Insurance Company Fraud Detection — Tools and Tactics

Last updated: 29 May 2026

If you run claims for a UK insurer, MGA, or third-party administrator, the question we get asked most often isn't whether fraud is rising — everyone can see that it is. The ABI's 2024 figures put detected general insurance fraud at £1.1bn, and the undetected number is generally reckoned to be at least double that. What you actually need to know is which detection investments pay back and which get sold to you as silver bullets and then quietly underdeliver. This piece walks through what works, what's hype, and how the bits stitch together — including the part most pillar articles skip, which is how you handle card and bank data when you do pay out a legitimate claim without opening a different kind of risk.

We sit on the payments side of this — Paytia handles PCI-compliant payment capture for several UK insurers and TPAs — so the angle here is heavier on the data-control questions than the actuarial ones. If you want the wider context first, our claims processing guide is the pillar that this article sits under, and our piece on PCI compliance for insurance claims covers the regulatory ground around payment data specifically.

What modern insurance fraud detection actually looks like in 2026#

Ten years ago, fraud detection in a typical UK insurer meant a referral form, a SIU (special investigations unit) of ex-police, and a spreadsheet of known dodgy postcodes. That model still exists at smaller MGAs, and it still catches some fraud, but it's nowhere near enough for the volume and sophistication of organised claims fraud that the market sees now. The carriers catching the most fraud have shifted from human-only triage to a layered model where models, networks, and humans each do what they're good at.

The four layers that show up consistently in mature programmes are network analytics (who is connected to whom across claims, policies, addresses, bank accounts, phone numbers, devices), behavioural analytics (how does this claim's notification pattern compare to known fraud rings), content analytics (do the photos, documents, and voice patterns hold up under forensic checks), and payment-stage controls (does the settlement actually land with the genuine claimant or has the destination been swapped at the last minute).

None of these layers works on its own. Network analytics catches organised rings but misses solo opportunists. Voice analytics catches stress patterns in fabricated FNOL calls but won't help you with a written online claim. Payment-stage controls catch the last-mile diversion attempts that get through everything upstream. You need all four, and the order they fire in matters because a model that flags too late costs you the claim payment, and a model that flags too early annoys genuine customers into churn.

Network analytics: the layer that catches the rings#

Organised claims fraud — staged accidents, fabricated injuries, ghost broking — almost always shows up as a network signal before it shows up as a claim signal. The same bank account or postcode appears across multiple claims with different surnames. The same vehicle keeps being written off and re-registered. The same medical reporting organisation keeps appearing on no-fault personal injury claims with the same handful of solicitors. None of these patterns are visible from a single claim file, which is why the rings used to win for years.

Network analytics tools (Quantexa, BAE NetReveal, Friss, Shift Technology — the names you'll see in the fraud detection tools comparison article) build a connected graph of every entity in your book and surface clusters that look statistically unusual. The implementation question that matters is whether the graph also includes data from the cross-industry fraud database (CIFAS in the UK) and the insurance industry register (IFB / IFR) — without that, you're only seeing rings that have hit your book before, not rings that have hit other carriers.

Two things go wrong here. The first is alert fatigue: poorly tuned graph models flag thousands of weak signals, the SIU drowns, and the genuine high-risk clusters get lost in the noise. The second is over-blocking — the model fires on a genuine extended family who happen to share an address, and your customer experience team gets the complaint. The carriers that get this right invest as heavily in alert triage workflows and feedback loops as they do in the analytics engine itself.

Voice analytics on FNOL calls#

First Notification of Loss is when most claims fraud is born. The claimant calls in, tells the story, and your handler captures it. Voice analytics tools listen to that audio in real time or in post-call review and flag patterns associated with deception — unusual hesitation, vocabulary shifts, rehearsed-sounding phrasing, voice stress markers. The honest answer is that the science here is more contested than the vendors will tell you. Voice stress analysis as a standalone fraud indicator has been picked apart in peer-reviewed studies, and you shouldn't be making claim decisions on a stress score alone.

Where voice analytics earns its keep is as a triage signal that feeds into the wider model. A high stress score on its own means nothing; a high stress score on a claim that also has weak network signals, a vehicle bought a week before the loss, and inconsistent damage photos is a strong combined signal that warrants a deeper investigation. The model is the value, not the individual feature.

