, especially with organised fraud that presents normally on any single claim.

Solution. Systematic screening at multiple lifecycle points; link and network analysis; industry database integration; image forensics; structured referral workflow.

KPI. Fraud detection rate; savings from repudiated and withdrawn claims; referral-to-confirmation conversion rate; false positive rate.

Outcome. Reduced indemnity spend and improved deterrence — but only if false positives are controlled, since delayed honest claims carry their own cost.

7.6 Stronger regulatory compliance

Problem. Demonstrating fair outcomes and adherence to conduct rules requires evidence that legacy systems cannot produce.

Solution. Structured decision recording with reason codes; complete audit trails; vulnerability identification and handling; complaint root-cause coding; automated regulatory timeline enforcement; outcome monitoring reporting.

KPI. Complaint upheld rate; regulatory deadline compliance; audit findings; outcome monitoring coverage.

Outcome. Reduced regulatory risk, cheaper audits, faster and cheaper responses to information requests, and defensibility in dispute.

7.7 Reserve accuracy

Problem. Inconsistent reserving distorts financial reporting, reinsurance recovery and pricing feedback.

Solution. Automatic initial reserves by claim profile; mandatory review triggers; movement reason codes; adequacy reporting; guidance from severity models.

KPI. Reserve accuracy (initial reserve vs ultimate paid); average number of reserve movements per claim; percentage of claims with a single large late movement.

Outcome. More reliable financials, better capital efficiency, fewer surprises, and pricing signals that reflect reality.

7.8 Operational visibility

Problem. Leaders manage on lagging monthly reports assembled manually in spreadsheets, and cannot see problems until they are historical.

Solution. Live dashboards by role; exception-based supervision; ageing and backlog visibility; SLA warnings; supplier scorecards.

KPI. Report production time; percentage of decisions made on live vs monthly data; time from issue occurrence to detection.

Outcome. Earlier intervention, better resource allocation, and management attention directed at exceptions rather than everything.

7.9 Scalability and surge resilience

Problem. Catastrophe events, portfolio wins and seasonal peaks overwhelm operations built for average volume.

Solution. Cloud elasticity; automation absorbing routine volume; rapid onboarding of temporary handlers through configuration and guided workflow; event tagging and cat-specific processes; surge routing to overflow suppliers.

KPI. Peak-to-average handling capacity ratio; time to onboard a new handler to productivity; SLA maintenance during surge.

Outcome. Ability to take on new business or absorb a cat event without service collapse — a direct commercial capability, not just an operational one.

7.10 Supplier performance improvement

Problem. Supply chains are managed on relationship and anecdote rather than data.

Solution. Structured instruction and reporting; SLA measurement; cost-per-job benchmarking; quality and rework tracking; automatic rotation and re-tender rules.

KPI. Average supplier cost by job type; rework rate; supplier SLA compliance; customer satisfaction by supplier.

Outcome. Lower indemnity spend and better customer outcomes simultaneously; and credible leverage in rate negotiation.

7.11 Benefits summary

Chapter 8
Implementation challenges

This chapter is deliberately unflattering. Claims system implementations fail more often than vendors advertise and less often than sceptics assume; the difference between the two outcomes is usually visible in the first eight weeks.

8.1 Legacy systems

The challenge. The incumbent is typically 10–30 years old, undocumented, customised beyond recognition, integrated into a dozen downstream processes, and maintained by two people who understand it — one of whom is retiring.

Realistic strategies:

“New claims only” deserves particular attention because it is the most frequently chosen and most frequently underestimated. Long-tail classes (liability, injury, disease, construction) can leave the legacy system live for a decade. Budget for it explicitly, including licensing, security patching and the reporting complexity of a portfolio split across two systems.

8.2 Data migration

The most consistently underestimated workstream in the programme.

What makes it hard: decades of inconsistent capture; free text where structure is needed; codes whose meaning changed over time; duplicated parties; documents stored outside the system; financial history that must reconcile to the penny; and the fact that nobody remembers why certain fields were used the way they were.

A workable sequence:

• Profile — analyse what data actually exists, its quality and its distribution. Do this before scoping, not after.

• Decide scope — full history, or open claims plus N years closed? Full document migration or on-demand retrieval from an archive?

• Map — field by field, with business sign-off on every transformation and every default value.

• Cleanse — at source where possible; migrating dirty data preserves the dirt.

• Trial migrate — repeatedly. Three to five full rehearsals is normal, not excessive.

• Reconcile — financials to the penny, claim counts exactly, with a signed reconciliation pack.

• Cut over — with a defined freeze window and a tested rollback plan.

Decisions that need making early: what happens to claims mid-workflow at cutover; how legacy claim references are preserved and searchable; whether documents are migrated or linked; and how you will run reporting across both systems during dual running.

8.3 Integration complexity

The challenge. Every integration involves two systems, two teams, two release schedules and at least one system that lacks a usable API.

Practical mitigations: inventory and prioritise integrations at business-case stage, not design stage; classify each as day-one/phase-two/never; identify API-less systems early and decide between RPA, file transfer or process change; insist on sandbox environments from all parties before development starts; and build monitoring and alerting for every interface, because silent integration failure is a persistent operational hazard.

8.4 User adoption

The challenge. Handlers are busy, sceptical of change (frequently with justification from previous programmes), and will revert to shadow processes — spreadsheets, personal email folders, paper notes — if the system makes their job harder.

What actually works:

• Involve handlers in design, genuinely and early. Not a survey; workshops where their input changes decisions.

• Identify and empower champions in each team — respected practitioners, not necessarily the most senior.

• Train on their claims and their scenarios, not generic demo data.

• Provide floor-walking support in the first fortnight, at density that feels excessive.

• Reduce caseload expectations temporarily and say so publicly. Productivity dips at go-live; pretending otherwise destroys trust.

• Fix the small irritations fast. Nothing builds adoption like a handler seeing their complaint resolved in a week.

• Monitor shadow processes actively and treat them as design feedback, not misconduct.

8.5 Governance

The challenge. Claims programmes touch claims, IT, finance, compliance, data protection, procurement and often multiple business units. Without clear decision rights, the programme stalls in consultation.

Minimum viable governance: a named executive sponsor with genuine authority; a single empowered product owner from claims who can make binding decisions daily; a steering group meeting at least fortnightly with pre-agreed decision authority; a documented escalation path with a maximum decision latency (48–72 hours); and a scope change process with a visible cost and schedule impact attached to every change.

The decision-latency point deserves emphasis. Most delayed claims programmes are not delayed by technical difficulty; they are delayed by hundreds of small decisions taking three weeks each.

8.6 Data quality

Legacy data quality problems become visible precisely when you try to use the data. Common findings: cause-of-loss coded as “other” in 40% of claims; party records duplicated many times over; reserve movements without reasons; and closure dates that reflect administrative action rather than actual resolution.

Response: profile early; fix at source where the source still exists; design the new system so that the problem cannot recur (mandatory structured fields, controlled vocabularies, validation rules); and accept that some historical data is simply not recoverable — document that limitation for future analysts rather than letting them discover it in a board paper.

8.7 Compliance and change control

Changing a claims system changes regulated processes. Involve compliance and data protection from the start: DPIA for new processing (especially AI), records of processing updated, retention rules configured before go-live rather than after, regulatory reporting continuity through cutover, and evidence that controls in the old system exist in the new one.

