Motor insurance data analysis by quantum machine learning

Abstract In this paper, we analyse motor insurance claim data using a quantum machine learning approach. The objective of this study is to demonstrate how insurance claims can be analysed by leveraging the properties of quantum computing and to show that quantum-based approaches can improve prediction accuracy in motor insurance claim analysis, a task of considerable importance in the highly competitive insurance market. We employ a hybrid quantum-classical algorithm based on quantum reservoir computing (QRC) to improve predictive modelling through resampling of the original insurance data. The algorithm is implemented on IBM’s noise-free Qiskit simulator running on classical hardware, as the method does not require a large number of qubits. To assess its effectiveness, we benchmark the QRC-based approach against established classical machine learning techniques, including linear regression, XGBoost, and CatBoost.