There's a separate operational gain from recording and transcribing FNOL calls that nothing to do with fraud detection per se — it gives you a baseline for what the claimant said at first contact, which is invaluable when their story shifts later. Discrepancies between FNOL and subsequent statements are themselves a fraud signal, and you can only catch them if you've captured FNOL accurately.

Image and document forensics#

Photographs and supporting documents submitted with a claim used to be near-impossible to verify at scale. That changed when image forensics moved from forensic labs into commodity SaaS. Current tools check EXIF metadata for evidence of editing, run reverse image search against a database of previously submitted photos (catching the same crash photo being reused across multiple claims), detect generative AI signatures in synthetic images, and run structural analysis on documents to spot fabricated invoices, repair quotes, or medical reports.

The reverse-image-search check is the cheapest and highest-yield. A meaningful share of small property and motor claims get adjudicated with photos that have already appeared on Facebook Marketplace, eBay damaged-stock listings, or other carriers' claims. Building a shared industry database here has been talked about for years and is starting to happen through the IFB; in the meantime, individual carriers and the larger TPAs are building their own image hashes against historic claims.

Document forensics is harder to call. A well-faked repair invoice from a fictional garage with a working website and a valid VAT number can be very difficult to flag from the document alone — you usually catch these by cross-referencing the supplier against an external register or by detecting the same supplier appearing across multiple unrelated claims, which loops you back to network analytics. The lesson, as so often, is that no single signal is decisive. The integration of signals is.

Behavioural and biometric signals on digital channels#

For claims submitted through a portal or app, behavioural biometrics quietly do a lot of work. Tools like BioCatch and BehavioSec record how the claimant types, moves the cursor, taps the screen, and pauses. Genuine first-party claimants behave in characteristic ways; mules and impersonators don't. Specific signals — copy-paste of card numbers (rather than typing them), unfamiliarity with the claimant's own name or address fields, oddly fast or oddly hesitant entry on personal data — feed straight into the fraud model.

The same data also helps on the genuine-customer side of the balance. If the model is confident the person submitting the claim is the policyholder, you can fast-track payment and avoid friction. That trade-off — speed for honest customers, friction for suspect ones — is the bit that turns fraud detection from a cost centre into a customer experience win, and it's what the carriers using behavioural biometrics seriously talk about most.

The settlement-stage trap most carriers underestimate#

You've done the network analytics, the FNOL voice review, the image forensics, the behavioural check. The claim is genuine, the model approved it, you're ready to pay. This is where a different class of fraud appears, and it's the one that sits on our side of the fence at Paytia.

Bank-detail diversion fraud has grown fast as the upstream fraud signals have got better. Criminals don't bother fabricating a whole claim if they can intercept a real one at the last mile. The attack pattern is usually social engineering — someone calls your contact centre claiming to be the genuine claimant, gives enough policy data to authenticate, and asks to change the settlement bank account to a new one. The change goes through, payment goes out, and by the time the genuine claimant calls to chase, the mule account has been drained.

The controls that block this aren't analytical — they're procedural. A cooling-off period on bank-detail changes (most carriers now hold for at least 24 hours before paying out to a freshly added account). Out-of-band verification through a known good channel before the change is effected. Step-up authentication on the call — the kind that DTMF masking and channel separation make possible without your agent ever hearing the authentication factors. And, critically, separating who can capture a new bank detail from who can approve a payment to it.

For card-based refunds (you'd be surprised how often we see refunds go to a card rather than to the original bank account), the same principle applies in payment form. The card capture has to be PCI-compliant — which is the bit our piece on claims payment integration covers in detail — but it also has to be operationally separated from the claim decision. If the handler who approved the claim is also the one keying the card, you've collapsed the segregation of duties that stops insider fraud.

Internal fraud: the conversation no one wants#

External fraud gets all the attention. Internal fraud — handlers approving claims to friends, agents skimming settlement amounts, contact centre staff diverting refund payments to mule cards — is harder to talk about because it makes everyone feel uncomfortable, but every SIU we talk to says it's a real and persistent share of the loss number.

Three controls do most of the work. The first is making sure no single person can both decide a claim and execute the payment. The second is auditable trails on who saw what data and who changed which fields, which is where good claims systems and good payment systems should both be giving you forensic logs as standard. The third is keeping cardholder data out of the agent's view entirely — the card not present pillar covers this in depth for general payments, and the same logic applies to claims settlements paid by card.

