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

Recent outcomes

Voice AI · Research

6× deeper insights

Text-based interviews converted to automated phone calls

AI Automation · Ops

20k+ txns day one

Manual invoice OCR across 40+ gas stations

Loyalty · Retail

1,062 users in 4 weeks

SuperValu & Centra loyalty platform with receipt validation

SaaS · Logistics

2,000+ shipments yr 1

Multi-carrier shipping hub for Indonesian eCommerce

4.9

on Clutch

See our work

The problem

Sound familiar?

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?

Short answer

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 platforms, delivered in 14-18 weeks at a fixed cost. Pre-payment fraud detection returns an order of magnitude more than post-payment recovery.

Key takeaways

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.

Trusted by

Fraud detection delivery, by the numbers

anomaly scoring: AI

fraud detection: Pre-payment

cost delivery: Fixed

week delivery cycles: 14-18

Built to flag claims before settlement, not after

Rules-based detection generates too many false positives to be useful at scale. ML scoring trained on confirmed fraud and legitimate claims from your own claims history identifies patterns rules cannot express, and the output is a score, not a binary flag, calibrated against real outcomes.

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 and SIU training
Adjusters and SIU investigators trained on the score breakdown and referral workflow before full rollout.

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
9 years and 100+ products shipped
Clients include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. Track record building fraud and risk-scoring platforms.
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.

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.

Talk about your fraud detection project

Related services

Insurance Fraud Detection Software Development, scoped in one call.

Tell us what's broken. Within one business day you get a straight take on cost, timeline, and the right first step. No deck, no pressure.

Stay on topic

More on fintech

Generative AI in Finance

Cost to Build a Cross-Border Payment App Like Wise

Money transfer app development costs $35K-$70K for an MVP covering 2-3 corridors and takes 16-20 weeks. This guide covers cost tables, phased feature sets, why clone scripts fail, and when custom beats off-the-shelf for fintech companies building cross-border payment platforms.

Funeral Home Software Development Company

Custom funeral software for case and arrangement management, online arrangement platforms for direct cremation operators, memorial and obituary websites, family communication portals, preneed and at-need planning tools, and funeral billing and payment plans.

: 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

Tell us what you need. We'll tell you what it would take.

We scope Insurance Fraud Detection Software Development in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.

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

Insurance software by area

01
Insurance industry hub
RaftLabs track record building software for insurers, MGAs, and brokers.

Built to flag claims before settlement, not after

Sound familiar?

Fraud detection delivery, by the numbers

Built to flag claims before settlement, not after

What we build

From scope to live fraud scoring

Fraud loss and workflow scoping

Model and integration design

Build and integrate

Launch and SIU training

Why insurers and MGAs choose RaftLabs

Have an insurance fraud detection project?

Related services

Insurance Fraud Detection Software Development, scoped in one call.

More on fintech

Frequently asked questions

Tell us what you need. We'll tell you what it would take.

Insurance software by area