Insurance fraud is one of the most prevalent issues for healthcare providers, resulting in tens of billions in annual revenue losses, as reported by the National Health Care Anti-Fraud Association (NHCAA) in its last publicly available survey. The inefficiencies of manual claims processing only worsen the problem, highlighting the urgent need for innovative solutions. Fortunately, AI and machine learning models have revolutionized the industry, enabling healthcare insurers to detect and prevent fraud with remarkable efficiency and precision. Let’s explore the role of AI in the insurance industry and how it helps healthcare providers mitigate financial losses and manage claims effectively.

Garbage in, garbage out (GIGO) is a popular concept in computer programming. The GIGO principle applies in machine learning and artificial intelligence, too. If you feed your model rubbish (read: inaccurate or irrelevant data), it will turn out to be rubbish.

Almost every organisation engaged in the process of developing machine learning algorithms dabbles with the idea of setting up an in-house team for data annotation requirements. Companies feel that assigning the seemingly easy task of data annotation to their employees will save them of both time and money.