Global Healthcare Fraud Detection Market to Reach US$8.0 Billion by 2030

The global market for Healthcare Fraud Detection estimated at US$2.1 Billion in the year 2024, is expected to reach US$8.0 Billion by 2030, growing at a CAGR of 25.4% over the analysis period 2024-2030. Descriptive Analytics, one of the segments analyzed in the report, is expected to record a 27.6% CAGR and reach US$5.4 Billion by the end of the analysis period. Growth in the Predictive Analytics segment is estimated at 20.9% CAGR over the analysis period.

The U.S. Market is Estimated at US$561.2 Million While China is Forecast to Grow at 33.8% CAGR

The Healthcare Fraud Detection market in the U.S. is estimated at US$561.2 Million in the year 2024. China, the world`s second largest economy, is forecast to reach a projected market size of US$2.0 Billion by the year 2030 trailing a CAGR of 33.8% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 20.2% and 22.9% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 21.4% CAGR.

Global Healthcare Fraud Detection Market – Key Trends & Drivers Summarized

Why Has Fraud Detection Become a Priority Across Global Healthcare Systems?
Healthcare fraud detection has become a critical pillar of operational integrity and financial sustainability for healthcare payers, providers, and governments. Fraudulent activities such as upcoding, phantom billing, kickbacks, identity theft, and prescription fraud result in billions of dollars in losses annually, straining public budgets and private insurance systems alike. The growing complexity of healthcare billing systems, combined with the digitization of health records and claims, has made it easier for fraudulent actors to exploit systemic loopholes—necessitating advanced detection tools to maintain accountability and reduce economic waste.

Increased scrutiny from regulators and a surge in value-based reimbursement models are accelerating the need for real-time fraud prevention. Health systems are shifting from reactive audits to proactive surveillance using predictive analytics, machine learning, and anomaly detection. As fraud schemes evolve in sophistication, especially in digitally enabled claims environments, the emphasis is moving toward intelligent systems that detect irregularities before they result in payments, rather than relying on post-transaction investigations.

How Are Data Analytics and AI Revolutionizing Fraud Detection Capabilities?
The application of artificial intelligence (AI) and big data analytics has dramatically enhanced the capabilities of fraud detection systems. Predictive modeling algorithms can now analyze millions of claims, electronic health records (EHRs), and provider behavior patterns to identify suspicious deviations. Unsupervised learning models are particularly effective at uncovering new fraud schemes by clustering similar behavior and flagging outliers in real time. Natural language processing (NLP) is also being used to scan unstructured data sources like doctor notes and insurance correspondence to identify discrepancies and unusual billing language.

Additionally, AI-based scoring systems assign fraud risk levels to claims and providers, enabling insurers and investigators to prioritize high-risk targets. Blockchain is gaining interest as a secure, immutable platform for verifying transactions and maintaining transparency across health ecosystems. Combined with robust data governance and interoperability frameworks, these technologies are improving detection accuracy, reducing false positives, and accelerating resolution timelines—all of which contribute to more cost-effective and responsive fraud management.

Which Stakeholders and Regions Are Leading Adoption of Fraud Detection Systems?
Insurance companies and public health programs are leading adopters of healthcare fraud detection technologies due to their exposure to large-scale claims processing. In the U.S., Medicare and Medicaid programs have driven innovation through large-scale anti-fraud initiatives and partnerships with data analytics firms. Private insurers are investing heavily in AI platforms to protect premium revenues and maintain regulatory compliance. Meanwhile, providers are also implementing internal fraud risk controls to avoid litigation, protect reputational capital, and comply with payer contracts.

Regionally, North America holds the largest market share due to the complexity of its insurance landscape and the scale of fraudulent activity. Europe is following closely, with national health systems introducing centralized monitoring mechanisms and predictive audit tools. In Asia-Pacific and Latin America, increasing digitization of claims and healthcare infrastructure is paving the way for fraud detection platform adoption, especially as universal healthcare programs expand and private insurance markets grow. Cross-border data-sharing initiatives are also emerging to combat international fraud rings, especially in telemedicine and pharmaceutical distribution.

The Growth in the Healthcare Fraud Detection Market Is Driven by Several Factors
The growth in the healthcare fraud detection market is driven by the escalating scale and complexity of fraud schemes, rising healthcare expenditure, and increasing regulatory enforcement worldwide. The transition to digital health records and automated billing has created fertile ground for fraud but also enables robust data analytics to counteract it. The adoption of AI, machine learning, and blockchain technologies is transforming fraud detection from a retrospective audit function into a real-time, risk-based decision-making capability.

Payers and providers alike are under growing pressure to reduce administrative waste, recover lost revenue, and ensure compliance with reimbursement policies and government regulations. Investments in integrated fraud detection systems are now seen as essential to operational efficiency and financial control. As healthcare ecosystems become more interconnected and data-driven, fraud detection is evolving into a foundational capability that supports trust, transparency, and sustainability in modern healthcare delivery.