According to a new healthcare market research industry, "Healthcare Fraud Analytics Market by Solution Type (Descriptive, Predictive, Prescriptive), Application (Insurance Claim, Payment Integrity), Delivery (On-premise, Cloud), End User (Government, Employers, Payers), COVID-19 Impact - Global Forecast to 2026", is projected to reach USD 5.0 billion by 2026 from USD 1.5 billion in 2021, at a CAGR of 26.7% during the forecast period.
Fraud analytics is the efficient use of data analytics and related business insights developed through statistical, quantitative, predictive, comparative, cognitive, and other emerging applied analytical models for detecting and preventing healthcare fraud.
The healthcare industry has been witnessing a number of cases of frauds, done by patients, doctors, physicians, and other medical specialists. Many healthcare providers and specialists have been observed to be engaged in fraudulent activities, for the sake of profit In the healthcare sector.
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A couple of reasons contributing to the growth of the health insurance market include the rise in the aging population, growth in healthcare expenditure, and increased burden of diseases. In the US, the number of citizens without health insurance has significantly decreased, from 48 million in 2010 to 28.6 million in 2016. In 2017, 12.2 million people signed up for or renewed their health insurance during the 2017 open enrollment period (Source: National Center for Health Statistics).
Emerging markets such as Asia promise significant growth in health insurance coverage, mainly due to increasing government initiatives, rising government and private investments for promoting medical insurance, and growing income levels.
The healthcare industry is changing at an incredible rate, and one of the major contributors to this change is the increasing popularity of healthcare communication through social media.
The deployment of fraud analytics solutions is a time-consuming process. The process involves creating user interfaces, new databases, and predictive models; evaluating and deploying models, and monitoring their effectiveness. In this process, data analysts continuously run algorithms until they get the most effective predictive model.
This means they must start the same process again with new data. Thus, if data analysts fail at one stage, the whole process is disturbed. Furthermore, the software requires frequent upgrades, as fraudsters constantly change tactics. This adds to the total cost of fraud analytics solutions.