Fraud identified only after payment is made -- when recovery is expensive, slow, and rarely complete -- because the adjuster workflow has no automated scoring step to flag suspicious claims before settlement?
Claims with multiple fraud indicators such as staged accident patterns, frequent claimants, and linked claimant networks processed straight through because the adjuster reviewing the file has no tool to surface those signals?
Insurance Fraud Detection Software Development
Insurance fraud costs the industry billions annually, and most detection still happens post-payment through recovery rather than pre-payment through scoring -- because the claims workflow has no automated scoring step before settlement approval.
The adjuster reviewing a suspicious claim in isolation has no way to see that the same claimant, address, or repair shop appears across forty other claims in the portfolio. A fraud detection system surfaces those connections before the payment goes out, not after.
AI anomaly scoring on claims at intake
Network link analysis across claimants and third parties
Document authenticity verification
SIU referral and case management workflow
Insurance fraud detection software scores claims at intake using ML models trained on historical claims data, surfaces network links between claimants and third parties, verifies document authenticity, and routes high-scoring claims to SIU investigators through a structured referral and case management workflow. RaftLabs builds custom fraud detection systems for insurers and MGAs, integrated with existing claims management platforms, with delivery in 14-18 weeks at a fixed cost. Pre-payment fraud detection has a dramatically higher return on investment than post-payment recovery -- preventing a fraudulent settlement costs a fraction of what recovery costs when it succeeds at all.
AIAnomaly scoring
·Pre-paymentFraud detection
·FixedCost delivery
·14-18Week delivery cycles
Insurance fraud detection built to flag claims before settlement, not after
Preventing a fraudulent payment costs a fraction of recovering it. Recovery through civil litigation or insurer fraud units succeeds in a minority of cases, takes years, and consumes SIU and legal resource that could be intercepting fraud in the current portfolio. The return on investment from pre-payment detection is not marginal -- it is an order of magnitude better than post-payment recovery. The obstacle has been the workflow: adjusters reviewing claims individually do not have the population view required to spot patterns.
Rules-based detection systems -- the classic approach -- generate too many false positives to be useful at scale. When 30 percent of flagged claims turn out to be legitimate, adjusters start ignoring flags. ML scoring trained on confirmed fraud and legitimate claims from the insurer's own claims history identifies patterns that rules cannot express: the combination of claim timing, coverage age, incident description structure, and claimant behaviour that characterises fraud in a specific product line. The output is a score, not a binary flag, and the score is actionable because it is calibrated against real outcomes.
What we build
AI claim anomaly scoring
ML model trained on the insurer's historical claims data to score new claims at intake on fraud probability. Score features include claim frequency per policyholder, time since policy inception, incident-to-report lag, claim amount relative to premium paid, and coverage-specific anomaly signals identified during model training. The score is surfaced in the adjuster's claims workbench at the point of claim review -- not as a separate system requiring a separate login. High-scoring claims are flagged for enhanced scrutiny before settlement approval, with the score breakdown showing which features contributed most so the adjuster understands why the claim was flagged rather than just that it was.
Network link analysis
Entity relationship mapping across claims, claimants, witnesses, repair shops, medical providers, and solicitors. The network is built continuously as new claims are submitted, so a new claim involving a repair shop with a history of inflated estimates is immediately linked to that history. Network visualisation showing the connections between parties across multiple claims -- useful for SIU investigators building a picture of an organised fraud ring. Cluster detection for staged accident rings, crash-for-cash networks, and organised fraud groups operating across multiple policyholders. Cross-policy and cross-product linkage so a claimant active across motor, property, and liability products is identified as a connected entity rather than appearing as an unrelated individual in each product silo.
Document authenticity verification
Document analysis for submitted supporting materials: repair estimates, medical reports, invoices, police reports, and photographs. Metadata analysis for digital documents covering creation date, editing history, and software fingerprint -- a repair estimate created in a word processor the day after the claim was submitted but dated three weeks earlier is a detection signal. Image analysis for photo manipulation and inconsistencies -- duplicate images submitted with different date stamps, photos with edited metadata, or stock images submitted as original loss photographs. Third-party database cross-reference for repair shop and medical provider legitimacy against known provider registries and watchlists.
