Predicting credit card delinquencies: An application of deep neural networks

TY - JOUR

T1 - Predicting credit card delinquencies

T2 - An application of deep neural networks

AU - Sun, Ting

AU - Vasarhelyi, Miklos A.

N1 - Publisher Copyright: © 2018 John Wiley & Sons, Ltd.

PY - 2018/10/1

Y1 - 2018/10/1

N2 - 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.

AB - 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.

KW - artificial intelligence

KW - credit card delinquency

KW - deep neural network

KW - machine learning

KW - risk assessment

UR - https://www.scopus.com/pages/publications/85058640424

UR - https://www.scopus.com/pages/publications/85058640424#tab=citedBy

U2 - 10.1002/isaf.1437

DO - 10.1002/isaf.1437

M3 - Article

SN - 1550-1949

VL - 25

SP - 174

EP - 189

JO - Intelligent Systems in Accounting, Finance and Management

JF - Intelligent Systems in Accounting, Finance and Management

IS - 4

ER -