Insurance Fraud Detection Services
Built to flag claims before settlement, not after
Preventing a fraudulent payment costs far less than recovering it. The adjuster reviewing a suspicious claim in isolation has no way to see that the same claimant, address, or repair shop appears across many other claims in the portfolio. ML scoring trained on your own confirmed fraud and legitimate claims identifies patterns rules cannot express, and 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
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
Trusted by
The brief
Start with what is not working.
Good software decisions begin with the constraint, not a list of features or a preferred technology.
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?
Plain answer
Insurance fraud detection software scores each claim at intake using machine learning trained on an insurer's own confirmed fraud and legitimate claims, links claimants and providers across the portfolio, verifies document authenticity, and routes high-risk claims to SIU investigators. RaftLabs builds custom systems for insurers and MGAs that integrate with existing claims platforms, launching a validated v1 first, then iterating.
What to remember
- SHAP value feature-contribution breakdowns give adjusters an interpretable explanation for every flag, supporting model governance and regulatory explainability.
- Network link analysis builds a continuously updated entity graph across claimants, repair shops, and medical providers, surfacing organised fraud rings a single-claim review would miss.
- False positives are managed through enhanced-scrutiny review queues, not automatic payment holds, keeping legitimate claimants moving through the workflow without delay.
- The minimum useful training set is 2-3 years of closed claims with confirmed fraud/legitimate outcome labels across your product lines.
Proof
- production software shipped across fintech, healthcare, and logistics
- Since 2015
- RaftLabs delivery record
- scope and cost agreed in writing before any development starts
- Fixed price
- Every RaftLabs engagement
Insurance fraud detection software built on your own claims history
Insurance fraud costs US consumers an estimated $308.6 billion a year (Coalition Against Insurance Fraud, 2022). Most of it leaves the building as paid claims before anyone reviews the file. Rules-based detection throws too many false positives to work at scale. Our insurance fraud detection software scores each claim at intake. The machine learning models train on confirmed fraud and legitimate claims from your own history. The output is a calibrated score, not a binary flag.
The first decision is not what to build.
It is whether software is the right answer at all. We use four possible recommendations before discussing a development scope.
Buy
Run the build-vs-buy numbersChoose a maintained product when it handles the core workflow and the remaining gaps can be solved with configuration, a connector, or a small process change. Paying for software you do not have to maintain is often the better trade.
Repair
Keep the existing product when users still get value from it and the code can be understood safely. Start with evidence: where the code lives, how the data is organized, what is tested, which outside services it depends on, who can access what, and how updates go live.
Build
Own the system when the workflow shapes margin, customer experience, speed, or a product customers pay for. Start with one complete path and the system connections that path cannot work without.
Wait
Do not turn a changing policy into permanent software. Stabilise the rules, name the workflow owner, and measure the cost of the current process before paying for a product.
Not sure which path is yours? Bring it to a 30-minute call. Leave with the answer in writing.
Capabilities
What we build
AI claim anomaly scoring
ML scoring at intake on claim frequency, incident-to-report lag, and coverage-specific anomaly signals, with SHAP feature-contribution breakdowns for adjuster explainability.
Network link analysis
Continuously updated entity graph across claimants, repair shops, and medical providers, with cluster detection for staged-accident and crash-for-cash rings.
Document authenticity verification
Metadata and image analysis flagging manipulated photos and inconsistent repair estimates, cross-referenced against provider watchlists.
Rules engine and watchlist management
Deterministic rules for blacklisted claimants and flagged VINs applied alongside the ML score, with rule performance reporting to refine noisy rules.
SIU referral and case management
Automated referral above a configurable threshold with case data pre-assembled, plus outcome feedback that improves model accuracy over time.
Fraud reporting and model performance
Fraud value intercepted, precision/recall/AUC tracked over time to catch model drift, and scheduled recalibration as fraud patterns evolve.
How we work
From scope to live fraud scoring
- Phase 101
Fraud loss and workflow scoping
We map your current fraud loss rate and where in the claims workflow detection should happen. You leave scoping with a written scope document and a fixed-price quote.