8.8 Vendor lock-in

Some lock-in is inevitable; the question is whether it is proportionate and whether you have an exit.

Protections to negotiate before signature, when you have leverage:

• Data extraction rights — full data export including documents, in a documented, usable format, at any time, at a defined (ideally zero) cost

• Configuration portability — at minimum, documentation of your configuration in a readable form

• Source code escrow — of limited practical value for SaaS, but worth considering for critical single-tenant deployments

• Price protection — capped uplifts, ideally indexed, for a defined term

• Exit assistance — contractually defined transition support with agreed rates and timescales

• Change of control — rights on vendor acquisition, which is common in this market

8.9 The failure modes, summarised

Chapter 9
The buying guide

9.1 Requirements gathering

Start with problems, not features. A requirements list assembled by asking each team “what do you need?” produces 400 undifferentiated requirements, most of which describe the current system. A better sequence:

• Baseline the operation. Volumes by class, cycle times, cost per claim, complaint themes, leakage findings, handler time analysis. This becomes both your requirements evidence and your benefits baseline.

• Map the current process as it actually runs, including the workarounds. The workarounds are the requirements.

• Identify the top ten pain points with quantified impact.

• Design the target process before looking at software. If you cannot describe how you want to work, you will buy someone else’s answer by default.

• Derive requirements from the target process, categorised MoSCoW.

• Separate “must be in the product” from “must be achievable” — configurability can satisfy many requirements that look like feature gaps.

9.2 Stakeholder mapping

Frequently forgotten: the actuarial and data teams, who will discover after go-live that a field they depend on no longer exists.

9.3 The evaluation process

A workable structure, typically 12–20 weeks:

 Week 1-3    Baseline + target process design
 Week 3-5    Requirements + business case
 Week 5-6    Market scan → longlist (8-12)
 Week 6-8    RFI → shortlist (4-6)
 Week 8-11   RFP + scripted demonstrations
 Week 11-13  Deep dives: technical, security, data model
 Week 13-15  Reference calls + site visits
 Week 15-17  Proof of concept / sandbox trial (shortlist of 2)
 Week 17-20  Commercial negotiation + contract

Two practices that materially improve outcomes:

Scripted demonstrations. Send every vendor the same three or four of your claim scenarios, with your data structure, and require them to demonstrate handling them end to end. Free-form demos show the vendor’s best path through their product; scripted demos show yours.

Sandbox trial. Give your own analysts and handlers hands-on access for two to four weeks. This is the single most predictive evaluation activity, because it reveals configurability, usability and support responsiveness — the three things that determine your next ten years and are invisible in a demo.

9.4 Vendor scorecard

Weightings below are a defensible default; adjust to context and agree them before you see any vendor, to prevent post-hoc rationalisation.

Scoring guidance: score 0–5 against defined descriptors written in advance, require written evidence for scores of 4–5, and record dissent. A scorecard where everyone agrees is usually a scorecard nobody engaged with.

9.5 Total cost of ownership

Year-one licence cost is the least informative number in the process. Model ten years:

9.6 Questions to ask suppliers

Product and configuration

• Show us a live configuration change: add a mandatory approval on reserves above a threshold. Who does it and how long?
• What proportion of your customers’ requirements require vendor development?
• Demonstrate a multi-feature claim with separate reserves and settlements.
• How are custom fields exposed to reporting and the API?

Implementation

• Who exactly will staff our project, and what else are they working on?
• Give three references implementing at our scale, in our jurisdiction, in the last 24 months — including one that went less well.
• What is your average time from contract to go-live for a comparable client, and how many of your last ten implementations hit the original date?
• What have you learned from an implementation that failed?

Commercial and risk

• What drives cost increases as we grow?
• What is your annual uplift mechanism?
• What is your data extraction process and cost?
• Who owns you, and has ownership changed in the last three years?
• What percentage of revenue goes into R&D?

Roadmap and support

• Show the last four release notes and the next four planned.
• How do customers influence the roadmap?
• What are your support SLAs by severity, and your actual performance against them last quarter?
• What is your process when a release breaks something in our configuration?

9.7 AI evaluation checklist

• Specific models named, hosting location disclosed, regional processing available

• Contractual guarantee that our data is not used for model training

• Retrieval grounding demonstrated on our documents, not a canned demo

• Accuracy measured and evidenced for our claim types

• Confidence thresholds configurable, with defined human review paths

• AI involvement recorded in the audit trail (model, version, inputs, reviewer)

• Bias testing methodology documented and shareable

• Features can be disabled per team, claim type or entirely

• Model change and notification policy defined

• Liability position for AI-caused harm stated in the contract

• DPIA support materials available

• Human override always available and captured

9.8 Security checklist

• ISO 27001 certified (scope covering the service we buy) — certificate reviewed, not just claimed

• SOC 2 Type II report available under NDA (relevant particularly for North American deployments)

• Independent penetration testing at least annually; summary available

• Encryption in transit (TLS 1.2+) and at rest (AES-256); key management documented

• SSO via SAML 2.0/OIDC; MFA enforceable; session policy configurable

• Granular RBAC including field and record-level restriction

• Immutable audit logging including read access to sensitive data

• Data residency guaranteed and contractually specified

• Sub-processor list disclosed with change notification rights

• Backup frequency, retention, and tested restore evidence

• Documented RTO and RPO, with evidence of DR testing

• Breach notification timescales in contract, aligned to regulatory obligations

• Secure SDLC: code review, dependency scanning, vulnerability SLAs

• Right to audit or to receive audit reports

9.9 Scalability checklist

• Evidence of customers at 3–5× our volume

• Documented performance benchmarks (page load, search, report generation at volume)

• Behaviour under surge (cat event) demonstrated or evidenced

• Bulk operations available (bulk reassignment, bulk update, bulk correspondence)

• API rate limits stated and sufficient for our integration volumes

• Reporting performance at full data volume, not demo volume

• Multi-entity/multi-client architecture if relevant

• Multi-currency and multi-jurisdiction support if relevant

• Onboarding time for a new user, evidenced

• Cost curve as volume grows — modelled, not asserted

Chapter 10
Implementation roadmap

10.1 Phases at a glance

 PHASE 0   PHASE 1      PHASE 2       PHASE 3     PHASE 4    PHASE 5     PHASE 6
 Mobilise  Discovery    Build &       Test        Train      Go-live     Optimise
           & design     configure                                        & scale
 2-4 wks   6-10 wks     8-16 wks      4-8 wks     3-6 wks    1-2 wks     Continuous
    │          │            │            │           │          │            │
 Sponsor   Process     Config       SIT/UAT      Role-based  Cutover   Benefits
 Team      Data model  Integrate    Perf test    Champions   Hypercare  Iteration
 Baseline  Migration   Migrate      Migration    Sandbox     Monitor    Phase 2
 metrics   plan        rehearse     rehearsal    practice    Stabilise  scope

Typical elapsed time: 6–12 months for a mid-sized single-class deployment; 12–24 months for a multi-class, multi-entity enterprise programme. Anyone promising a complex enterprise migration in eight weeks is describing a pilot, not an implementation.

10.2 Phase 0 — Mobilisation

Appoint the sponsor and a full-time product owner from claims. Establish governance and decision latency rules. Capture your baseline metrics — this is the last moment it is possible. Agree the benefits framework and who owns each benefit. Confirm scope and what is explicitly excluded.

