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Prediction of Claims in Export Credit Finance: A Comparison of Four Machine Learning Techniques

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  • Mathias Bärtl

    (Hochschule für Technik, Wirtschaft und Medien Offenburg, 77652 Offenburg, Germany)

  • Simone Krummaker

    (Faculty of Actuarial Science and Insurance, Cass Business School, University of London, London EC1Y8TZ, UK)

Abstract

This study evaluates four machine learning (ML) techniques (Decision Trees (DT), Random Forests (RF), Neural Networks (NN) and Probabilistic Neural Networks (PNN)) on their ability to accurately predict export credit insurance claims. Additionally, we compare the performance of the ML techniques against a simple benchmark (BM) heuristic. The analysis is based on the utilisation of a dataset provided by the Berne Union, which is the most comprehensive collection of export credit insurance data and has been used in only two scientific studies so far. All ML techniques performed relatively well in predicting whether or not claims would be incurred, and, with limitations, in predicting the order of magnitude of the claims. No satisfactory results were achieved predicting actual claim ratios. RF performed significantly better than DT, NN and PNN against all prediction tasks, and most reliably carried their validation performance forward to test performance.

Suggested Citation

  • Mathias Bärtl & Simone Krummaker, 2020. "Prediction of Claims in Export Credit Finance: A Comparison of Four Machine Learning Techniques," Risks, MDPI, vol. 8(1), pages 1-27, March.
    • Handle: RePEc:gam:jrisks:v:8:y:2020:i:1:p:22-:d:326934
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    References listed on IDEAS

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    Cited by:

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    3. Nelson Kemboi Yego & Juma Kasozi & Joseph Nkurunziza, 2021. "A Comparative Analysis of Machine Learning Models for the Prediction of Insurance Uptake in Kenya," Data, MDPI, vol. 6(11), pages 1-17, November.
    4. Evaggelia Siopi & Thomas Poufinas & James Ming Chen & Charalampos Agiropoulos, 2023. "Can Regulation Affect the Solvency of Insurers? New Evidence from European Insurers," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 29(1), pages 15-30, May.
    5. Dawud Thongtha & Nathakhun Wiroonsri, 2023. "Normal Approximation for Fire Incident Simulation Using Permanental Cox Processes," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-20, March.

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