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Fraud has led to a huge addition of expenses in health insurance sector inIndia. The work is aimed to provide methods applied to health insurance frauddetection. The work presents two approaches - a markov model and an improvedmarkov model using gradient boosting method in health insurance claims. Thedataset 382,587 claims of which 38,082 claims are fraudulent. The markov basedmodel gave the accuracy of 94.07% with F1-score at 0.6683. However, theimproved markov model performed much better in comparison with the accuracy of97.10% and F1-score of 0.8546. It was observed that the improved markov modelgave much lower false positives compared to markov model.

Markov model with machine learning integration for fraud detection in health insurance