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Comparison of different approaches using Random Forest for imbalanced credit data

Author

Listed:
  • Anna Matuszyk

    (Warsaw School of Economics, Collegium of Management and Finance, Financial System Department)

Abstract

Credit scoring models are extensively used in credit risk management of individual customers. These models are based on econometric methods using past data about customers, both defaulters and non- -defaulters. These models focus on the optimal separation between good and bad customers taking into account two types of errors that appear, namely: the False Positive (Type 1 error) and the False Negative (Type 2 error). The purpose of the project was to focus on the problem of unbalanced data. Different balancing methods have been applied to the data set obtained from the financial institution operating in the European market. Various levels of unbalance have been considered and different statistical assessment metrics have been compared.

Suggested Citation

  • Anna Matuszyk, 2023. "Comparison of different approaches using Random Forest for imbalanced credit data," Bank i Kredyt, Narodowy Bank Polski, vol. 54(4), pages 419-436.
    • Handle: RePEc:nbp:nbpbik:v:54:y:2023:i:4:p:419-436
    as

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    File URL: https://bankikredyt.nbp.pl/content/2023/04/bik_04_2023_04.pdf
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    References listed on IDEAS

    as
    1. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    2. B Baesens & T Van Gestel & S Viaene & M Stepanova & J Suykens & J Vanthienen, 2003. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 627-635, June.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    credit scoring models; unbalanced data; balancing technique; Random Forest; model performance;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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