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<p>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.</p>

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Motor insurance data analysis by quantum machine learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月10日

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  • Muhsin Tamturk,
  • Eran Ginossar,
  • Marco Carenzo,
  • Claude Perret

摘要

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.