Snapshot img

Healthcare Fraud Detection Market Size 2024-2028

The healthcare fraud detection market size is forecast to increase by USD 914.3 million at a CAGR of 11% between 2023 and 2028.

In the healthcare industry, the market is experiencing significant growth due to several key factors. The increasing number of patients seeking health insurance and the complexity of insurance claims are driving the need for advanced solutions. Statistical methods, machine learning, and artificial intelligence are being employed to enhance payment integrity and detect fraudulent activities in real time. These technologies enable on-premises and cloud-based solutions to analyze large volumes of data and identify patterns that may indicate fraud. The emergence of social media and its impact on the healthcare industry also necessitates the use of advanced analytics to ensure accurate claim processing and prevent fraud. However, challenges persist, including the time-consuming deployment and need for frequent upgrades of fraud detection systems. To address these challenges, healthcare providers and insurance companies are investing in advanced analytics solutions to streamline operations, improve efficiency, and maintain payment integrity.

What will be the Size of the Market During the Forecast Period?

Request Free Sample

Healthcare fraud continues to pose a significant challenge for the healthcare industry, resulting in substantial financial losses. According to estimates, healthcare fraud costs the US economy approximately USD 68 billion annually. This figure includes fraudulent claims, billing schemes, identity theft, prescription fraud, and other fraudulent healthcare activities. Fraudulent claims arise when providers or patients submit false or exaggerated claims to insurance companies for medical services. Billing schemes involve overcharging for services or supplies, while identity theft occurs when an individual uses someone else's personal information to obtain healthcare services or prescription medications. Prescription fraud includes the unlawful distribution of prescription drugs, often for financial gain.



Furthermore, healthcare fraud offenders employ various tactics to evade detection, making it essential for healthcare organizations to implement strong fraud detection and prevention measures. Advanced analytics solutions, such as data analysis techniques and statistical methods, have emerged as effective tools in the fight against healthcare fraud. Machine learning and artificial intelligence (AI) are increasingly being used in healthcare fraud detection. These technologies enable descriptive analytics, which involves analyzing historical data to identify patterns and trends. Predictive analytics uses this information to anticipate future fraudulent activities, while prescriptive analytics recommends actions to prevent fraud. Data science plays a crucial role in healthcare fraud detection, as it involves extracting insights from complex data sets. Data analytics, including fraud detection solutions, can be delivered through on-premise or cloud-based solutions. On-premise solutions offer greater control over data security, while cloud-based solutions provide flexibility and scalability. Insurance claims review is a critical component of healthcare fraud detection.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Descriptive analytics
  Predictive analytics
  Prescriptive analytics


Geography

  North America

    Canada
    US


  Asia

    China
    India


  Europe

    Germany


  Rest of World (ROW)

By Type Insights

The descriptive analytics segment is estimated to witness significant growth during the forecast period.

Descriptive analytics serves as the foundation for advanced analytics such as predictive and prescriptive analytics. By integrating basic descriptive analytics with additional data sources, meaningful insights are generated. Descriptive analytics is a fundamental analytics technique widely used by healthcare organizations. Each business unit employs descriptive analytics to monitor operational efficiency and identify trends. Financial statements, presentations, and dashboards showcase the outcomes of descriptive analytics. This form of analytics examines past data to understand the changes that have occurred. Insurance claims review, pharmacy billing fraud, and payment integrity are some areas where descriptive analytics plays a crucial role in maintaining healthcare spending.

Furthermore, machine learning and artificial intelligence technologies can enhance the capabilities of descriptive analytics, leading to improved fraud detection. On-premis