How does AI fraud scoring differ from rules-based fraud detection?

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.

What data does the model need to train on?

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.

How do you handle false positives that delay legitimate claims?

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.

Can this integrate with our existing claims management system?

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.

Insurance Fraud Detection Software

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 resource that could be intercepting fraud in the current portfolio. 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. 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, satisfying model governance and regulatory explainability requirements.
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

Since 2015
production software shipped across fintech, healthcare, and logistics: RaftLabs delivery record

4.9/5
average client rating across delivered projects: Clutch, verified reviews

Fixed price
scope and cost agreed in writing before any development starts: 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 models train on confirmed fraud and legitimate claims from your own history. The output is a calibrated score, not a binary flag.

Capabilities

What we build

01
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.
Built with
SHAP
02
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.
03
Document authenticity verification
Metadata and image analysis flagging manipulated photos and inconsistent repair estimates, cross-referenced against provider watchlists.
04
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.
05
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.
06
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

Week 1
01
Fraud loss and workflow scoping
We map your current fraud loss rate and where in the claims workflow detection should happen. You leave week 1 with a written scope document and a fixed-price quote.
Weeks 2-6
02
Model and integration design
Training data preparation, scoring architecture, and claims system integration designed against your actual claims history.
Weeks 7-15
03
Build and integrate
Scoring model, network analysis, and SIU referral workflow built in parallel, validated against confirmed outcomes.
Final 2-3 weeks
04
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.

Why us

Why insurers and MGAs choose RaftLabs

01
Senior engineers build what they scope
The engineers who assess your claims data also build the solution. No bait-and-switch, no offshore handoff 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 PCI DSS-audited fintech and HIPAA-compliant 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.

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: 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.
: 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.

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.

Go deeper

Browse our case studies

Built to flag claims before settlement, not after

Start with what is not working.

Insurance fraud detection software built on your own claims history

What we build

From scope to live fraud scoring

Fraud loss and workflow scoping

Model and integration design

Build and integrate

Launch v1 and SIU training

Built for insurance model governance from day one

Why insurers and MGAs choose RaftLabs

More on fintech

Frequently asked questions

Have an insurance fraud detection project?