Name
Abstract | ||
|---|---|---|
AbstractSummaryThe 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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1002/isaf.1437 | Periodicals |
Keywords | Field | DocType |
artificial intelligence, credit card delinquency, deep neural network, machine learning, risk assessment | Decision tree,Naive Bayes classifier,Computer science,Issuer,Risk assessment,Credit card,Artificial intelligence,Deep learning,Artificial neural network,Machine learning,Credit risk | Journal |
Volume | Issue | ISSN |
25 | 4 | 1055-615X |
Citations | PageRank | References |
1 | 0.35 | 21 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ting Sun | 1 | 39 | 12.08 |
| Miklos A. Vasarhelyi | 2 | 427 | 42.36 |