Healthcare companies face unique challenges in controlling cost and preventing losses proactively. This is especially true with fraud detection in the healthcare industry.
The rising demand for healthcare fraud detection compelled the Industry experts to look for new technology solutions to prevent fraudulent activities and offset rising healthcare costs.The aim of any effective fraud detection program is to identify fraudulent billing activity, series of hospital admissions with an extended period, identical doctor and ailment information, purchasing short term policy, etc.
The challenge lies in identifying fraud, abuse and waste before authorization and payment, while employing safeguards to protect medical resources and patients’ well-being.
Cognub has designed a cognitive model to detect fraud and abuse that involves machine learning and artificial intelligence techniques in healthcare claims. The model can effectively assess individual claim’s attributes in relation to other claims and find out how the claims are related to or different from each other. Machine learning based model will help in performing more complicated tasks than the traditional rules-based system which include learning fraud patterns from data and identifying previously unknown types of fraud.