Comparison of Classical ML algorithms and Quantum Machine Learning Algorithms in Identifying Insurance Claim Fraud

Fraud detection in insurance system is a significant challenge because of the data imbalance and complex claim patterns. For these kinds of tasks classical machine learning models like support vector Machines have demonstrated very good performance. A promising paradigm that has surface recently for improving learning through quantum features is Quantum Machine Learning. In this research paper for the purpose of home insurance fraud detection a comparative study of the classical SVM with quantum support vector machines is done. A dataset with different features was used to evaluate the accuracy, false positives and recall. Qiskit ZZ Feature map and fidelity-based quantum kernels are used to implement the quantum support vector machines. The experimental results show that the QSVM achieves competitive accuracy but under class imbalance it struggles with fraud recall because of the limitations of quantum kernel methods. This paper gives the practical insights of Quantum Machine learning for the insurance fraud detection. Also, Explainable AI(XAI) technique was incorporated to analyze the fraud detections.