How AI Can Help Insurers Detect and Cut Down on Fraud

Insurance fraud remains one of the most expensive white-collar crimes in the United States, with roughly 10% of claims deemed fraudulent. While generative AI has introduced new risks, such as synthetic identities and fabricated evidence, it’s also proving to be a game-changer in fraud detection. A recent Deloitte report suggests that property and casualty (P&C) insurers could save between $80 billion and $160 billion annually by deploying AI-driven technologies to fight fraud more effectively.

AI allows insurers to process and evaluate far more data than humans ever could—quickly and accurately. Technologies such as text analytics, image and video analysis, and natural language processing are being used to flag inconsistencies in claims. Other tools like geospatial analysis, IoT data from telematics and smart home sensors, and even simulation modeling of service providers are giving insurers a broader, deeper view into suspicious patterns and potential fraud rings.

Deloitte emphasizes a multifaceted approach: combining data types and detection tools across various channels and claims stages. Soft fraud—exaggerations of legitimate claims—accounts for 60% of all fraud and is much harder to detect than hard fraud, which involves completely fabricated losses. Even so, AI promises significant improvement in both categories, especially when paired with ongoing investment.

While implementation won’t be cheap—Deloitte estimates insurers will spend around $32 billion on fraud tech by 2032—the savings potential is substantial. Beyond cost reductions, cracking down on fraud could also boost consumer trust at a time when rising premiums and inflation are straining customer loyalty. For insurers, the message is clear: invest in AI, reduce fraud, and improve policyholder satisfaction.