One real example. A UK contact centre we work with ran an internal review after a small uptick in refund-to-card complaints. The investigation found two agents who had been issuing tiny refunds (£2-£8) to mule cards under cover of genuine claim payments, banking on the amounts being too small to investigate individually. The total had grown to a meaningful number over eight months. The detection didn't come from analytics — it came from a claimant complaint and a manual audit. Channel separation, where the agent never sees the card data and the cardholder enters it themselves via keypad, would have stopped this at the start.

Regulatory pressure on fraud controls#

The FCA's Consumer Duty has changed the conversation around fraud detection in a way that's not always obvious. The duty requires firms to act in good faith and avoid causing foreseeable harm to retail customers. False positive fraud blocks — genuine customers wrongly denied or delayed — are a foreseeable harm under the duty. The implication is that fraud models have to be tested for fairness and for false-positive rates, not just for true-positive yield.

In practice that means documenting your fraud model's performance characteristics, monitoring for disparate impact across customer segments, and having a clear and prompt route for declined customers to challenge a decision. Carriers that can show they've thought about this come out of FCA reviews much cleaner than those that can't. The model accuracy conversation has moved from a SIU concern to a regulatory one.

On the data protection side, processing claims data through third-party fraud analytics tools sits under UK GDPR, which means a documented lawful basis (legitimate interest, with a balancing test) and a clear retention policy. The Insurance Fraud Bureau (IFB) and CIFAS both have well-established data-sharing frameworks that handle the legal architecture for you, which is a good reason to use them rather than building bilateral data swaps with other carriers.

What an integrated fraud detection stack looks like#

The fragmented vendor landscape makes it tempting to buy point solutions for each signal type and let your in-house team stitch them together. A few carriers do this well, but the more common outcome is a sprawl of dashboards, no single risk score per claim, and SIU teams who can't see the wood for the trees. The integrated model — one platform that ingests all signals and produces a single fraud risk score with explainable drivers — is harder to procure but easier to operate.

The integration questions that matter at the procurement stage are: does the platform ingest from your policy administration system without a custom connector, does it write back to your claims workflow in real time (so a high-risk score routes to SIU without a handler having to refer manually), and does it expose model decisions in a way you can explain to a customer, a regulator, and an internal auditor. The vendor that ticks the third box is usually the one to take seriously.

For carriers below a certain scale (under about £150m gross written premium), the build-versus-buy maths tilts hard towards buying. The ongoing model maintenance, the data engineering, and the need to keep up with evolving fraud patterns are all costs that don't shrink with your portfolio. The carriers that win at the smaller end tend to be the ones that pick a credible best-of-breed platform and integrate it well, rather than the ones that try to build their own analytics in-house.

Where third-party administrators sit in this picture#

A lot of UK general insurance — particularly in travel, motor, gadget, and pet — is administered by TPAs rather than by the insurer directly. That changes the fraud-detection picture in ways that aren't always recognised. The TPA holds the operational relationship with the claimant; the insurer carries the risk. The data, the analytics signals, the SIU outcomes, and the feedback loops all sit at the TPA. When fraud volumes spike, the conversation between insurer and TPA can get awkward fast.

The contractual side of this matters. Service level agreements between insurer and TPA need to specify not just average handling time and customer satisfaction but also fraud detection performance — what proportion of claims are flagged, what the false-positive rate looks like, how SIU outcomes are reported back to the insurer. Without that detail, an insurer can find themselves carrying losses on a book where the TPA's incentive structure is weighted entirely towards speed and satisfaction rather than detection.

The data-sharing side matters too. Most TPAs handle multiple insurers, which creates a real opportunity to spot fraud patterns across carriers — the same fraudster filing similar claims with different insurers through the same TPA is exactly the kind of pattern network analytics should catch. The legal architecture for cross-carrier signal sharing within a single TPA is much cleaner than between independent carriers, and it's an underused fraud detection advantage for TPAs that run multi-insurer books.

Measuring whether your fraud programme is actually working#

The single most common metric people use — detected fraud as a percentage of premium — is misleading on its own. A high detection rate could mean your programme is excellent, or it could mean you have an unusually fraud-prone book. A low detection rate could mean you're missing fraud, or it could mean you have an unusually clean book. The metrics that actually tell you something are model precision (of the claims flagged, what share were genuinely fraudulent), model recall (of the genuinely fraudulent claims, what share did the model catch), false positive rate (how many genuine customers got blocked), and the loss-ratio delta against a control group of unflagged claims.

