Role of AI in Healthcare Fraud Detection

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, fraudulent activities done by patients include the fraudulent procurement of sickness certificates, prescription fraud, and evasion of medical charges.

Over the years, the number of people benefitting from various healthcare schemes has grown considerably. 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.

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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.

This growth is aided by the increasing affordability of health insurance for the middle class in this region and the rising awareness regarding the benefits of health insurance. In the UAE, as per a new regulatory policy (2017), any citizen residing and working in the UAE needs to be insured medically. Such regulatory changes in the buying behavior of employers (from employer-based plans to providing individual spending allowances to the staff) are driving the health insurance market in the region.

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.

This vast network of healthcare influencers, leaders, patients, providers, organizations, and governmental entities creates a massive amount of healthcare data on a regular basis. This data, if segregated, segmented, and analyzed in a meaningful way, can offer incredible value for improving treatment efficiencies and health outcomes.

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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.

Descriptive analytics forms the base for the effective application of predictive or prescriptive analytics. Hence, these analytics use the basics of descriptive analytics and integrate them with additional sources of data in order to produce meaningful insights.