P&C insurers to deploy AI-powered multimodal technologies to combat fraud: Deloitte

By implementing AI-driven technologies across the claims life cycle and integrating real-time analysis from multiple modalities, property and casualty (P&C) insurers could reduce fraudulent claims and save between $80 billion and $160 billion by 2032, according to Deloitte’s FSI Predictions 2025 report.

After tax evasion, insurance fraud remains the second-most costly white-collar crime in the U.S.

According to the Federal Bureau of Investigation, insurance fraud costs the average American family $400 to $700 annually due to increased premiums to cover the expense.

An estimated 10% of P&C insurance claims are fraudulent, resulting in an annual loss of $122 billion—accounting for 40% of the insurance industry’s total fraud losses.

One reason fraud is so widespread is that policyholders typically interact with insurers only when paying premiums or filing claims for injuries or property damage. This limited contact reduces insurers’ ability to continuously monitor policyholders’ activities, allowing fraudulent activities to go undetected.

Additionally, the COVID-19 pandemic accelerated digitisation, creating new opportunities for fraudsters while also driving innovative solutions to combat fraud.

The Coalition Against Insurance Fraud reports that 78% of U.S. consumers are concerned about insurance fraud, likely because they recognise that fraud doesn’t just affect insurers—the losses are passed on to policyholders through higher premiums.

Deloitte warns that continuing to raise premiums to offset fraud losses is unlikely to be a sustainable strategy for long-term profitability and market share growth.

Instead, insurers should adopt proactive measures to prevent fraud before it occurs, moving beyond traditional rules-based detection methods toward more advanced exposure and prevention techniques.

Deloitte believes that deploying AI-powered multimodal technologies to detect fraudulent behavior across the claims life cycle can significantly reduce this costly drain on consumers.

AI-powered multimodal technologies refer to advanced systems that leverage AI to process and integrate data from multiple modalities or sources—such as text, images, audio, video, and sensor data. By combining and analysing diverse data types, these technologies generate more comprehensive and accurate insights than single-modality systems.

Deloitte explained how AI can help detect and prevent fraud: “AI is equipping insurers with new fraud detection models that can free up human investigators to focus on more complex fraudulent cases across the claims life cycle. By combining AI-driven anti-fraud technologies with advanced data analytics (depending on the law of each jurisdiction), insurers can enhance their capabilities to detect and prevent fraud. This can be beneficial in the property claims and personal auto insurance segments due to their complexity and sheer volume of data, need for real-time processing, and potential for significant cost savings and efficiency improvements.

“Multiple techniques such as automated business rules, embedded AI and machine learning methods, text mining, anomaly detection, and network link analysis could score millions of claims in real time. Combining data from various modalities, such as text, images, audio, and video, could help identify patterns and anomalies and enhance the investigative process by reducing false positives, increasing detection rates of fraudulent claims, and saving on costs associated with fraud investigations. Such techniques must, however, be deployed with effective human oversight and in alignment with the laws of each jurisdiction.”

Deloitte noted that future success depends on combining advanced AI with human expertise. Attracting and retaining skilled talent, along with continued investment in automation, will be important for companies aiming to achieve their long-term anti-fraud goals.

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