Application of Machine Learning to Detect Fraudulent Maternal Medical Claims

Abstract

Fraudulent activities have caused great losses in the healthcare industry all over the world. Different methods such as upcoding, billing for services not rendered and many more are ways fraudulent activities occur. Traditional methods of fraud detection such as auditing and rule-based programming are no longer efficient due to the increase of data and complexity in the billing process of medical claims. There is a great need for new optimized methods to assist in fraud detection. Data mining and Machine learning are optimized methods that can be used to improve the sector. The objectives of the research were to train a machine learning-based model which detects a health insurance claim considered fraudulent, identify features in insurance claims that can be used for fraud detection, identify appropriate machine algorithms models to use for fraud detection, compare the performance of different machine learning algorithms and implement a prototype for detecting fraudulent health insurance claims The research explored the use of different machine learning methods to be able to detect fraud. The method used was the CRISP-DM process. The data went through stages of data collection, where data was collected from an insurance company which is based in Kenya, data preprocessing and transformation to ensure the data was clean, Training where the data was trained using different models, and lastly evaluation where a comparison analysis was done fo based on the performance of each model. The results gotten from the benchmark and performance evaluation showed that the gradient boosting classifier performed the best with an accuracy of 90.0% and AUC of 95.0%. The other models that performed well included the random forest with an accuracy of 90% and ANN with an accuracy of 88.0%. The model that performed poorly was the Logistic Regression with an accuracy of 59% and Naive Bayes with an accuracy of 47%. The gradient boosting tree classifier model was then used to develop a prototype.