Rules engine and watchlist management
Configurable rules engine for deterministic fraud signals: blacklisted claimants, known fraud addresses, flagged vehicle VINs, and high-risk provider lists. Rules applied in combination with the ML score rather than as a replacement for it -- a claim that scores moderately on the model but hits a watchlist rule is treated differently than a moderate score with no rule trigger. Watchlist management with add, review, and expiry workflows so your fraud team maintains the lists without a software release. Rule performance reporting showing true positive rate, false positive rate, and claims value intercepted per rule -- so rules that generate noise without catching fraud are identified and refined.
SIU referral and case management
Automated SIU referral triggered when a claim score or rule flag exceeds a configurable threshold. The referral does not require adjuster action -- the case is opened in the SIU queue automatically with the claim data, anomaly score breakdown, network links, and document analysis findings assembled. SIU investigator workbench with case notes, evidence attachment, and investigation outcome recording. Outcome data fed back into model training so confirmed fraud cases improve the model's accuracy over time and confirmed legitimate cases reduce false positive rates. Referral-to-investigation conversion rate and investigation outcome reporting for SIU performance management and regulatory reporting.
Fraud reporting and model performance
Fraud detection dashboard showing claims reviewed, flags generated, SIU referrals, confirmed fraud rate, and fraud value intercepted over configurable time periods. Model performance metrics -- precision, recall, and area under the ROC curve -- tracked over time so model drift is visible before it affects detection rates. Fraud trend analysis by product line, geography, and claim type for portfolio-level fraud intelligence. Model recalibration cadence with new confirmed fraud and confirmed legitimate claim data fed back into training on a scheduled basis, keeping the model calibrated as fraud patterns in your portfolio evolve.
Frequently asked questions
Rules-based detection applies fixed criteria -- if a claim meets condition A and condition B, flag it. Rules are transparent and fast to implement, but they can only express patterns that a human has already identified and codified. Fraudsters adapt to known rules. ML scoring learns the combination of signals that distinguishes fraud from legitimate claims in your specific claims population, including signals that no individual underwriter or SIU investigator would think to write as a rule. The practical difference is that ML scoring maintains accuracy as fraud patterns evolve, while a rules set that is not continuously updated becomes less effective over time. We build both layers -- rules for known deterministic signals and ML scoring for pattern detection -- because the combination outperforms either approach alone.
The model trains on your historical claims data with confirmed outcomes -- claims confirmed as fraudulent and claims confirmed as legitimate. The minimum useful training set is typically two to three years of closed claims with outcome labels, covering enough confirmed fraud cases across your product lines for the model to learn meaningful patterns. We work with your data as it exists -- structured claims data from your claims management system and, where available, documents and adjuster notes. If your confirmed fraud labelling is incomplete, we work with your SIU team to establish a labelling process as part of the project scope. A model trained on your data will always outperform a generic industry fraud model because it learns the specific patterns present in your policyholder population and product mix.
False positives are managed through score thresholds and enhanced scrutiny workflows rather than claim holds. A claim that scores above the enhanced scrutiny threshold goes to a fast-track adjuster review queue -- not to an automatic payment hold. The adjuster reviews the score breakdown and decides within a defined SLA whether to proceed to settlement or refer to SIU. The threshold is calibrated during initial deployment to hit a false positive rate your claims team can handle without SLA impact, and it is adjusted as the model's precision improves. The goal is to surface risk to the adjuster, not to automate claim decisions -- that distinction keeps legitimate claimants moving through the workflow without unnecessary delay.
Yes. The fraud detection system integrates with your claims management system via API, receiving claim data at intake and writing scores and flags back to the claim record in real time. The adjuster sees the score in their existing workbench rather than logging into a separate system. We have built integrations with Guidewire ClaimCenter, Majesco Claims, and custom-built claims platforms. If your claims system has an API layer, we scope the integration during discovery. If it does not, we build a middleware layer that reads claim data from your database or file exports and writes flags back via the available channel. The integration approach is scoped specifically for your setup before development begins.
Talk to us about your insurance fraud detection project.
Tell us your product lines, current fraud loss rate, and where in the claims workflow you want detection to happen. We will scope a scoring and referral system built around your SIU process.
