Document Type : Research Manuscript
Authors
Faculty of Statistics, Mathematics, and Computer Science, Allameh Tabataba'i University, Tehran, Iran
Abstract
Abstract of paper: This study explores the application of Recurrent Neural Networks (RNNs) for predicting loan defaults, with a particular emphasis on incorporating uncertainty estimation into the predictive framework. Conventional RNN models demonstrate high accuracy, but they fail to provide quantitative measures of prediction uncertainty. To address this limitation, a dual modeling approach is proposed: a standard RNN model for achieving high predictive accuracy and an uncertainty-aware RNN model incorporating Bayesian inference. The uncertainty-aware model enables enhanced risk assessment through confidence level estimation and improved capture of complex temporal dependencies in financial data. Experimental results indicate that both proposed models outperform traditional methods, with the uncertainty-aware variant offering superior risk evaluation capabilities through its probabilistic outputs. These findings contribute to advancing credit risk assessment methodologies and offer practical value for financial institutions seeking more robust default prediction systems.
Keyword: Recurrent Neural Networks (RNNs), Loan Default Prediction, Uncertainty Quantification, Credit Risk Assessment.
Keywords
- Recurrent Neural Networks (RNNs)
- Loan default prediction
- Uncertainty quantification
- Credit risk assessment
Main Subjects
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