Towards Better Detection of Fraud in Health Insurance Claims in Kenya: Use of Naïve Bayes Classification Algorithm

The extent, possibility, and complexity of the healthcare industry have attracted widespread fraud that has contributed to rising healthcare costs hence affecting patients’ health and negatively impacting the economy of many countries. Despite putting up various technologies and strategies to fight fraud such as planned, targeted audits, random audits, whistle-blowing, and biometric systems, fraud in claims has continued to be a challenge in most of the health insurance providers in Kenya. This paper explored the application of data mining in detecting fraud in health insurance claims in Kenya. Classification algorithms (Naïve Bayes, Decision Tree and K-Nearest Neighbour) were used to build predictive models for the knowledge discovery process. After conducting several experiments, the resulting models showed that the Naïve Bayes works well in detecting fraud in claims with 91.790% classification accuracy and 74.12% testing hit rate. A prototype was developed based on the rules extracted from the Naïve Bayes model, which, if adopted, will save costs by detecting fraud as it is committed. Fraud detection in health insurance claims is much needed in many countries so as to help reduce loss of money and in return improve service delivery to patients.