The other metric that matters is the speed-to-payment for unflagged claims. If your fraud programme is slowing down 95% of genuine claims to catch the 5% suspicious ones, you've shipped a customer experience problem in the guise of a risk control. The good programmes get the model confident enough that the bottom 70% of claims by risk score get fast-tracked to payment within 48 hours, and only the top 30% by risk see additional checks.

The fraud patterns that are growing fastest in 2026#

The mix of fraud types insurers are seeing has shifted noticeably over the last 18 months, and the programmes worth building today are the ones tuned to where the volume is heading, not where it was. Three patterns deserve specific mention because they don't map cleanly onto the SIU playbooks of five years ago.

The first is generative-AI-assisted claims fabrication. Synthetic damage photos, AI-generated repair invoices, fabricated medical reports written in convincingly clinical language — all of these are now achievable for a fraudster with a £20 monthly AI subscription and no specific expertise. The detection answer is partly technical (image forensics that look for diffusion-model artifacts, document forensics that catch the linguistic patterns of large language models), but it's also operational — asking for original metadata-intact files rather than re-saved JPEGs, requesting verifiable supplier references, and triangulating documents against external registers like Companies House or the FCA's regulated firms list.

The second is account takeover at the claim notification stage. The fraudster compromises a genuine policyholder's email or phone, files a fictitious claim in their name, and diverts the settlement. The genuine customer often doesn't notice until the renewal letter or a subsequent legitimate claim flags up the prior loss history. Detection here lives at the authentication layer — strong customer authentication on the claim portal, channel verification when claim details change, and an independent path to the policyholder if the claim looks materially different from their historic pattern.

The third is the small-amount, high-volume pattern. Rather than one £40,000 staged accident, fraudsters increasingly run dozens or hundreds of small claims — £300 property damage, £150 contents loss, £80 travel-bag-missing — banking on the cost-to-investigate being higher than the cost-to-pay. The model answer is risk-scoring at very small ticket sizes and accepting some leakage on truly small claims while still aggregating the pattern across the network. The carriers that pay every £200 claim through without thinking are the carriers feeding this pattern.

Building an internal feedback loop that actually improves the model#

Off-the-shelf fraud models degrade. Fraud patterns evolve, your portfolio composition shifts, and signals that were predictive last year start firing on genuine customers this year. The carriers that get the most out of their analytics investment are the ones that have built tight feedback loops between SIU outcomes and model retraining.

The mechanic is straightforward in principle but rarely well-executed in practice. Every claim the model flags should end up with a labelled outcome — confirmed fraud, suspected but not confirmed, false positive, not investigated. Those labels feed back into the next model training cycle, which sharpens precision without losing recall. The carriers that get this wrong tend to lose the labels somewhere between SIU's case management system and the analytics vendor's training pipeline, and within 18 months the model is firing on patterns that have already shifted.

The other half of the feedback loop is on the customer-experience side. Every false positive — every genuine claim wrongly slowed or declined — should generate a learning. Carriers that treat false positives as the cost of doing business and don't investigate them lose the ability to tune the model for fairness, which is exactly the Consumer Duty risk we covered earlier. The carriers that do well here have a clear, prompt route for customers to challenge a decision, and they feed those overturned decisions back into the next model iteration.

How fraud detection interacts with claims handler training#

Analytics platforms don't replace human judgment; they sharpen where humans focus. A claims handler with a clear risk score in front of them, with the specific drivers exposed (high network connectivity to a known ring, voice stress pattern unusual for first-time callers, photo metadata showing edits) can ask much better follow-up questions than one staring at a raw claim form. The investment in claims handler training has to evolve alongside the analytics investment, otherwise the platform produces signals nobody can act on intelligently.

The carriers we see doing this best treat fraud awareness as a continuous discipline rather than a one-off training module. Monthly briefings on emerging patterns. Anonymised case studies of recent confirmed frauds, with the signals that should have been spotted earlier. Practical exercises on how to handle the "I just need to change my bank details" call that might be a genuine claimant or might be the start of a diversion fraud. None of this is expensive; all of it compounds over time in a way pure analytics investment doesn't.

The other operational point worth making is that fraud detection programmes that don't have visible SIU and contact centre buy-in tend to underperform. Handlers who don't trust the model will refer everything to SIU just in case, which floods the queue and slows genuine claims. Handlers who trust the model — because they've seen it catch obvious frauds early in their tenure — refer the right cases and act on the rest. That trust is built through training and through making model decisions explainable, which is one reason the explainability question comes up so heavily at procurement.

