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Predicting credit card delinquencies: An application of deep neural networks

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  • Ting Sun
  • Miklos A. Vasarhelyi

Abstract

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real‐life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine‐learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.

Suggested Citation

  • Ting Sun & Miklos A. Vasarhelyi, 2018. "Predicting credit card delinquencies: An application of deep neural networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 25(4), pages 174-189, October.
    • Handle: RePEc:wly:isacfm:v:25:y:2018:i:4:p:174-189
    DOI: 10.1002/isaf.1437
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    References listed on IDEAS

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    Cited by:

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    2. Cheng Few Lee, 2020. "Financial econometrics, mathematics, statistics, and financial technology: an overall view," Review of Quantitative Finance and Accounting, Springer, vol. 54(4), pages 1529-1578, May.
    3. Gunnarsson, Björn Rafn & vanden Broucke, Seppe & Baesens, Bart & Óskarsdóttir, María & Lemahieu, Wilfried, 2021. "Deep learning for credit scoring: Do or don’t?," European Journal of Operational Research, Elsevier, vol. 295(1), pages 292-305.

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