10.3 Phase 1 — Discovery and design

Detailed process design for the target state (not the current state). Data model design including custom fields and controlled vocabularies. Integration specification per interface. Migration scope and mapping. Permission and authority matrix. Correspondence template inventory. Reporting requirements with agreed definitions.

Deliverable that pays for itself: a signed data dictionary defining every field and every status, including exactly what “closed” means. Ambiguity here poisons reporting permanently.

10.4 Phase 2 — Build and configure

Iterative configuration with fortnightly business review — not a six-month disappearance followed by a reveal. Integration development in parallel, with early connectivity testing. Migration rehearsals starting early and repeating. Correspondence templates built and reviewed by compliance.

Discipline: every deviation from standard product behaviour requires written justification approved by the sponsor. This single rule prevents most over-customisation.

10.5 Phase 3 — Testing

UAT must be run by the people who will use it, on scenarios drawn from your own claim history. UAT executed by the project team validates the project team’s understanding, which is not the risk you are trying to retire.

10.6 Phase 4 — Training

• Role-based, not one-size-fits-all

• Scenario-based, on migrated data in a sandbox that survives go-live

• Champions trained first and deeply, several weeks ahead

• Reference materials in the flow of work: short videos, in-app guidance, quick-reference cards

• Refresher sessions scheduled for 30 and 90 days post-go-live, when the real questions surface

• Training for supervisors on the management views, which are usually neglected entirely

10.7 Phase 5 — Go-live

Cutover checklist

• Final migration completed and reconciled, with sign-off
• Freeze window communicated to all stakeholders including suppliers and clients
• Rollback plan documented and tested
• Hypercare rota staffed, including vendor presence
• Floor-walkers assigned by team
• Issue triage process with agreed severity definitions and response times
• Daily stand-up scheduled for the first two weeks
• Caseload expectations formally reduced and communicated
• Communications sent to claimants, suppliers, brokers and clients
• Legacy system in agreed read-only or run-off state
• Regulatory reporting continuity confirmed
• Monitoring and alerting live on all integrations

Expect a productivity dip of two to six weeks. Plan for it, staff for it, and say so in advance. Programmes that promise no dip lose credibility in week one.

10.8 Phase 6 — Optimisation

The phase most often skipped, and where most of the benefit actually lands.

Months 1–3: stabilise, fix irritations fast, monitor adoption and shadow processes. Months 3–6: measure against baseline, tune workflows and rules with real data, expand automation where evidence supports it. Months 6–12: extend to further classes or entities, introduce advanced analytics, begin AI adoption sequence, formal benefits review against the business case.

10.9 Success metrics

Targets should be set against your baseline. Adopting someone else’s benchmark is how business cases become fiction.

10.10 Common implementation mistakes

• No baseline captured. You cannot prove benefit against a number you never measured.

• Automating the current process. Redesign first; configuration second.

• Part-time product owner. The role needs daily decision-making authority and availability.

• Migration scoped without profiling. Guarantees mid-project surprise.

• UAT run by the project team. Validates the wrong understanding.

• Training too early. Deliver it close to go-live or it evaporates.

• Big bang with no rollback. Even a low-probability failure needs a plan.

• Hypercare withdrawn too soon. Week three is when the difficult questions arrive.

• No optimisation phase budgeted. The benefit case assumes work that nobody funded.

• Declaring victory at go-live. Go-live is the start of value, not the end of the project.

Chapter 11
Industry use cases

Claims processes vary more between sectors than most software marketing acknowledges. This chapter sets out what actually differs.

11.1 General insurance (personal lines)

Characteristics: high volume, low average value, high automation potential, direct consumer contact, acute service sensitivity, aggregator-driven price competition.

Priorities: STP for simple claims, digital FNOL, self-service, supply chain settlement, fraud screening at scale, NPS measurement.

Class-specific notes: Motor — total loss determination, credit hire management, third-party capture, salvage, ADAS calibration cost. Home — escape of water dominates frequency and cost in many markets; surge management for storm and flood; validation of high-value contents. Travel — medical assistance coordination and cross-border supplier networks. Pet — high frequency, invoice-driven, strong extraction and STP potential.

11.2 Commercial insurance

Characteristics: lower volume, higher value, longer tail, broker-mediated, complex cover, multiple stakeholders, business interruption complexity.

Priorities: multi-feature claims, coverage analysis tooling, expert panel management, broker communication and visibility, large-loss governance, reinsurance identification, aggregation tracking.

11.3 Motor and fleet

Characteristics: high frequency, third-party exposure, downtime cost, recovery-rich, telematics-enabled.

Fleet-specific priorities: driver-initiated FNOL from a phone at the roadside; downtime minimisation through immediate repair booking and replacement vehicle provision; third-party intervention (capturing the third party early is the single biggest cost lever in motor); telematics-triggered FNOL; recovery and subrogation rates; and cost-per-vehicle reporting by depot, driver and vehicle type to feed risk management.

11.4 Property

Characteristics: surge-prone, supplier-intensive, scope-dispute-prone, emotionally significant for the claimant.

Priorities: validation and scoping at the earliest possible point (a video call with an adjuster within hours often prevents weeks of delay); contractor network management and quality control; alternative accommodation management; surge processes with event tagging; drying and restoration tracking; and clear scope agreement to prevent disputes later.

11.5 Healthcare and medical claims

Characteristics: high volume, invoice-driven, clinically complex, heavily regulated on data.

Priorities: provider network and pricing management; clinical coding validation; automated invoice adjudication against schedules and policy limits; pre-authorisation workflow; special category data protection (this is the most sensitive data in insurance); fraud, waste and abuse detection; and — in the US — extensive payer-specific requirements including HIPAA compliance and the specific mechanics of claims adjudication under coverage plans.

11.6 Construction

Characteristics: long-tail, technically complex, multi-party, litigation-prone, high value, latent defect exposure.

Priorities: multi-party liability apportionment; expert coordination (structural, geotechnical, quantum); contract and warranty analysis; long limitation periods with hard diary control; project documentation management at volume; and staged settlement tracking.

11.7 Marine, aviation and specialty

Characteristics: international, high value, jurisdictionally complex, subscription market (Lloyd’s and company market), general average and salvage concepts.

Priorities: multi-currency and multi-jurisdiction handling; surveyor coordination globally; co-insurance and subscription market shares; complex reinsurance and aggregation; documentation in multiple languages; and market messaging standards where applicable.

11.8 Utilities and infrastructure

Characteristics: third-party damage (strikes on buried assets), public liability, high claim volumes against the organisation, regulatory scrutiny.

Priorities: asset and location linkage (which asset, which contractor, which works order); contractor recovery; incident-to-claim conversion from field reporting; and evidence capture at the scene.

11.9 Housing associations and social housing

Characteristics: tenant-facing, reputationally sensitive, statutory obligations, disrepair claims exposure, high volumes of small property claims.

Priorities: tenant communication and vulnerability handling; disrepair claim management with legal cost control; linkage to repairs and asset management systems (the same defect frequently appears in both); property-level claims history to identify systemic problems; and transparency for regulators and boards. Disrepair claims in particular reward early identification and structured legal cost tracking, since costs frequently exceed damages.

