1. Predicción de riesgo crediticio en Colombia usando técnicas de inteligencia artificial.
- Author
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Borrero-Tigreros, Diego and Bedoya-Leiva, Oscar
- Subjects
- *
CREDIT risk , *FINANCIAL risk , *INSTALLMENT plan , *SUPPORT vector machines , *FINANCIAL institutions , *ARTIFICIAL intelligence , *DECISION trees - Abstract
In this paper, new models for credit risk prediction in Colombia are proposed by using different artificial intelligence techniques. These models can be used to support the risk management area in banks, and they aim to identify clients that could be in default, generating a possible credit risk for financial institutions. Three techniques are used to obtain the models (neuronal networks, decision trees, and support vector machines) that predict the next payment of a client's fee based on basic data from the client and previous recorded installment payments. Decision trees turns out to be more accurate than the other techniques that have been used when predicting credit risk with a ROC area of 88.29%. The proposed models reach accuracies that are like some other papers in the state of the art and in some cases, they overcome models in other studies. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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