Articlegold2018

Credit Risk Analysis Using Machine and Deep Learning Models

Peter Martey Addo · Dominique Guégan · Bertrand K. Hassani

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Financial Distress and Bankruptcy Prediction Banking stability, regulation, efficiency Imbalanced Data Classification Techniques

Abstract

Due to the advanced technology associated with Big Data, data availability and computing power, most banks or lending institutions are renewing their business models. Credit risk predictions, monitoring, model reliability and effective loan processing are key to decision-making and transparency. In this work, we build binary classifiers based on machine and deep learning models on real data in predicting loan default probability. The top 10 important features from these models are selected and then used in the modeling process to test the stability of binary classifiers by comparing their performance on separate data. We observe that the tree-based models are more stable than the models based on multilayer artificial neural networks. This opens several questions relative to the intensive use of deep learning systems in enterprises.

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References

1972The Estimation of the Lorenz Curve and Gini Index 2003Credit rating analysis with support vector machines and neural networks: a market comparative study 2010Consumer credit-risk models via machine-learning algorithms 1998Data mining for direct marketing: problems and solutions 2010A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems 2007A neural network approach for credit risk evaluation

Cited by

2021Deep learning for credit scoring: Do or don’t?2022Corporate finance risk prediction based on LightGBM 2019Bayesian regularized artificial neural networks for the estimation of the probability of default 2020Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE 2020A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees 2019Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting 2021Data Science Methodologies: Current Challenges and Future Approaches

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