11.10 Public sector

Characteristics: transparency obligations, procurement rules, public liability and employers’ liability exposure, political and media sensitivity, multi-department complexity.

Priorities: highway and public liability claim management with defect and inspection record linkage (the statutory defence in highways claims depends entirely on inspection evidence); FOI and complaint interaction; departmental cost allocation; risk management feedback loops; and procurement-compliant supplier management.

11.11 Self-insured organisations and captives

Characteristics: no external insurer to defer to, direct cost exposure, retention-driven decisions, risk management integration.

Priorities: incident capture from operational systems; total cost of risk reporting; retention and aggregate monitoring against excess layers; actuarial data quality for retention decisions; excess insurer notification thresholds; and — distinctively — genuine integration between claims and prevention, because the same organisation bears both costs.

11.12 TPAs and MGAs

TPA priorities: absolute client data segregation; client-specific workflows, SLAs and correspondence branding; client reporting portals and bordereaux; per-claim or per-service fee billing; rapid onboarding of new clients (a competitive differentiator — measured in weeks, not months, for market-leading operators); and evidence of delegated authority compliance.

MGA priorities: delegated authority controls enforced systemically; capacity provider reporting and bordereaux in required formats; claims data quality sufficient to satisfy capacity providers at renewal; binder-level aggregation and limits; and speed to launch new schemes.

11.13 Sector comparison

Chapter 12
Integrations

A claims platform’s value is bounded by what it can reach. This chapter covers the integrations that matter, what “good” looks like for each, and how to scope them.

12.1 The integration landscape

                          ┌──────────────────┐
        Policy admin ─────┤                  ├───── Finance / ERP
        Underwriting ─────┤                  ├───── Payment gateways
        CRM          ─────┤   CLAIMS         ├───── Banking rails
        Broker systems────┤   PLATFORM       ├───── Sanctions / KYC
                          │                  │
        Identity (SSO)────┤                  ├───── Microsoft 365 / Google
        Telephony    ─────┤                  ├───── SMS / messaging
        Supplier portals──┤                  ├───── E-signature
        Fraud bureaux ────┤                  ├───── Data warehouse / BI
        IoT / telematics──┤                  ├───── Reinsurance systems
                          └──────────────────┘
                    REST APIs · webhooks · events · SFTP · iPaaS

12.2 Policy administration

Why: every claim starts by answering “what cover was in force at the date of loss?”

What good looks like: real-time lookup rather than nightly extract; retrieval of the date-of-loss version including endorsements, not the current version; cover section and limit detail, not just a policy number; claims-experience write-back to inform renewal and pricing; and graceful degradation when the PAS is unavailable (cached data with a clear staleness indicator rather than a blank screen).

Common failure: overnight batch replication, which means a claim notified the day after a mid-term adjustment is validated against the wrong cover.

12.3 CRM

Why: single customer view, relationship context, and awareness of claims activity in servicing and renewal conversations.

What good looks like: shared customer identity; claim status visible to servicing teams without granting them full claim access; complaint linkage; and preference/consent synchronisation so a customer who opts out of marketing SMS still receives operational claim messages.

12.4 Finance and ERP

Why: payments must reconcile, reserves must post to the ledger, and recoveries must be tracked as receivables.

What good looks like: automated posting of reserve movements and payment transactions with the correct accounting treatment; cost centre and client allocation; supplier invoice matching; reconciliation reporting between claims and finance with an exception report rather than a manual comparison; multi-currency with a defined rate source and date convention; and tax treatment (VAT/GST, IPT interactions) handled explicitly.

The reconciliation point is worth emphasising: in many operations, the monthly claims-to-finance reconciliation consumes days of skilled effort. A well-designed integration reduces it to reviewing exceptions.

12.5 Payment gateways and banking

What good looks like: direct API integration to banking or payment providers; multiple methods (bank transfer, faster/instant payment rails, card, digital wallet, prepaid card, cheque where required); payee bank verification services; payment status returned into the claim automatically; failed payment handling workflow; and payment file generation as a fallback.

Control requirements: segregation of duties enforced in the system; dual approval above thresholds; bank detail change verification through an independent channel; sanctions re-screening at the moment of payment, not only at FNOL.

12.6 Identity providers and SSO

What good looks like: SAML 2.0 and/or OpenID Connect; SCIM provisioning and — critically — automated de-provisioning when someone leaves; group-to-role mapping so access rights follow HR changes; conditional access support; MFA enforcement; and session policy configuration.

Frequently missed: de-provisioning. Orphaned accounts are among the most common findings in claims system security audits.

12.7 Microsoft 365 and Google Workspace

The highest-frequency integration in daily use, and consequently the one where poor implementation causes the most cumulative friction.

What good looks like: two-way email sync with automatic filing to the correct claim; outbound email sent from the platform but appearing in the sender’s mailbox; calendar integration for appointments and site visits; document editing in place (Word/Excel/Docs) with the edited version returning to the claim; Teams or Chat notifications for escalations; and shared mailbox ingestion with allocation rules.

Test in the sandbox, not the demo: how does the system handle a reply-all chain that mentions three claim numbers? A forwarded email with the claim reference only in the body? An attachment over the size limit? These edge cases occur hundreds of times a week.

12.8 Business intelligence and data platforms

What good looks like: a documented, stable data model; either a direct read replica, a data export API, or native connectors to Power BI/Tableau/Looker; incremental extract capability rather than full refresh; historical/temporal data so “as at” reporting is possible; and no vendor gatekeeping of your own data.

Contractual point: confirm that bulk data extraction is permitted, at what frequency, and at what cost. Some agreements restrict this in ways that only become apparent when you try.

12.9 Sanctions, KYC and fraud data

Screening against sanctions and PEP lists at FNOL, at party addition and at payment; identity verification; industry fraud databases and claims registers (in the UK, for example, CUE, MIAFTR and the IFB’s systems; equivalents exist in most markets); and credit/bank verification services. These are usually third-party services with their own per-check costs — include them in TCO.

12.10 Telephony and contact centre

Screen-pop on inbound call with claim context; click-to-dial; call recording linked to the claim; transcription for file notes and QA; IVR integration for status self-service; and callback scheduling. The value is largely in eliminating the handler’s search-for-the-claim-while-the-customer-waits pattern.

12.11 Supplier and partner systems

Adjuster and engineer platforms; repair network management systems; medical agencies; legal case management; salvage platforms; and hire providers. Where direct API integration is impractical (common with smaller suppliers), a supplier portal with structured forms is a legitimate and often better answer than forcing integration.

12.12 IoT, telematics and connected data

Telematics crash detection triggering FNOL; connected home leak sensors; fleet dash-cam footage retrieval; building management systems; and — in commercial property — increasingly, monitoring data that establishes what happened and when.

Realistic view: the technology works; the barriers are consent, data rights, evidential standards and the operational question of what you do with an alert at 3am. Start with a narrow, well-defined use case.

12.13 Integration governance

Chapter 13
Security and compliance

Claims systems hold some of the most sensitive personal data any organisation processes: health information, financial details, allegations of criminal conduct, information about vulnerable people, and often data about the organisation’s own employees.

13.1 GDPR and data protection

Applicable in the UK and EU; the principles have influenced legislation well beyond (Brazil’s LGPD, South Africa’s POPIA, and increasingly US state laws such as the CPRA).

