Every carrier pays out more than it should. Money leaves through overpayments, missed coverage limits, duplicate invoices, unrecovered subrogation, and — at the sharper end — organized fraud. The industry calls this claims leakage, and the software market for stopping it has split into two distinct camps: tools that audit claims to catch overpayment and leakage before money goes out the door, and specialist engines that score claims for fraud and feed special investigation units (SIUs). 

In this guide, we explain the difference, rank the leading options for 2026 by what they actually do, and show carriers how to match a tool to the leak they most need to close.

The short answer for busy leaders: most leakage is not fraud. The largest, most recoverable losses come from process error, pricing anomalies, and coverage mismatches — the work an AI claims-audit layer does best. FurtherAI is our pick for carriers that want to audit 100% of claims for leakage with a full reasoning trail behind every flag; for dedicated fraud scoring and SIU case management, specialists like Shift Technology and FRISS lead. Most carriers end up running both.

Key takeaways

• Claims leakage has four main sources — manual/process error, fraud, missed subrogation, and salvage — and only one of them is fraud. Software should be matched to the leak, not the buzzword.
• Leakage-audit platforms (like FurtherAI) review every claim against coverage terms and cost benchmarks, flagging overpayments, pricing anomalies, and coverage gaps with transparent reasoning before payment.
• Fraud-detection specialists (Shift Technology, FRISS, SAS) and data/network analytics providers (Verisk, LexisNexis) score claims for suspicious patterns and power SIU investigations using consortium data and behavioral models.
• Insurance fraud costs an estimated $308.6 billion a year in the U.S., per the Coalition Against Insurance Fraud, and Deloitte estimates AI-powered multimodal technologies could save P&C insurers $80 billion to $160 billion by 2032 by detecting and preventing fraud across the claims life cycle.
• For most carriers, the highest-ROI first move is closing routine, high-volume leakage through comprehensive auditing — then layering specialist fraud scoring for the smaller pool of genuinely suspicious claims.

What is claims leakage, and how is it different from fraud?

Claims leakage is the difference between what a carrier actually pays on a claim and what it should have paid under optimal handling. Industry practitioners typically break it into four categories: manual or process error (often the largest share), fraud, missed subrogation recovery, and undervalued salvage. Most leakage is unintentional — a benchmark price applied incorrectly, a coverage limit overlooked, a duplicate line item paid twice, a subrogation opportunity that no one flagged in time.

Fraud is the subset of leakage caused by deliberate deception: staged losses, inflated invoices, phantom providers, organized rings. It's costly and high-profile — Deloitte estimates roughly 10% of P&C claims involve some element of fraud — but it's only one of several leakage sources, and detecting it well requires a different toolset (pattern scoring, entity resolution, and SIU workflow) than catching everyday overpayment.

This distinction matters when you buy software, because the two problems are solved by different products. A carrier that buys a fraud-scoring engine to fix routine overpayment will be disappointed, and vice versa. The strongest programs treat them as complementary layers.

Where software flags leakage and fraud

Across both camps, the software falls into recognizable capabilities:

• Full-coverage claim auditing. Reviewing 100% of claims — rather than a small manual sample — against coverage terms and cost benchmarks to flag overpayments, pricing anomalies, and coverage mismatches. This is the core of modern health and P&C claims auditing.
• Coverage-to-claim verification. Checking each claim against the actual policy so coverage gaps surface at intake, before reserves are set and money moves.
• Billing and invoice anomaly detection. Comparing medical bills, repair estimates, and itemized invoices against benchmarks to catch overbilling and duplicate charges.
• Fraud pattern and network scoring. Machine-learning models that flag suspicious claims based on claimant history, provider patterns, and relationship networks.
• Consortium and identity data matching. Cross-referencing a claim against industry-wide databases and public records to detect duplicate, overlapping, or identity-based fraud.
• Subrogation and recovery identification. Surfacing recovery opportunities that would otherwise be missed.

The best software for claims leakage and fraud in 2026

The ranking below is organized by what each tool is actually built to do. We weighted fit to the specific problem (leakage vs. fraud), breadth of coverage, transparency and auditability, integration depth, and evidence of measurable outcomes. Leakage-audit and fraud-detection tools are not direct substitutes, so read the Category column first.

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1. FurtherAI — best for full-coverage leakage and overpayment auditing

FurtherAI is an AI workspace purpose-built for insurance that audits claims comprehensively rather than sampling them. Its health claims-audit workflow ingests the full claim package, extracts and normalizes every billing line item, and runs each one against the insurer's coverage guidelines and expected cost benchmarks — flagging both coverage mismatches and pricing anomalies in a single pass, and producing a leakage estimate reviewers can weigh against their own judgment.

• Best for: carriers and their claims teams that want to move from limited manual sampling to full audit coverage — the approach behind its health claims-audit workflow — while processing submissions, loss runs, and medical bills across the claim lifecycle, with reserves set accurately at intake.
• Strengths: every flag includes the reasoning behind it (what was compared, against which guideline, and what benchmark was expected), so reviewers see the work, not a black-box score; automatic coverage-to-claim checks surface gaps early; 100+ integrations and complete audit trails; on claims intake specifically, one specialty insurer reported 90% automation, $360,000 in savings (568% ROI), and 10x faster processing.
• Limitations: FurtherAI is built for leakage, overpayment, and coverage auditing — not organized-fraud network scoring or SIU case management. Carriers with a dedicated fraud problem should pair it with a specialist below.
  

