Property and casualty carriers can win the fight against insurance fraud

Deloitte predicts that, by implementing AI-driven technologies across the claims life cycle and integrating real-time analysis from multiple modalities, P&C insurers could reduce fraudulent claims and save between US$80 billion and US$160 billion by 2032 (see “About this prediction”).

AI-fueled multimodal technologies refer to advanced systems that leverage AI to process and integrate data from multiple modalities or sources. These modalities can include text, images, audio, video, and sensor data, among others. By combining and analyzing diverse types of data, these technologies can generate more comprehensive and accurate insights than single-modality systems.

Why is P&C insurance fraud detection so challenging?

An estimated 10% of P&C insurance claims are fraudulent, resulting in a US$122 billion loss annually, or 40% of the total fraud losses of the insurance industry.⁶ One of the reasons why fraud is so prevalent is because typically, policyholders only interact with their insurance providers when paying premiums annually or when they need to file claims for injuries or property damage. This infrequent interaction can limit insurers’ ability to continuously oversee policyholders’ activities, which can allow fraudulent activities to go undetected.

Fraud is typically segmented into soft and hard incidents. Soft fraud involves inflating a legitimate claim. For example, a policyholder could overstate repair costs or exaggerate an injury. Hard fraud is when premeditated actions are taken to create a false claim—for instance, if a policyholder stages an accident, commits arson, fakes a theft, or utilizes the same photograph to make claims across multiple insurance companies. Soft fraud is more common, likely because it’s hard to prove; it accounts for 60% of all incidents.⁷

Mounting pressure propels demand for advanced detection tools

The onset of the COVID-19 pandemic accelerated digitization, creating new opportunities for fraudsters as well as a flurry of innovative solutions to combat fraud.⁸ Fraud-detection technology has become a rapidly growing industry, estimated to multiply eight times, from US$4 billion in 2023 to US$32 billion by 2032.⁹ At the same time, pressure from regulatory bodies like the National Association of Insurance Commissioners is pushing insurers to implement effective and advanced fraud detection systems.¹⁰

How can AI 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.¹¹

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. Here are some areas where AI can be used:

• Text analytics. Natural language processing analyzes textual data of claims forms, emails, and social media posts to identify keywords and entities. While claims with suspicious language or inconsistent details can be flagged for further investigation, regulations like the Colorado AI Act require AI algorithm-based models to avoid discrimination and bias when flagging risk.¹²
• Audio-image-video analysis. Speech recognition and sentiment analysis can examine customer calls for signs of duress, allowed under the European Union’s AI Act on emotion inference for safety.¹³ Photo analytics can uncover irregularities in metadata, manipulation, and repeated use. Causation analytics can identify if alleged injuries were likely consistent with the experienced accident. Video analytics can verify the occurrence and extent of damage, identify authenticity of images, and highlight signs of tampering or staging.
• Geospatial analysis. Satellite images and comprehensive 3D drone footage can verify the extent and location of damage that may not be clearly visible in physical inspections. This could also reduce the risk of personal injury to claims personnel, especially at natural disaster sites.
• Internet of Things data. Real-time surveillance devices like vehicle telematics can reconstruct accidents and verify the legitimacy of claims. Smart home sensors like water leak detectors and security cameras can help gather evidence that can be used to verify claims and detect fraudulent or staged activities.
• Simulation models. Replicating the behavior of medical providers, repair shops, and others that individuals may work with under different scenarios in a controlled virtual environment can identify patterns and deviations from standard industry practices and detect instances such as overbilling, unnecessary services, and coordinated activities or probable collision rings between entities.

Combining AI and human foresight could be the way forward 

Over the past two decades, insurers have established special investigative units to detect and mitigate fraud. Looking ahead, anti-fraud leaders face several challenges from managing expenses to talent. Putting the “art” in artificial intelligence, insurers that pair sophisticated technology with human enablement can detect claims fraud and could potentially save billions of dollars for policyholders. Attracting and retaining skilled talent, along with continued support for automation, will likely also be important for companies as they look to achieve their long-term anti-fraud goals. 

This article contains general information and predictions only and Deloitte is not, by means of this article, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services. This article is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor.

Deloitte shall not be responsible for any loss sustained by any person who relies on this article.