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- W4293066274 abstract "Credit scoring is a significant research domain for both finance and computer science researchers. Customers’ loan application behavior is highly related to their future default performance, which requires more studies. This study applies a modified approach of sequential-pattern-mining-based classification to mine important and discriminant behavioral patterns of customers. And then, we use the patterns to predict customers’ likelihood to default on a future loan. Our approach specifically addresses the problem of data imbalance. Evaluation based on real business data shows that our approach outperforms a series of time series classification methods, including deep learning models. In addition, such pattern-based classification approaches have the merit of explainability in features. Specific patterns of customer loan applications are found to be related to their future default behavior. Therefore, our method enhances the business understanding, provides managerial insights, and thus is more likely to be accepted by the industry." @default.
- W4293066274 created "2022-08-26" @default.
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- W4293066274 date "2022-05-25" @default.
- W4293066274 modified "2023-09-26" @default.
- W4293066274 title "An approach for credit scoring based on sequential pattern mining" @default.
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- W4293066274 doi "https://doi.org/10.1117/12.2637635" @default.
- W4293066274 hasPublicationYear "2022" @default.
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