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Application of a Machine Learning Algorithm to Assess and Minimize Credit Risks

Author

Listed:
  • Garnik Arakelyan

    (Faculty of Computer Science and Statistics, Armenian State University of Economics (ASUE), Paruyr Sevaki Str. 77, Yerevan 0069, Armenia)

  • Armen Ghazaryan

    (Faculty of Computer Science and Statistics, Armenian State University of Economics (ASUE), Paruyr Sevaki Str. 77, Yerevan 0069, Armenia)

Abstract

The banking system, as the most important sector of the economy of every country, often encounters a number of risks. Financial institutions of that system operate in an unstable environment, and without having complete information about that environment, they may suffer significant losses. The main source of such losses is considered to be credit risks, and for the management of these, various mathematical models are being developed which will allow banks to make decisions on granting a loan. Lately, for this purpose, machine learning (ML) classification algorithms have often been used for credit risk modeling. In this research work, using the ideas of well-known ML algorithms, a new algorithm for solving the binary classification problem was developed. By means of the algorithm created, based on real data, a classification model has been developed. Qualitative indicators of that model, such as ROC AUC, PR AUC, precision, recall, and F1 score, were evaluated. By modifying the resulting probabilities into a range of 300–850 score points, a scoring model has been developed, the usage of which can mitigate credit risk and protect financial organizations from major losses.

Suggested Citation

  • Garnik Arakelyan & Armen Ghazaryan, 2025. "Application of a Machine Learning Algorithm to Assess and Minimize Credit Risks," JRFM, MDPI, vol. 18(9), pages 1-16, September.
    • Handle: RePEc:gam:jjrfmx:v:18:y:2025:i:9:p:520-:d:1751055
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    References listed on IDEAS

    as
    1. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep learning models," Working Papers 2018:08, Department of Economics, University of Venice "Ca' Foscari".
    2. Xin Wang & Kai Zong & Cuicui Luo, 2022. "Credit risk detection based on machine learning algorithms," International Journal of Financial Services Management, Inderscience Enterprises Ltd, vol. 11(3), pages 183-189.
    3. Jomark Pablo Noriega & Luis Antonio Rivera & José Alfredo Herrera, 2023. "Machine Learning for Credit Risk Prediction: A Systematic Literature Review," Data, MDPI, vol. 8(11), pages 1-17, November.
    4. David Mhlanga, 2021. "Financial Inclusion in Emerging Economies: The Application of Machine Learning and Artificial Intelligence in Credit Risk Assessment," IJFS, MDPI, vol. 9(3), pages 1-16, July.
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