- Phase 202
Model and integration design
Training data preparation, scoring architecture, and claims system integration designed against your actual claims history.
- Phase 303
Build and integrate
Scoring model, network analysis, and SIU referral workflow built in parallel, validated against confirmed outcomes.
- Phase 404
Launch v1 and SIU training
A validated v1 goes live on a defined set of product lines. Adjusters and SIU investigators train on the score breakdown and referral workflow. The model then keeps improving as confirmed outcomes feed back in.
Built for insurance model governance from day one
Insurance is not an unregulated ML playground. A model that touches claims decisions has to survive regulatory review for unfair discrimination and stay explainable to a regulator, not just accurate.
The NAIC Model Bulletin on the Use of Artificial Intelligence Systems by Insurers (2023) expects a documented AI governance program covering data, testing, and oversight.
Colorado SB21-169 and NYDFS Circular Letter No. 7 (2024) require insurers to show their algorithms and external data do not produce unfair discrimination against protected classes.
Every flag carries a SHAP feature-contribution breakdown, so an adjuster, an SIU investigator, and a market-conduct examiner all see why a claim scored the way it did. We document the training data, the validation approach, and the recalibration schedule as part of delivery. Scoring can integrate with ISO ClaimSearch and NICB referrals where you already rely on them.
Proof
What insurers and MGAs evaluate
- 01
The team that scopes it builds it
The engineers who assess your claims data also build the solution. No bait-and-switch after the contract is signed.
- 02
Fixed price before development starts
We scope the work, calculate the cost, and lock it in writing before any development starts.
- 03
Shipping production software since 2015
Real engagements across SaaS, fintech, healthcare, and logistics, not every one published as a named case study. We build data-intensive platforms for regulated industries, including fintech and healthcare.
- 04
Models trained on your data, not a generic industry model
Your specific policyholder population and product mix produce better accuracy than an off-the-shelf model.
- 05
Explainable scores, not a black box
SHAP breakdowns satisfy internal model governance and regulatory explainability requirements.
What it costs
Every project starts at $10000.
The first phase covers one fraud-detection path, like claim anomaly scoring, with rules and case management wired before expansion.
Starting investment
Starts at $10000
Start with the claim type where fraudulent payments cost you the most. After a 30-minute call, you get a written build, buy, repair, or wait recommendation plus first-phase direction. Free, whether or not you build with us.
Work with us
Have an 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.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.
Common questions
Rules-based detection applies fixed criteria and can only express patterns a human has already identified and codified. ML scoring learns the combination of signals that distinguishes fraud from legitimate claims in your specific claims population, including signals no individual investigator would think to write as a rule. We build both layers 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.
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 threshold is calibrated during initial deployment and adjusted as the model's precision improves.
Start with one lane, prove it, then scale: not ten parallel pilots. Prioritize fraud detection and claims automation first; generative AI for underwriting follows, needing more data preparation and compliance review. Focus on the shortest feedback loops first.
Buying a point fraud solution without fixing the underlying data pipeline is the most common failure: the model gets fed inconsistent, incomplete data and underperforms regardless. Treating analytics as a one-time IT project is another: model accuracy degrades as fraud tactics and claim behaviors evolve, so retraining must be ongoing. Analytics is an ongoing discipline, not a one-time project. And adjusters route around the system when nobody explained how model recommendations fit existing judgment: transparent scoring wins trust; black-box instructions lose it.
Track precision (of flagged claims, how many were worth flagging), recall (of fraud, how much was caught), and ranking quality (the most suspicious claims rise to the top). Accuracy is the least useful metric since fraud is rare. Track straight-through processing rate as a proxy for labor cost removed, plus claims cycle time (first notice to settlement), loss ratio improvement, fraud detection rate and false positive rate together, and adjuster productivity.
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. We have built integrations with Guidewire ClaimCenter, Majesco Claims, and custom-built claims platforms.
Go deeper