Researcher: Khanya May, University of the Witwatersrand, Johannesburg
Supervisor: Dr Wilbert Chagwiza, University of the Witwatersrand, Johannesburg
Fraud presents a threat that has serious consequences in the insurance industry. In recent years the use of machine learning and analytical techniques for fraud detection has been a topic of several research projects. This research aims to explore the capabilities of random forest in fraud detection. The random forest model is applied to TSA insurance claims data in an effort to predict fraud. The model achieved an accuracy and F measure of 69.72% and 75.04%, respectively. The performance of the model is better than that of an unskilled model which would accurately predict 54.67%. The is a 76.09% chance that the model will be able to distinguish between fraudulent and legitimate claims.