The economics: what does a fraud programme actually cost?#

The honest answer is that the cost varies massively with carrier size and book mix, but the rough ratios are useful. A mature fraud detection programme at a mid-market UK general insurer typically costs between 1% and 2% of gross written premium — covering the analytics platform licence, the SIU team, integration and data engineering, plus a share of the claims platform that's dedicated to fraud workflows. Against that, the programme should be delivering between 3% and 5% of GWP in detected and prevented losses, which is a comfortable 2-3x payback before you count the reputational and regulatory benefits.

Smaller carriers and MGAs run thinner programmes — often a single fraud analyst or part-time SIU function supported by one analytics tool — and the payback is harder to demonstrate cleanly because the loss volume is lower. The case for fraud investment at smaller scale isn't usually about ROI on the loss numbers; it's about the regulatory exposure of being seen to have weak controls if a major fraud lands, and about positioning the carrier to scale up cleanly when it grows.

The cost item that tends to surprise people is integration and data engineering. The analytics platform licence is rarely the biggest line item — it's the work of plumbing policy administration, claims, billing, contact centre, and external data sources into the platform in a way that produces a clean, real-time risk score on every claim. Carriers that underbudget for integration end up with platforms that produce overnight batch reports rather than real-time scores, which is barely better than the spreadsheet-and-SIU model it was meant to replace.

Where Paytia fits in the stack#

We're not trying to be your whole fraud platform. What Paytia does is the payment data control layer — making sure that when card or bank detail capture is needed during claim settlement, it happens in a way that doesn't expose the data to your agents, doesn't put cardholder data into your call recordings, and doesn't drag your claims contact centre into PCI scope. For UK insurers and TPAs settling claims by card refund or capturing card data for premium collection, this is the unsexy plumbing that prevents the unsexy losses.

The clients we work with in this space — insurance brokers, claims TPAs, motor and travel insurers — consistently report that the visible win is PCI scope reduction (Pinnacle Group cut audited scope by 95% with our channel-separation model, which the Pinnacle Group case study covers in detail), but the less visible win is the audit trail on every payment-data touch, which is exactly what you need when internal-fraud questions come up. Travel insurer InsureandGo and others in the sector run the same model for the same reasons — refunds and premium captures happen without payment data ever entering the agent's view, which closes off the internal-fraud vector before it opens.

The integration question for fraud-conscious insurers is what gets logged and how. Every payment-data touch through the Paytia platform produces an audit trail — when the capture started, which user initiated it (without that user ever seeing the card), what tokenised reference was produced, and how the resulting payment was approved and executed. That log lives alongside your fraud platform's logs and gives you a complete forensic picture if a settlement is later questioned, whether by a customer dispute, an internal audit, or a regulator's investigation.

A short checklist for tightening up an existing programme#

If you're already running a fraud detection programme and the question is what to improve next, the patterns we see most often that need fixing are these. Each is concrete enough to action without a procurement cycle, and each closes a specific loss vector that mature programmes still leak on.

The first is reviewing your bank-detail-change controls from start to finish. Pick a random sample of recent claims where the settlement bank account was changed after FNOL, and trace the call recording, the authentication challenge, and the cooling-off period actually applied. Most carriers find at least one case in a small sample where the control didn't fire as designed. The second is checking your image forensics coverage — if you're not running reverse image search on photo evidence, that's the highest-yield single thing you can add for a relatively modest cost. The third is auditing your handler-side segregation of duties — pull the list of claim refunds processed by card in the last quarter and check whether the same user ID authorised the claim and the payment. That's the internal-fraud control that gets quietly weakened over time as teams get smaller and roles get blurred.

The fourth is measuring your false-positive rate honestly. Talk to the contact centre team about how many genuine customers complained about a claim being slowed or declined, and trace those complaints back to the model. If the rate is anywhere above 2-3% of flagged claims, you've got tuning work to do. The fifth is checking what data your fraud platform shares with CIFAS and the IFB, and whether you're getting full benefit of the cross-industry signals. Many carriers contribute but don't fully consume what's available.

Next steps#

If you're scoping a new fraud detection programme or refreshing an existing one, the obvious starting point is the fraud detection tools comparison piece, which walks through the major vendors and what they're each strong at. If your immediate problem is on the payment-data control side rather than the analytics side, our insurance industry page sets out how channel separation works for claims settlement specifically, and a 15-minute call or a live working demo is the fastest way to see whether the model fits how your claims team operates today.