Obligations with direct system implications:

Practical point on SARs: claimants involved in disputes increasingly use subject access requests tactically. A system that cannot compile a complete, redacted response efficiently turns each SAR into days of manual work.

13.2 Conduct regulation

Requirements differ by market but converge on the same theme: evidence of fair outcomes.

• UK — FCA Consumer Duty (outcomes, understanding, support, fair value), ICOBS claims handling rules, vulnerability guidance, complaint handling rules (DISP) and Financial Ombudsman Service referral obligations.

• EU — IDD conduct requirements, national supervisory expectations, EIOPA guidance.

• US — state-level unfair claims settlement practices acts, prompt payment statutes, market conduct examinations, NAIC model regulations, and HIPAA where health data is involved.

• Australia, Canada, and others — general insurance codes of practice with claims-specific timeframes and vulnerability obligations.

System implications: outcome data capture; vulnerability identification, recording and handling adjustments; complaint capture with root-cause coding and regulatory clock management; evidence that communications were clear and understandable; and consistent decision recording that supports outcome testing.

13.3 Standards and certifications

A caution: certification demonstrates that a management system exists, not that the specific product is secure. Read the scope statement carefully — a certificate covering the vendor’s corporate IT but not the platform you are buying is not what you need.

13.4 Technical controls to require

• Encryption: TLS 1.2+ in transit; AES-256 at rest; documented key management with rotation; consider customer-managed keys for highly sensitive deployments

• Access: SSO, enforced MFA, RBAC with field and record-level restriction, least privilege, quarterly access reviews, automated de-provisioning, logged and time-limited break-glass access

• Monitoring: SIEM integration, anomalous access detection (bulk exports, out-of-hours access, access to claims outside a user’s normal portfolio), and alerting

• Application security: secure SDLC, code review, SAST/DAST, dependency scanning, defined vulnerability remediation SLAs by severity

• Testing: annual independent penetration testing at minimum, with summary reports available to customers

• Segregation: environment separation, with production data never used in test without anonymisation

13.5 Retention and deletion

Retention in claims is genuinely complex because different obligations run for different periods: limitation periods (long for injury and disease — potentially decades where latent conditions are involved), regulatory record-keeping, tax, and reinsurance.

What the system must support: retention rules by claim type and data category; automatic scheduling of review or deletion; legal hold that overrides deletion; defensible deletion evidence; and — often overlooked — retention applied to documents, communications and audit logs, not just the claim record.

13.6 Business continuity and disaster recovery

Define and test:

The question most buyers forget to ask: what is our process if the platform is unavailable for six hours during a catastrophe event? The answer should exist before you need it.

13.7 Vendor and supply chain risk

Request and review: sub-processor list with change notification rights; the vendor’s own supplier security assessment process; financial stability evidence; cyber insurance confirmation; incident history and how it was communicated; and contractual breach notification timescales aligned with your regulatory obligations (which will be tighter than a vendor’s default terms).

Chapter 14
Claims KPIs

Metrics shape behaviour. Choose badly and you will get exactly what you measured, at the expense of what you meant.

14.1 The metric families

  EFFICIENCY        FINANCIAL        QUALITY          EXPERIENCE      RISK
  ──────────        ──────────       ──────────       ──────────      ──────────
  Cycle time        Cost per claim   Leakage          NPS / CSAT      Fraud rate
  Touch count       Severity         Reserve accuracy Complaints      Reopen rate
  Claims per FTE    Frequency        Audit score      Chase contacts  Litigation %
  STP rate          Loss ratio       First-time-right Effort score    Compliance
  Automation rate   Recovery rate    Reversal rate    Resolution rate SLA breach

14.2 Definitions and interpretation

Cost per claim. Total claims handling expense (people, technology, suppliers’ handling fees, overhead) divided by claims closed. Excludes indemnity. Watch: falling cost per claim alongside rising leakage is a false economy — always read them together.

Average settlement time / cycle time. Days from notification to closure. Segment by claim type; a blended figure across motor and liability is meaningless. Use median as well as mean — the mean is dominated by a small number of long-tail claims. Watch: cycle time targets are the most common driver of premature closure.

Touch time vs elapsed time. Actual handler minutes worked versus calendar duration. The ratio exposes where claims are waiting rather than being worked — usually the biggest available improvement and the least visible one.

Leakage. The gap between correct and actual payment, measured through structured file audit against a defined standard. Requires a consistent audit methodology, trained auditors and a meaningful sample. Watch: leakage measured by the people who caused it produces reassuring results.

Severity. Average cost per claim (indemnity). Track by peril, class and cohort. Rising severity may indicate inflation, mix change, leakage, or reserving practice change — the diagnostic work is in decomposing it.

Frequency. Claims per policy or per exposure unit. Primarily an underwriting metric, but claims data quality determines its accuracy.

Reserve accuracy. Initial reserve vs ultimate cost, measured on closed claims. Report the distribution, not just the average: a mean of zero can conceal systematic over- and under-reserving cancelling out.

Recovery rate. Recovered amounts as a percentage of recoverable amounts — and the denominator is the hard part. Many operations report recoveries against total claims, which flatters the number and hides missed opportunities.

Automation rate. Percentage of process steps completed without human intervention. Useful directionally; easily gamed by counting trivial steps.

STP rate. Percentage of claims settled end-to-end with no human touch. Define precisely and consistently; always pair with an accuracy measure.

Fraud rate. Detected fraud as a percentage of claims, plus value saved. Report referral-to-confirmation conversion and false positive rate alongside, or you will incentivise over-referral.

Reopen rate. Percentage of closed claims reopened. One of the best single indicators of closure quality and the most useful counterweight to cycle-time targets.

NPS and CSAT. Measure at closure and, ideally, at an interim point. Segment by outcome — a declined claim will score poorly regardless of service quality, and mixing declines into the overall score obscures whether your process is working.

Customer effort score. Increasingly more predictive than satisfaction in service contexts: how much work did the claimant have to do?

Complaints per 1,000 claims and upheld rate. With root-cause coding, this is the most actionable quality metric available.

First-time-right / decision reversal rate. Percentage of decisions overturned on review, complaint or ombudsman referral. A direct measure of technical quality.

SLA compliance. By SLA type, client and team. For TPAs, contractually financial.

14.3 Balanced scorecard

The core discipline: never report an efficiency metric without its quality counterweight.

14.4 A practical dashboard hierarchy

14.5 Common measurement mistakes

• Reporting blended averages across incomparable claim types.

• Using means where the distribution is heavily skewed.

• Measuring what is easy rather than what matters (activity counts instead of outcomes).

• Efficiency metrics without quality counterweights.

• Vanity benchmarking against published figures with incompatible definitions.

• No agreed data dictionary, so two reports on the same metric disagree.

• Measuring so much that nothing gets attention.

• Never revisiting whether a metric still reflects the strategy.

Chapter 15
Future trends: signal vs noise

This chapter separates what is already changing operations, what is credible within two to three years, and what is currently more narrative than substance. Treat this as informed opinion rather than fact — it is a forward view, and forward views are wrong at a predictable rate.

15.1 Signal — happening now, at scale

AI-assisted handling. Summarisation, drafting, extraction and knowledge retrieval are in production and delivering measurable time savings. This is no longer speculative. The differentiator over the next two years is not access to models — everyone has that — but the quality of the data and documentation available to ground them.

