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Performance Of Using Machine Learning Approaches For Credit Rating Prediction: Random Forest And Boosting Algorithms

Author

Listed:
  • Chiou, W. Paul

    (Northeastern University)

  • Dong, Yuchen

    (MathWorks)

  • Ma, Sofia

    (MathWorks)

Abstract

Applying machine learning techniques to improve the accuracy and efficiency of predictions of credit risk rating is increasingly critical to the financial industry. In this study, we apply MATLAB to investigate the performance of two approaches, decision forest and boosting algorithms, by using a wide range of financial data. The empirical outcomes suggest that both methods exhibit considerable performance but may be superior in different scenarios. Boosting algorithms method exhibits accuracy rates of approximately 67% across the credit rating categories. The decision forests model generates lower accuracy rates for low and medium classifications than the boosting method, but the accuracy rate for high credit ratings reaches 79%, more accurate than the outcome using the boosting method.

Suggested Citation

  • Chiou, W. Paul & Dong, Yuchen & Ma, Sofia, 2023. "Performance Of Using Machine Learning Approaches For Credit Rating Prediction: Random Forest And Boosting Algorithms," Journal of Financial Transformation, Capco Institute, vol. 58, pages 44-53.
    • Handle: RePEc:ris:jofitr:2345
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    More about this item

    Keywords

    Machine learning; MATLAB;

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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