 Predicting credit card delinquencies: An application of deep neural networksTing Sun and Miklos A. Vasarhelyi Intelligent Systems in Accounting, Finance and Management, 2018, vol. 25, issue 4, 174-189 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. Date: 2018 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:wly:isacfm:v:25:y:2018:i:4:p:174-189 Ordering information: This journal article can be ordered from Access Statistics for this article More articles in Intelligent Systems in Accounting, Finance and Management from John Wiley & Sons, Ltd. |
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