Digital FNOL and self-service as default. Phone-first FNOL is becoming the exception in personal lines. The operational consequence is that data capture quality at intake is now a design problem rather than a training problem.

Supply chain settlement. Repair and replacement networks rather than cash settlement, driven by cost control and improved outcomes. Expanding from motor and home into contents, technology and health services.

100% call and communication QA. Transcription economics have made full-coverage quality monitoring viable, replacing 1–2% sampling. This changes what is knowable about an operation.

Configuration over code. Business users changing workflows and rules directly. The commercial consequence is that speed of change is becoming a competitive variable rather than an IT constraint.

15.2 Credible — two to three years out

AI agents with narrow, bounded autonomy. Not “AI handles claims”, but AI completing multi-step bounded tasks: gathering outstanding documents, chasing a supplier, assembling an evidence pack, preparing a settlement recommendation with rationale. The governance and audit machinery for this is the limiting factor, not the model capability.

Computer vision for property scoping. Vehicle damage assessment from images is working in narrow domains. Property is harder — occlusion, scale, hidden damage — but progress is real, particularly for interior water damage and roof assessment from drone or satellite imagery.

Embedded insurance claims. As insurance is sold inside other products (vehicle purchase, travel booking, e-commerce, device purchase), claims must be initiated and often resolved inside those same journeys via API. This is an architectural requirement more than a technology one: your claims platform needs to be callable by systems you do not control.

Parametric and index-based settlement. Pre-agreed payouts triggered by objective indices (rainfall, wind speed, flight delay, seismic intensity). Growing in agriculture, travel, event cancellation and catastrophe cover. It does not eliminate claims handling but changes it fundamentally: verification replaces assessment.

Real-time reserving. Model-updated reserves recalculated as new information arrives, rather than at handler-initiated review points. Technically feasible now; the barriers are financial governance and auditability.

IoT-triggered claims and prevention. Already real in fleet telematics and some commercial property. The more interesting development is prevention: sensors that avert the loss entirely, which changes the insurer’s role and, over time, the claims department’s volume.

Voice AI for intake and status. Conversational systems handling FNOL and status queries. The technology is close; the risk is deploying it where vulnerability detection matters, which requires careful routing design.

15.3 Noise — treat with scepticism

“Fully autonomous claims handling.” Demonstrable in narrow, low-value, high-certainty scenarios. Presented as imminent for claims generally, it ignores liability determination, coverage interpretation, vulnerability, regulatory accountability and the simple fact that someone must be answerable. Expect expansion at the simple end and no meaningful movement at the complex end.

“Blockchain will transform claims.” More than a decade of pilots have produced few durable production deployments in claims. Specific niches — parametric triggers, marine documentation, subscription-market data sharing — remain plausible. Generalised claims-on-blockchain does not.

“AI eliminates fraud.” AI improves detection. Fraudsters also have AI, including for generating synthetic documentation and images. This is an arms race, not a solved problem.

“Zero-touch everything.” Some claims genuinely benefit from human contact — bereavement, serious injury, total property loss, vulnerable customers. Optimising these to zero-touch damages both outcomes and reputation. The strategic goal is right-touch, not zero-touch.

“Metaverse claims” and similar. Periodically resurfaces. There is a real, small question about insuring digital assets; there is no evidence of a transformation in claims handling.

15.4 What to do about it

Chapter 16
The buyer’s toolkit

Practical, reusable artefacts. Adapt freely.

16.1 Software comparison checklist

Core claims handling

• Configurable FNOL by product and peril
• Multi-channel intake into a single structure
• Multi-feature claims with separate reserves and settlements
• Configurable workflow without vendor development
• Task, queue and diary management
• SLA tracking with business-hours calculation and pause conditions
• Dynamic re-triage when facts change
• Structured coverage position recording with reason codes

Financial

• Multi-head reserves with movement reasons and authority enforcement
• Payment approval with segregation of duties
• Duplicate payment detection and bank verification
• Recovery, salvage and subrogation tracking
• Reinsurance identification and cession support
• Multi-currency with defined rate handling

Evidence and communication

• Document management with version control and classification
• OCR and structured extraction with confidence thresholds
• Two-way email integration with auto-filing
• SMS and messaging with threading
• Claimant, supplier and client portals
• Mobile field capture with offline working
• Digital signature

Insight and control

• Role-based dashboards
• Self-service reporting and ad-hoc query
• Documented data dictionary
• Immutable, field-level audit trail including AI attribution
• RBAC with field and record-level restriction
• Retention and legal hold enforcement
• Vulnerability identification and handling adjustments
• Complaint capture with root-cause coding

Platform

• Documented REST API with sandbox and versioning policy
• Webhooks/events
• SSO and SCIM provisioning
• Data extraction rights confirmed contractually
• Performance evidence at 3–5× our volume

16.2 Vendor scorecard template

Score 0–5 against descriptors agreed in advance. Require written evidence for any score above 3. Record dissenting views.

16.3 Implementation checklist

Before contract

• Baseline metrics captured and signed off
• Target process designed
• Business case approved with named benefit owners
• Data profiling completed
• Integration inventory with priority classification
• Sponsor and full-time product owner appointed
• Data extraction and exit rights negotiated

During implementation

• Governance operating with defined decision latency
• Fortnightly business review of configuration
• Deviation-from-standard log with sponsor approval
• Migration rehearsed at least three times with reconciliation
• Integration monitoring built before go-live
• UAT by real users on real scenarios
• Training delivered close to go-live, role-based
• Rollback plan documented and tested

At and after go-live

• Hypercare staffed with vendor presence
• Caseload expectations formally reduced
• Daily issue triage for two weeks
• Adoption and shadow-process monitoring
• 30/90-day refresher training scheduled
• Benefits measured against baseline at 3, 6 and 12 months
• Optimisation phase funded and resourced

16.4 Executive approval checklist

• Problem stated in business terms with quantified impact

• Baseline metrics evidenced, not estimated

• Options considered, including do-nothing and improve-current

• Ten-year TCO with internal resource included

• Benefits with named owners, KPIs and realisation dates

• Risk register with mitigation and residual risk

• Regulatory and data protection assessment completed

• Resource impact on business-as-usual acknowledged and covered

• Governance structure and decision rights defined

• Success criteria and a defined review point

• Exit strategy and vendor dependency assessed

16.5 ROI calculator inputs

Volume and cost baseline

• Annual claims by class; average indemnity by class; total indemnity
• Claims handling FTE and fully-loaded cost
• Cost per claim (handling only)
• Supplier and expert spend
• Technology cost of current estate

Performance baseline

• Average and median cycle time by class
• Administrative time as % of handler time
• Inbound chase contacts per claim
• Complaint rate and cost per complaint
• Reopen rate
• Audited leakage rate
• Recovery rate against identified recoverable amounts
• Current STP/automation rate

Improvement assumptions (state each explicitly and conservatively)

• Leakage reduction (%) → indemnity saving
• Administrative time reduction (%) → capacity released (FTE equivalent)
• Cycle time reduction (%) → touches avoided and complaints avoided
• Chase contact reduction (%) → contact centre capacity
• Recovery rate improvement (%) → additional recoveries
• STP increase (%) → handling cost avoided
• Reduced technology and integration maintenance cost

Cost inputs

• Subscription (10 years, with uplift assumptions)
• Implementation, integration, migration
• Internal resource and backfill
• Training (initial and ongoing)
• Third-party services (screening, ID, payments, AI usage)
• Legacy run-off cost
• Contingency (20–30% is realistic for first-time enterprise implementations)

Outputs: net benefit by year, cumulative, payback period, NPV at your discount rate, and — importantly — a sensitivity analysis showing what happens if benefits land at 50% of assumption.

