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Early Warning System for Debt Group Migration: The Case of One Commercial Bank in Vietnam

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  • Nguyen Quoc Hung

    (University of Economics Ho Chi Minh City, Ho Chi Minh City, Vietnam)

  • Trinh Hoang Viet

    (University of Economics Ho Chi Minh City, Ho Chi Minh City, Vietnam)

  • Phuong Truong Viet

    (University of Economics Ho Chi Minh City, Ho Chi Minh City, Vietnam)

  • Ly Truong Thi Minh

    (University of Economics Ho Chi Minh City, Ho Chi Minh City, Vietnam)

Abstract

This study utilizes machine learning models, including Logistic Regression, Support Vector Machine, Decision Tree, and Random Forest, in the early warning system for debt group migration in a Vietnamese commercial bank. In predicting customers’ overdue debt migration (B Score), the RF model achieves the highest accuracy of 81.84%. However, if the priority is to reduce Type I errors, SVM performs better with a recall of 91.48%, although the accuracy drops to 46.62%. When predicting customers’ debt group improvement (C Score), SVM proves to be the optimal model in terms of both accuracy and criteria based on Type II errors, with an accuracy of 71.6% and precision of 62.3%. When applied to new datasets, the evaluation criteria decrease, but SVM remains the most optimal model for both B Score and C Score. Additionally, the research results demonstrate that tuning the model parameters leads to a significant improvement in accuracy compared to the default parameters.

Suggested Citation

  • Nguyen Quoc Hung & Trinh Hoang Viet & Phuong Truong Viet & Ly Truong Thi Minh, 2024. "Early Warning System for Debt Group Migration: The Case of One Commercial Bank in Vietnam," Foundations of Management, Sciendo, vol. 16(1), pages 195-216.
  • Handle: RePEc:vrs:founma:v:16:y:2024:i:1:p:195-216:n:1012
    DOI: 10.2478/fman-2024-0012
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    References listed on IDEAS

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    1. Kwon, Yujin & Park, Sung Y., 2023. "Modeling an early warning system for household debt risk in Korea: A simple deep learning approach," Journal of Asian Economics, Elsevier, vol. 84(C).
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    4. Forster, Jonathan J. & Buzzacchi, Matteo & Sudjianto, Agus & Nagao, Risa, 2016. "Modelling credit grade migration in large portfolios using cumulative t-link transition models," European Journal of Operational Research, Elsevier, vol. 254(3), pages 977-984.
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    More about this item

    Keywords

    machine learning models; debt group migration; B score; C score; model parameters tuning;
    All these keywords.

    JEL classification:

    • E50 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - General
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage

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