NOT BUILT — PHASE 4

AI Claims Fraud Detection That Finds What Adjusters Miss

Q: How does AI detect insurance claims fraud?

AI claims fraud detection works by analyzing claims data, policy information, historical patterns, and external signals using machine learning models. Clara Adjusta by FluxForce scores every claim for fraud probability at intake, cross-references against known fraud patterns, and identifies duplicate and staged claims automatically — achieving a 90%+ true positive rate on SIU referrals.

Q: What is claims leakage and how does AI prevent it?

Claims leakage refers to overpayments, missed fraud, and processing inefficiencies that erode insurer profitability. According to the Coalition Against Insurance Fraud, insurance fraud costs the US industry over $308 billion annually. AI prevents claims leakage by catching fraud earlier, automating triage, and identifying overpayment patterns. Clara Adjusta quantifies leakage savings per quarter with detailed audit trails.

Q: Can AI improve SIU referral accuracy?

Yes. Traditional SIU referral processes produce high false positive rates, overwhelming investigation teams with non-fraudulent claims. AI improves SIU referral accuracy by applying multi-signal fraud scoring that evaluates claims against behavioral patterns, historical data, and external intelligence. Clara Adjusta achieves a 90%+ true positive rate on SIU referrals, ensuring investigators focus on genuine fraud.

Q: How much can AI reduce claims processing time?

AI-powered claims fraud detection reduces per-claim processing time by 45% by automating triage, fraud scoring, and documentation review. Manual claims triage typically takes 30-60 minutes per claim. Clara Adjusta scores claims at intake, routes low-risk claims for fast-track processing, and only escalates high-probability fraud to human investigators — freeing capacity across the claims operation.

Q: Can AI detect staged and duplicate insurance claims?

Yes. AI detects staged and duplicate insurance claims by analyzing entity relationships, claim timing patterns, provider networks, and documentation inconsistencies across the entire claims portfolio. Clara Adjusta cross-references every incoming claim against historical claims data to identify duplicates, near-duplicates, and coordinated staging patterns that human reviewers miss at volume.

Q: What regulations apply to AI claims fraud detection in insurance?

AI claims fraud detection is subject to insurance regulations including NAIC model laws in the US, Solvency II in the EU, FCA requirements in the UK, and the EU AI Act's transparency requirements for automated decision-making. Clara Adjusta maps every fraud scoring decision to the applicable regulatory framework with audit-ready documentation for compliance teams and regulators.

Q: Is AI claims fraud detection fully autonomous or human-in-the-loop?

AI claims fraud detection uses configurable autonomy. Low-risk claims with clear patterns are triaged autonomously for fast-track processing. Medium-risk claims default to human-in-the-loop review but can be configured for autonomous routing. High-risk claims — including suspected staged events and large-value fraud — always require human SIU investigation. The insurer configures exactly where the threshold sits per claim type and coverage line.

Clara Adjusta — Senior AI Claims Intelligence Officer

Your claims team processes thousands of claims monthly. Fraud hides in
plain sight — duplicate filings, staged events, inflated damages. Manual
triage catches some. Most slips through. Clara Adjusta scores every claim
at intake — reducing per-claim processing by 45% with a 90%+ SIU
referral true positive rate. Claims leakage savings quantified every
quarter.

Request Early Access

Download Underwriting Brief

High

Claims Fraud Accuracy

45%

Per-Claim Processing Reduction

90%+

SIU Referral True Positive

Real

Duplicate/Staged Catch Rate

Quarterly

Leakage Savings Report

THE PROBLEM

The Problem Your Claims Team Faces Every Day

Your claims adjusters open their queues each morning to hundreds of new filings. Each claim needs triage — is it legitimate, suspicious, or fraud? According to the Coalition Against Insurance Fraud, insurance fraud costs the US industry over $308 billion annually. Most of it is never detected.

Meanwhile, your SIU team is overwhelmed with low-quality referrals.

Claims fraud undetected

Staged accidents, inflated damages, and phantom claims pass through manual review. According to the NICB , insurance fraud adds $400-$700 per year to the average family's premiums. Most fraud is detected only after payout — if at all.

Manual triage bottlenecks

Adjusters spend 30-60 minutes per claim on initial triage. At scale, this creates backlogs that delay legitimate claims and increase customer dissatisfaction. The triage process itself becomes a source of leakage.

Claims leakage

Overpayments, missed subrogation opportunities, and undetected fraud compound to erode profitability by 2-5% of total claims costs. According to McKinsey, AI-powered claims management can reduce leakage by 20-30% within 18 months of deployment.

JOB DESCRIPTION

What Clara Adjusta Does — Job Description

Clara Adjusta is a Senior AI Claims Intelligence Officer that operates inside your claims pipeline as a dedicated fraud detection and triage specialist.