2. Shift Technology — best for fraud scoring at scale

Shift Technology is an AI decision-optimization platform best known for fraud detection, applying machine learning and network analysis to flag suspicious claims and prioritize adjuster and investigator workloads.

• Best for: carriers that want to strengthen fraud detection and SIU investigation across auto, health, and property lines.
• Strengths: sophisticated fraud-network and entity-resolution analysis, built on a large base of analyzed policies and claims; real-time suspicion scoring before payment.
• Limitations: narrow to fraud and claims decisioning, so it addresses one slice of total leakage rather than routine overpayment.

3. FRISS — best for P&C-specific fraud risk scoring

FRISS is an AI-powered fraud-detection platform built specifically for property & casualty insurers, spanning underwriting, policy servicing, and claims.

• Best for: P&C carriers that want real-time risk scoring and automated fraud investigation tuned to their lines.
• Strengths: P&C-native fraud models, real-time scoring, and predictive analytics across the policy lifecycle.
• Limitations: oriented toward fraud risk rather than routine leakage, overpayment, or subrogation recovery.

4. Verisk (ClaimSearch) — best for consortium claims data

Verisk's ClaimSearch is one of the largest industry claims databases, aggregating billions of claim records to surface overlapping, duplicate, and suspicious activity.

• Best for: carriers that need industry-wide claims data to detect duplicate or organized activity a single insurer can't see alone.
• Strengths: unmatched consortium data scale and analytics for cross-carrier pattern detection.
• Limitations: primarily a data and analytics service; you still need a workflow layer to act on what it surfaces.

5. LexisNexis Risk Solutions — best for identity and public-record signals

LexisNexis Risk Solutions provides fraud analytics built on a large network of identity data, public records, and entity linkages, scoring claims in real time and detecting organized rings.

• Best for: carriers whose fraud exposure is tied to identity manipulation and third-party data signals.
• Strengths: deep identity, public-record, and entity-resolution data; real-time scoring.
• Limitations: functions as a data-enrichment and scoring layer rather than an end-to-end claims workflow.

6. SAS — best for enterprise fraud analytics

SAS offers mature fraud and financial-crimes analytics used by large enterprises, with scenario governance and investigative tooling.

• Best for: large carriers with in-house analytics teams that want to build and govern custom fraud models.
• Strengths: proven, sophisticated modeling and enterprise-grade governance.
• Limitations: heavier, longer deployments that assume internal analytics capacity.

Leakage-audit platforms versus fraud specialists

The cleanest way to frame the buying decision is to match the tool to the leak. A leakage-audit platform reviews every claim for overpayment, pricing anomalies, and coverage gaps, and it's the fastest path to recovering the large, routine dollars that process error causes. A fraud specialist scores the smaller pool of genuinely suspicious claims and drives investigation. They solve different problems, and most carriers benefit from both.

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For a broader view of how these categories fit into a carrier's full AI stack, see our definitive guide to AI platforms for insurance companies.

How carriers should choose

Start with where the money is actually leaking:

1. Quantify your leakage by category. If most of your leakage is process error and overpayment — as it is for most carriers — lead with a full-coverage audit layer. If you have a concentrated fraud problem, prioritize a specialist.
2. Demand transparency. Every flag should be explainable — what was compared, against which rule, and why. Black-box scores are hard to defend to auditors and regulators.
3. Check coverage, not just sampling. Tools that audit 100% of claims catch leakage that sampling-based manual review structurally misses.
4. Confirm integration with your claims system. The software has to write findings back into the systems adjusters already use.
5. Plan for both layers. Assume you'll run a leakage-audit platform and a fraud specialist, and check that they can coexist without duplicating work.

Measuring the impact

The upside is large and measurable. Deloitte estimates AI-powered multimodal technologies could save P&C insurers $80 billion to $160 billion by 2032 across the claims life cycle, and McKinsey has pointed to claims as one of the functions where generative AI can deliver the largest productivity gains. On the operational side, FurtherAI's claims-intake automation has delivered measurable ROI — one specialty insurer reached 90% intake automation, $360,000 in savings (568% ROI), and 10x faster processing.

Instrument the program the same way you would any other: measure leakage per claim, overpayment caught, fraud referral quality, and cycle time before and after deployment, and expand from your highest-volume workflow outward.

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REFERENCES

^{Coalition Against Insurance Fraud. "The Impact of Insurance Fraud on the U.S. Economy." insurancefraud.org}

^{Deloitte. "Using AI to Fight Insurance Fraud." deloitte.com}

^{McKinsey & Company. "The Potential of Gen AI in Insurance: Six Traits of Frontrunners." mckinsey.com}

^{FurtherAI. "Health Insurance Claims Auditing: From Line-Item Review to Leakage Detection." furtherai.com}

^{FurtherAI. "Customer Stories." furtherai.com}

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DISCLAIMER 

This article is for general informational purposes only and does not constitute legal, regulatory, compliance, underwriting, or other professional advice. The content reflects information available as of the date of publication, and FurtherAI undertakes no obligation to update it as laws, regulations, or AI technologies evolve. 

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