16.6 Procurement checklist

• Requirements documented and weighted before market engagement

• Competitive process appropriate to your procurement rules (note: public sector bodies have specific regimes)

• Evaluation panel with defined roles and conflict-of-interest declarations

• Scorecard weightings agreed before vendor contact

• Reference checks with clients of similar profile, including a difficult one

• Security due diligence completed by information security

• DPIA completed where required

• Contract review covering: SLAs with credits, data extraction, exit assistance, price uplift mechanism, liability caps, IP, change of control, AI provisions, sub-processors, breach notification

• Pilot or proof of concept before full commitment where feasible

• Approval and sign-off obtained per delegated authority

Chapter 17
Frequently asked questions

Fundamentals

Features and capability

Automation and AI

Implementation

Buying and vendors

Operations and metrics

Chapter 18
Glossary of 150 claims terms

Plain-English definitions. Where usage differs between markets, the difference is noted.

A

Actuary

— A professional who applies statistical methods to estimate future claim costs, reserves and pricing.

Adjuster

— A person who investigates and assesses claims. In the UK usually “loss adjuster” (often an external firm); in the US, often an insurer employee.

ADR (Alternative Dispute Resolution)

— Resolving disputes without litigation, typically through mediation or arbitration.

Aggregate

— A cumulative total, usually of claims against a limit within a policy period.

ALAE (Allocated Loss Adjustment Expense)

— Claim-handling costs attributable to a specific claim, such as legal or expert fees.

Allocation

— Assigning a claim, task or cost to a person, team, client or cost centre.

Apportionment

— Dividing liability or cost between multiple parties or insurers.

Arbitration

— Binding dispute resolution by an appointed arbitrator rather than a court.

Assignment

— Transferring a claim to a handler, or transferring rights under a policy to another party.

Audit trail

— An immutable record of every change to a claim: who, what, when, from where, and by what means.

Automation rate

— The proportion of process steps completed without human intervention.

Average (condition of)

— A clause reducing a settlement proportionally where the sum insured is less than the true value (underinsurance).

B

Bad faith

— Primarily a US concept: unreasonable claim denial or delay, exposing the insurer to damages beyond the policy limit.

Betterment

— The improvement in value when damaged property is replaced with new; the insured may contribute the difference.

Bordereau

— A structured periodic report of claims or premiums provided by an MGA or TPA to an insurer or capacity provider.

Broker

— An intermediary acting for the customer in arranging insurance and often assisting with claims.

Business interruption (BI)

— Cover for lost income and increased costs following an insured event affecting operations.

Business continuity

— Planning and capability to keep operating through disruption.

C

Captive

— An insurance company owned by the organisation whose risks it insures.

Case reserve

— The estimated ultimate cost of a specific known claim.

Catastrophe (cat) claim

— A claim arising from a large-scale event such as a storm, flood or earthquake, usually managed under a distinct surge process.

Cause code

— A structured classification of what caused the loss, essential for analytics and underwriting feedback.

Cession

— The portion of risk transferred from an insurer to a reinsurer.

Claim

— A request for payment or service under an insurance policy following a covered loss.

Claimant

— The person or organisation making the claim, whether the policyholder or a third party.

Claims handler

— The person responsible for managing a claim through its lifecycle.

Claims leakage

— The difference between what a claim should have cost and what it actually cost, caused by process failure.

Closure

— Formally concluding a claim once all actions are complete.

Coinsurance

— Risk shared between multiple insurers, or (in US health insurance) a percentage cost share borne by the insured.

Complaint

— An expression of dissatisfaction, with regulatory handling and reporting obligations in most markets.

Condition precedent

— A policy condition that must be satisfied before the insurer’s liability arises, such as prompt notification.

Contribution

— The principle that where multiple policies cover the same loss, insurers share the cost.

Coverage

— Whether and to what extent the policy responds to a particular loss.

CSAT

— Customer satisfaction score, typically captured by survey at claim closure.

Cycle time

— Elapsed time from notification to closure.

D

Deductible

— The amount borne by the insured before the policy pays. Called “excess” in UK usage.

Delegated authority

— Permission granted to a third party (MGA, TPA, broker) to bind cover or handle claims within defined limits.

Diary

— A scheduled future action on a claim; the core discipline of long-tail claims handling.

Digital signature

— Electronically executed signature with legal validity, used for discharge forms and settlement agreements.

Duty of fair presentation

— The insured’s obligation to disclose material information when arranging or renewing cover (UK Insurance Act 2015).

DPIA (Data Protection Impact Assessment)

— A structured assessment required for high-risk personal data processing.

E

Employers’ liability (EL)

— Cover for injury or illness suffered by employees; compulsory in the UK and equivalent to workers’ compensation elsewhere.

Endorsement

— A change to the policy terms, recorded as an amendment.

Escape of water

— Water damage from plumbing or appliances; a dominant peril in home insurance frequency and cost.

Excess

— UK term for the amount borne by the insured before the policy responds.

Exclusion

— A policy term specifying what is not covered.

Expert witness

— A specialist providing independent technical opinion, potentially for use in proceedings.

Exposure

— A distinct risk or potential liability; also used for the measure of insured risk.

F

Facultative reinsurance

— Reinsurance arranged for an individual risk rather than under a treaty.

Feature

— A distinct exposure within a claim, with its own reserve and potentially its own settlement. Also called a claim line, head of damage or sub-claim.

Fee scale

— Agreed rates for supplier or expert services.

First-party claim

— A claim by the policyholder under their own policy.

FNOL (First Notification of Loss)

— The initial report of a loss to the insurer; the most consequential data-capture moment in the lifecycle.

Fraud ring

— An organised group carrying out coordinated fraudulent claims, typically detected through link analysis rather than individual claim review.

Frequency

— The rate at which claims occur, usually per policy or per exposure unit.

G

General average

— A marine principle where all parties to a voyage share losses incurred to save the venture.

Grounding

— In AI, supplying a model with the relevant source documents so it answers from them rather than from training memory.

Gross written premium (GWP)

— Total premium written before deductions such as reinsurance.

H

Head of damage

— A category of loss within a claim (e.g. vehicle damage, injury, credit hire).

Hire (credit hire)

— Provision of a replacement vehicle to a non-fault party, with costs recovered from the at-fault insurer; a significant cost driver in UK motor.

Hold harmless

— A contractual agreement that one party will not hold another liable for specified losses.

I

IBNR (Incurred But Not Reported)

— An actuarial reserve for claims that have occurred but not yet been notified.

IDD (Insurance Distribution Directive)

— EU legislation governing insurance distribution conduct, retained in modified form in the UK.

Incident

— The event giving rise to a potential claim, which may be recorded before any claim is made.

Incurred

— Paid amounts plus outstanding reserves; the current estimated total cost of a claim.

Indemnity

— The principle of restoring the insured to their pre-loss position, no better and no worse.