CLARA ADJUSTA

Senior AI Claims Intelligence Officer | FF-CFD

Not Ready

Reports To

Your Head of Claims / CRO

Works With

Existing claims management system ,policy admin,
and SIU workflows

Deployed In

30 days (shadow mode first)

KEY RESPONSIBILITIES

Score every claim for fraud probability at intake using ML and pattern analysis

Reduce per-claim processing time by 45% through automated triage and routing

Achieve 90%+ true positive rate on SIU referrals — investigators focus on real fraud

Detect duplicate claims, staged events, and inflated damages across the portfolio

Quantify claims leakage savings per quarter with detailed evidence and audit trails

AUTONOMY MODEL

Low risk — Acts autonomously (approve, clear)

Medium risk — HITL by default (configurable)

High risk — ALWAYS human review (non-negotiable)

You configure the threshold per rule

Kill switch : Disable instantly

High

Claims Fraud Detection Accuracy

precision fraud scoring

45%

Per Claim Processing Reduction

reduction in processing time

90%+

Siu Referral True Positive Rate

of referrals are real fraud

High

Duplicate/Staged Claim Catch Rate

cross-portfolio detection

Quantified

Claims Leakage Savings

per quarter with evidence

<5 minutes

Triage Speed

per claim at intake

Auto-generated

Evidence Packing

SIU packages

100%

Audit Trail Coverage

every decision logged

HOW IT WORKS

How AI Claims Fraud Detection Works with Clara Adjusta

Clara Adjusta connects to your existing claims management system as a sidecar — no data migration, no core system changes. Here is how every claim flows:

Ingest

Claims data from your CMS feeds into Clara Adjusta via API. Data
includes: claim details, policy information, coverage terms, historical claims for the policyholder, and supporting documentation (medical records, repair estimates, photos, police reports).

Score

Every claim is scored for fraud probability at intake. Clara Adjusta
applies ML models to detect duplicate filings, staged events, inflated damages, and suspicious provider patterns. Entity resolution links claimants, providers, and witnesses across the full claims history.

Triage

Based on the fraud score, Clara Adjusta routes each claim:
• Low risk → Fast-tracked for standard processing
• Medium risk → Flagged for adjuster review (configurable)
• High risk → Escalated to SIU with complete evidence package (always)

Your team configures the threshold per claim type, per coverage line,
per geography.

Report

Every decision produces:
• A fraud probability score with contributing indicators
• Entity relationship mapping showing linked claims and parties
• Supporting evidence packaged for SIU investigation
• Quarterly leakage reports m quantifying savings
• Immutable audit trail for regulatory compliance.

NAIC Model Laws

State insurance fraud reporting requirements

Solvency II

EU regulatory framework for insurer risk management

FCA Insurance Conduct

UK claims handling and fraud requirements

GDPR

Data handling for policyholder and claimant data

IFPA Guidelines

Insurance fraud prevention best practices

EU AI Act

Explainable AI for automated claims decisioning

ROI — AI CLAIMS FRAUD DETECTION vs HIRING vs LEGACY TOOLS

AI Claims Fraud Detection Cost Comparison — 2026

How does Clara Adjusta compare to hiring SIU investigators or using
legacy fraud detection rules?

Criteria	Hire 3 SIU Investigators	Legacy Rules Engine	Clara Adjusta
Annual cost	$360K-$750K (salary + benefits)	$150K-$400K (license + maintenance)	TBD (Phase 4)
Deployment time	3-6 months (recruit + train)	6-12 months (implementation)	30 days
SIU referral accuracy	Varies (experience dependent)	30-50% true positive	90%
Per-claim processing	30-60 minutes manual	Batch scoring, limited	45% reduction
Duplicate/staged detection	Quarterly manual review	Batch reporting	Continuous, 30+ day warning
Leakage quantification	Estimated annually	Not available	Quarterly, evidence-based
Scales with volume	Hire more ($$)	License upgrades ($$)	Auto-scales
Available 24/7	No (shifts needed)	Yes	Yes
Learns from data	Yes (slowly)	No	Yes (continuous)

Key insight:According to the Coalition Against Insurance Fraud, insurance fraud costs the US industry over $308 billion annually. Even a 1% improvement in fraud detection across a mid-size insurer's claims book can save millions per year. Clara Adjusta targets the three biggest leakage drivers: undetected fraud, triage inefficiency, and SIU false positives.

Aiden Flux

Senior AI Fraud Risk Analyst

Enriches claims fraud scoring with real-time transaction fraud intelligence from across the institution

Learn now

Oscar Gray

Senior AI OSINT Intelligence Director

Adds external intelligence — breach databases, dark web signals , and public records — to claims fraud claims fraud

Learn now

Zara Trustwell

Director AI Regulatory Compliance

Maps every claims decision to insurance regulatory frameworks and ensures audit readiness.

