Home > Healthcare > Healthcare IT > Payer/Provider Solutions > Healthcare Fraud Analytics Market
Healthcare Fraud Analytics Market size was valued at USD 2.3 billion in 2023 and is expected to exhibit growth at a CAGR of 24.1% from 2024 and 2032. High market growth can be attributed to the ongoing advancements in data analytics, rising incidence of healthcare fraud, increased healthcare spending and complexity, and increasing adoption of digital health solutions, among other contributing factors.
Moreover, the growing incidence of healthcare fraud, including fraudulent claims, billing schemes, identity theft, and prescription fraud, is a significant driver for the market. For instance, according to data from the U.S. Sentencing Commission, in 2022, there were 431 healthcare fraud offenders, representing 8.4% of all theft, property destruction, and fraud offenses. This marks a 1.4% increase in healthcare fraud offenders since 2018. Fraudulent activities result in substantial financial losses for healthcare providers, insurers, and governments, creating a strong demand for analytics solutions that can detect and prevent fraud.
Report Attribute | Details |
---|---|
Base Year: | 2023 |
Healthcare Fraud Analytics Market Size in 2023: | USD 2.3 Billion |
Forecast Period: | 2024 - 2032 |
Forecast Period 2024 - 2032 CAGR: | 24.1% |
2032 Value Projection: | USD 16.5 Billion |
Historical Data for: | 2021 - 2023 |
No. of Pages: | 100 |
Tables, Charts & Figures: | 181 |
Segments covered: | Solution Type, Deployment Mode, Application, End-use, and Region |
Growth Drivers: |
|
Pitfalls & Challenges: |
|
Furthermore, as healthcare spending continues to rise, so does the complexity of healthcare systems and transactions. This complexity provides more opportunities for fraudulent activities to occur. Healthcare fraud analytics tools help manage this complexity by analyzing large volumes of data and identifying suspicious patterns or anomalies.
Healthcare fraud analytics refers to the use of data analysis techniques, including statistical methods, machine learning, and artificial intelligence, to detect, prevent, and investigate fraudulent activities in healthcare. This includes identifying patterns, anomalies, and suspicious behavior in claims, billing, and other healthcare-related data to mitigate financial losses and ensure compliance with regulations.