Insurable interest

— A legally recognised interest in the insured subject matter, required for a valid policy.

Insured peril

— An event specified in the policy as covered.

Interim payment

— A payment on account before final settlement, common in injury and large property claims.

ISO 27001

— International standard for information security management systems.

J

Joint and several liability

Where multiple parties are each fully liable for the whole loss, allowing a claimant to recover in full from any one of them.

K

Knock-for-knock

An agreement between insurers to each bear their own insured’s damage regardless of fault, reducing administrative cost.

L

Large loss

— A claim above a defined value threshold, triggering enhanced governance, senior handling and often reinsurer notification.

Latent defect

— A hidden flaw that manifests long after construction or manufacture, a key driver of long-tail construction claims.

Legal expenses insurance

— Cover for legal costs of pursuing or defending claims.

Liability

— Legal responsibility for loss or damage caused to another.

Limit of indemnity

— The maximum the policy will pay, in total or per claim.

Limitation period

— The statutory deadline for bringing a legal claim; missing it usually extinguishes the right to recover.

Loss adjuster

— An independent professional appointed to investigate and assess a claim, common in UK and European practice.

Loss ratio

— Claims cost as a percentage of premium; the core measure of underwriting performance.

Loss run

— A report of an organisation’s claims history, frequently required at renewal.

M

MGA (Managing General Agent)

— An intermediary with delegated authority to underwrite, and often to handle claims, on behalf of an insurer.

Material fact

— Information that would influence an underwriter’s decision to accept a risk or on what terms.

Mediation

— Non-binding facilitated negotiation, common in liability and construction disputes.

MIAFTR

— UK Motor Insurance Anti-Fraud and Theft Register, recording written-off and stolen vehicles.

Mitigation

— The insured’s duty to take reasonable steps to limit the loss.

Multi-feature claim

— A claim containing several distinct exposures, each independently reserved and settled.

N

Named perils

— A policy covering only specifically listed events, as opposed to all-risks cover.

Negligence

— Failure to take reasonable care, the foundation of most liability claims.

Net paid

— Amounts paid after deduction of recoveries and salvage.

NPS (Net Promoter Score)

— A measure of likelihood to recommend, widely used as a claims experience metric.

Notification

— Informing the insurer of a loss or circumstance that may give rise to a claim.

O

Occurrence

— An event giving rise to a claim; also a policy basis triggered by when the event happened rather than when it was reported.

Ombudsman

— An independent body resolving unresolved consumer complaints, such as the UK Financial Ombudsman Service.

Open claim

— A claim not yet closed, carrying an outstanding reserve.

Outstanding reserve

— The estimated remaining cost of an open claim.

Own damage

— Damage to the insured’s own property, as opposed to third-party liability.

P

Paid loss

— Amounts actually paid out on a claim to date.

Parametric insurance

— Cover paying a pre-agreed amount when an objective index or trigger is met, without loss assessment.

Part 36 offer

— A formal settlement offer under English civil procedure carrying costs consequences if refused and not beaten at trial.

Peril

— The cause of a loss, such as fire, flood or theft.

Periodical payment order (PPO)

— A court-ordered structure paying an injured claimant annual sums for life rather than a lump sum.

Policy administration system (PAS)

— The system managing policy lifecycle, distinct from the claims system.

Policy wording

— The contractual terms of the policy, which must be read at the version applicable at the date of loss.

Portfolio transfer

— Moving a book of policies or claims from one carrier or administrator to another.

Pre-authorisation

— Approval obtained before treatment or work commences, common in health and some property claims.

Proximate cause

— The dominant, effective cause of a loss, determining whether an insured peril applies.

Public liability

— Cover for injury or damage caused to third parties by the insured’s activities or premises.

Q

Quantum

— The amount of a claim, as distinct from liability for it.

Quota share

— Proportional reinsurance where the reinsurer takes a fixed percentage of premiums and losses.

R

Recovery

— Money reclaimed to offset the claim cost, including salvage, excess collection and subrogation proceeds.

Reinstatement

— Restoring damaged property to its former condition; also restoring a policy limit after a loss.

Reinsurance

— Insurance purchased by insurers to limit their own exposure.

Reopen rate

— The percentage of closed claims subsequently reopened; a key closure quality indicator.

Repudiation

— Declining a claim on the grounds that the policy does not respond.

Reservation of rights

— A notice that the insurer is investigating while preserving its position on coverage.

Reserve

— The estimated ultimate cost of a claim, held on the balance sheet.

Retention

— The amount of risk an organisation keeps rather than transferring; also, the period for which records are kept.

Run-off

— Managing existing claims on a book no longer being written.

S

Salvage

— Value recovered from damaged property, such as a written-off vehicle.

Sanctions screening

— Checking parties against sanctions and politically exposed person lists before payment.

Severity

— The average cost of claims.

SLA (Service Level Agreement)

— A defined performance standard, often contractual and financially backed for TPAs.

Special category data

— Sensitive personal data under GDPR, including health data, requiring additional protection.

STP (Straight-through processing)

— A claim settled end-to-end without human intervention.

Subrogation

— The insurer’s right to pursue a third party responsible for the loss, standing in the insured’s place.

Sum insured

— The maximum amount for which property or an interest is insured.

Surge

— A sudden volume spike, typically from weather events, requiring distinct operational processes.

T

Third-party administrator (TPA)

— An organisation handling claims on behalf of insurers or self-insured clients.

Third-party claim

— A claim brought by someone other than the policyholder, against the policyholder’s liability cover.

Total loss

— Where repair is uneconomic or impossible and the item is settled as a whole.

Touch time

— Actual handler time worked on a claim, as distinct from elapsed calendar time.

Treaty reinsurance

— Reinsurance covering a defined class or portfolio automatically, rather than risk by risk.

Triage

— Assessing a claim at intake to determine complexity, urgency and handling route.

U

Uberrimae fidei (utmost good faith)

— The principle that insurance contracts require complete honesty from both parties.

Ultimate cost

— The final total cost of a claim once fully settled and closed.

Underinsurance

— Where the sum insured is less than the true value at risk, potentially triggering average.

Underwriting

— Assessing, selecting and pricing risk.

Uninsured loss recovery (ULR)

— Recovering losses not covered by the policy, such as an excess, from a liable third party.

V

Valued policy

— A policy where the amount payable is agreed in advance rather than assessed at the time of loss.

Voidable policy

— A policy the insurer may treat as invalid, typically due to material misrepresentation.

Vulnerable customer

— A customer whose circumstances make them especially susceptible to harm, requiring adjusted handling under conduct regulation in many markets.

W

Waiver

— Voluntarily giving up a right, such as the right to rely on a breached policy condition.

Warranty

— A policy term requiring something to be done or maintained; breach may affect cover.

Wear and tear

— Gradual deterioration, typically excluded from cover.

Without prejudice

— Communication made in settlement negotiation that cannot generally be used as evidence.

Write-off

— Formally abandoning a recovery or reserve as uncollectable, or declaring an item a total loss.

X, Y, Z

Excess of loss (XoL)

— Non-proportional reinsurance responding above an attachment point.

Yield (recovery yield)

— The proportion of identified recoverable amounts actually recovered.

Zero-touch claim

— A claim requiring no human involvement at any point; the extreme form of straight-through processing.