Learn now

TRUST BUILDERS

Built for Regulated Insurance Companies

Configurable Autonomy

Low risk: Clara acts autonomously (fast-track routing). Medium risk: HITL by default (configurable). High risk: Always human SIU review. You set the threshold per claim type, per coverage line, per geography.

Kill Switch

Disable Clara Adjusta instantly. No system impact. No downtime.One click.

Shadow Mode

Run Clara Adjusta on your live claims for 30 days. Observation only — no routing, no action. Validate accuracy before going live.

Explainability

Every fraud score includes plain-English reasoning with contributing indicators and confidence levels. Your compliance team and regulators can read why each claim was fast-tracked, flagged, or escalated.

Audit Trail

Every decision logged with immutable, tamper-evident evidence chain. Regulation → rule → evidence → action → outcome.

No Migration

Sidecar integration. Clara Adjusta reads your existing claims feed and policy data. Your core systems stay untouched.

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Questions? We Have Answers

Frequently Asked
Questions

AI claims fraud detection works by analyzing claims data, policy information, historical patterns, and external signals using machine learning models. Clara Adjusta by FluxForce scores every claim for fraud probability at intake, cross-references against known fraud patterns, and identifies duplicate and staged claims automatically — achieving a 90%+ true positive rate on SIU referrals.

Claims leakage refers to overpayments, missed fraud, and processing inefficiencies that erode insurer profitability. According to the Coalition Against Insurance Fraud, insurance fraud costs the US industry over $308 billion annually. AI prevents claims leakage by catching fraud earlier, automating triage, and identifying overpayment patterns. Clara Adjusta quantifies leakage savings per quarter with detailed audit trails.

Yes. Traditional SIU referral processes produce high false positive rates, overwhelming investigation teams with non-fraudulent claims. AI improves SIU referral accuracy by applying multi-signal fraud scoring that evaluates claims against behavioral patterns, historical data, and external intelligence. Clara Adjusta achieves a 90%+ true positive rate on SIU referrals, ensuring investigators focus on genuine fraud.

AI-powered claims fraud detection reduces per-claim processing time by 45% by automating triage, fraud scoring, and documentation review. Manual claims triage typically takes 30-60 minutes per claim. Clara Adjusta scores claims at intake, routes low-risk claims for fast-track processing, and only escalates high-probability fraud to human investigators — freeing capacity across the claims operation.

Yes. AI detects staged and duplicate insurance claims by analyzing entity relationships, claim timing patterns, provider networks, and documentation inconsistencies across the entire claims portfolio. Clara Adjusta cross-references every incoming claim against historical claims data to identify duplicates, near-duplicates, and coordinated staging patterns that human reviewers miss at volume.

AI claims fraud detection is subject to insurance regulations including NAIC model laws in the US, Solvency II in the EU, FCA requirements in the UK, and the EU AI Act's transparency requirements for automated decision-making. Clara Adjusta maps every fraud scoring decision to the applicable regulatory framework with audit-ready documentation for compliance teams and regulators.

AI claims fraud detection uses configurable autonomy. Low-risk claims with clear patterns are triaged autonomously for fast-track processing. Medium-risk claims default to human-in-the-loop review but can be configured for autonomous routing. High-risk claims — including suspected staged events and large-value fraud — always require human SIU investigation. The insurer configures exactly where the threshold sits per claim type and coverage line.

AI Claims Fraud Detection That Finds What Adjusters Miss

Clara Adjusta

High

45%

90%+

Real

Quarterly

The Problem Your Claims Team Faces Every Day

Claims fraud undetected

Manual triage bottlenecks

Claims leakage

What Clara Adjusta Does — Job Description

CLARA ADJUSTA

Target Performance — Design Specifications

How AI Claims Fraud Detection Works with Clara Adjusta

Ingest

Score

Triage

Report

Want to See This on Your Claims Data?

Regulatory Frameworks Supported

NAIC Model Laws

Solvency II

FCA Insurance Conduct

GDPR

IFPA Guidelines

EU AI Act

What Your Claims Adjuster Sees

AI Claims Fraud Detection Cost Comparison — 2026

Agents That Work Best with AI Claims Fraud Detection

Aiden Flux

Oscar Gray

Zara Trustwell

Built for Regulated Insurance Companies

Configurable Autonomy

Kill Switch

Shadow Mode

Explainability

Audit Trail

No Migration

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Frequently Asked Questions

Insights on AI Security,Compliance
& Financial Automation

Frequently Asked
Questions