By Lewis Nibbelin, Research Writer, Triple-I
At least 10 percent of property/casualty insurance claims may be fraudulent, adding up to billions of dollars in fraudulent insurance claims every year, the National Insurance Crime Bureau estimates. While legislative reforms are necessary to combat fraud and legal system abuse, many insurers are turning to artificial intelligence and machine learning models to help mitigate the risks in the near term.
Often trained on years of data, AI-powered tools can flag suspicious claims or those likely to litigate based on early risk indicators, such as attorneys or firms frequently linked to inflated claims. Some systems leverage litigation propensity scoring to predict a claim’s likelihood to escalate from the first notice of loss, providing real-time risk ratings throughout the claim cycle that better enable adjustors to prioritize high-risk claims.
By synthesizing historical data and automating the review process, such systems can give insurers the chance to intervene or settle before claims escalate. Research indicates these early-warning models can identify potentially fraudulent claims within two weeks after submission, far outpacing traditional detection methods that involve manually sifting through large, complex volumes of data.
Delivering measurable outcomes
Early intervention can facilitate fairer settlement outcomes and protect insurers and policyholders from unnecessary legal costs that keep upward pressure on premium rates for all consumers. Deloitte analysis suggests applying AI across the claims cycle could save insurers between $80 billion and $160 billion by 2032 through fraudulent claim reduction, translating to billions in savings for their insureds.
Data libraries that pool litigation pattern and claims data from insurers and companies from other industries can also improve AI model insights. Rather than leaving organizations to rely exclusively on their own internal data, these cross-industry approaches can expand base datasets and prediction accuracy, allowing insurers to keep pace with emerging risks.
To grasp insurance executive readiness for AI adoption, Deloitte conducted a separate 2025 survey that found those who reported successful AI initiatives cited “close collaboration across business, tech, data, and talent functions” as the greatest contributing factor. Among all respondents, 35 percent ranked fraud detection as one of their top five areas for implementing generative AI.
It’s no wonder why: As tools to mitigate insurance fraud have evolved, so too have the tools available to bad actors aiming to defraud the claims process. Plaintiffs’ attorneys themselves are seizing on the opportunity, with research from Suite 200 Solutions indicating “almost all litigation financing funds now use AI to identify cases likely to win,” down to “case type, venue, judge, plaintiff attorney, and other factors.”
Tactics to mislead consumers into escalating claims are also increasingly AI-driven, including automated “robocalls” and text messages that solicit receivers to file lawsuits. Another study from the National Insurance Crime Bureau and 4WARN observed that third-party litigation funders (TPLF) are using AI-generated content to scale volume and prolong settlements, as part of a larger digital marketing campaign that attracts 27.8 million clicks to TPLF-hosted websites every month.
Traditional claims review methods fail to capture these modern digital risks, necessitating AI-powered detection and mitigation to stay ahead of new threats.
Industry collaboration is key
Yet, as companies scale their AI investments, human oversight must remain at the forefront, as should maintaining a traceable actuarial record behind every model. Beyond safeguarding model accuracy, AI data understanding and preparation are crucial to ensuring carriers comply with insurance regulations and can uphold consumer trust. Attracting talent that balances actuarial knowledge with AI expertise will be pivotal to successful model deployment.
To address these challenges, Triple-I and The Institutes RiskStream Collaborative – like Triple-I, an affiliate of The Institutes – recently established two coordinating councils to develop shared AI capabilities and research and governance standards across the insurance sector.
Led by RiskStream, the AI Solutions Council brings together insurers, tech firms, and other stakeholders to prioritize multiparty AI use cases and generate AI solutions across the insurance value chain. Alongside Triple-I’s AI Policy Council, which focuses on regulatory and governance frameworks for AI use in insurance, these bodies give insurers a structured way to collaborate on AI solutions and best practices rather than leaving each carrier to build capabilities in isolation.
Learn More:
Cyber Claim Severity Surges as AI, Litigation Accelerate Risk
Legal System Abuse Awareness Campaign Spreads Across U.S.
Legal System Abuse, Artificial Intelligence Cloud 2026 Outlook
Tech — Especially A.I. — Is Top of Mind for Global Insurance Executives
JIF 2025: Litigation Trends, Artificial Intelligence Take Center Stage
How Insurers Address Talent Gap Through Innovation & Technology