Insurance Fraud Detection Market Report 2026

The insurance fraud detection market size has grown exponentially in recent years. It will grow from $9.05 billion in 2025 to $11.32 billion in 2026 at a compound annual growth rate (CAGR) of 25.1%. The growth in the historic period can be attributed to increasing volume of digital insurance transactions, rising incidence of fraudulent claims, expansion of online insurance channels, growing regulatory scrutiny, adoption of data-driven fraud prevention tools.

The insurance fraud detection market size is expected to see exponential growth in the next few years. It will grow to $27.66 billion in 2030 at a compound annual growth rate (CAGR) of 25%. The growth in the forecast period can be attributed to increasing investments in ai-powered fraud detection, rising demand for real-time fraud analytics, expansion of cloud-based fraud platforms, growing focus on cross-channel fraud prevention, increasing integration of advanced authentication technologies. Major trends in the forecast period include increasing deployment of predictive fraud analytics, rising use of identity verification technologies, growing integration of ai-based detection engines, expansion of real-time fraud monitoring systems, enhanced focus on regulatory compliance.

The increasing incidence of insurance fraud is expected to drive the growth of the insurance fraud detection market. Insurance fraud involves deliberately deceiving an insurer - often through exaggerated or entirely false claims - to obtain money or coverage unlawfully, imposing costs on honest policyholders and insurers. Fraud-detection technologies help identify and prevent such schemes by analyzing data, detecting anomalies, and flagging suspicious activities. For instance, in June 2023, the Insurance Fraud Enforcement Department (IFED) of the City of London Police, a UK-based specialist police unit, reported a 61% increase in opportunistic insurance fraud cases for the period March 2022 to April 2023 compared with the previous period. Therefore, the rise in insurance fraud is contributing to the growth of the insurance fraud detection market.

Major companies operating in the insurance fraud detection market are focusing on developing advanced solutions, such as artificial intelligence (AI)-powered fraud detection tools, to enhance claim monitoring accuracy, reduce false positives, and improve the identification of fraudulent activities. AI-powered fraud detection tools are advanced systems that use artificial intelligence and large datasets to detect suspicious patterns, generate alerts, and support investigators in identifying potential fraud with greater precision. For instance, in June 2024, Clara Analytics Inc., a US-based software company, launched CLARA Fraud, an AI-driven fraud detection solution that leverages extensive workers’ compensation datasets to improve visibility into suspicious claims. The solution provides data-backed reasons for Special Investigation Unit (SIU) referrals and helps claims professionals investigate fraud confidently, uncovering fraudulent activities across millions of claims while minimizing false positives.

In February 2025, LexisNexis Risk Solutions, a US-based provider of data analytics, identity verification, and risk assessment solutions, acquired IDVerse for an undisclosed amount. With this acquisition, LexisNexis aimed to strengthen its document-authentication and deepfake detection capabilities in the insurance sector. IDVerse is a UK-based provider of AI-powered document authentication and fraud detection solutions.

Major companies operating in the insurance fraud detection market are ACI Worldwide Inc.; BAE Systems PLC; BRIDGEi2i Analytics Solutions Pvt. Ltd.; Datawalk Inc.; DXC Technology Co.; Experian PLC; Fair Isaac Corp.; Fiserv Inc.; FRISS Inc.; International Business Machines Corporation; iovation Inc.; Kount Inc.; RELX Group; Oracle Corp.; SAP SE; SAS Institute Inc.; Scorto Inc.; TransUnion LLC; Wipro Ltd.; Accenture plc; Equifax Inc.; Perceptiviti Inc.; Shift Technology S.A.; Verisk Analytics Inc.; Mody Data Solution Pvt. Ltd.; Pixalate Inc.; Skopenow Inc.; Sigma Insights Inc.; Fraud Guard LLC.

North America was the largest region in the insurance fraud detection market in 2025.Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the insurance fraud detection market report are Asia-Pacific, South East Asia, Western Europe, Eastern Europe, North America, South America, Middle East, Africa. The countries covered in the insurance fraud detection market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Taiwan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

Tariffs are impacting the insurance fraud detection market by increasing costs of imported analytics hardware, cybersecurity systems, authentication devices, and advanced computing infrastructure. Insurance companies in North America and Europe are most affected due to reliance on imported IT and security technologies, while Asia-Pacific faces higher costs for large-scale fraud detection deployments. These tariffs are increasing solution implementation costs and slowing technology upgrades. However, they are also encouraging localized software development, regional cybersecurity partnerships, and increased adoption of cloud-based fraud detection platforms.

The insurance fraud detection market research report is one of a series of new reports that provides insurance fraud detection market statistics, including insurance fraud detection industry global market size, regional shares, competitors with a insurance fraud detection market share, detailed insurance fraud detection market segments, market trends and opportunities, and any further data you may need to thrive in the insurance fraud detection industry. This insurance fraud detection market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.

Insurance fraud detection is a method aimed at identifying and preventing fraudulent activities related to money or insurance. Various software-based solutions are employed to analyze historical patterns and incidents, predicting and preventing future occurrences. Organizations utilize insurance fraud detection for fraud analytics, authentication, governance, risk management, and compliance to safeguard databases and identify vulnerabilities.

The primary deployment types of insurance fraud detection are on-premises and cloud. On-premises insurance fraud detection involves software that is implemented and activated on computers located on the premises of the individual or using the software, rather than at a distant facility like a server farm or cloud. The components of insurance fraud detection include solutions and services, catering to both small and medium-sized enterprises (SMEs) and large enterprises. Applications of insurance fraud detection encompass claims fraud, identity theft, payment and billing fraud, and money laundering. These applications find use across various end-users such as insurance companies, insurance intermediaries, agents and brokers, and other entities.

The insurance fraud detection includes revenues earned by entities by statistical data analysis services, multi-layered process, behavioral biometrics services, adaptive and predictive analytics servies. The market value includes the value of related goods sold by the service provider or included within the service offering. Only goods and services traded between entities or sold to end consumers are included.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD unless otherwise specified).

The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.

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