An Improved Ensemble Method With Data Resampling for Credit Risk Prediction

Idowu Aruleba · Yanxia Sun

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Financial Distress and Bankruptcy Prediction Customer churn and segmentation Imbalanced Data Classification Techniques

Abstract

The increasing complexity and dynamic nature of financial data present significant challenges in accurately predicting credit risk, a critical task in the banking and finance sector. The application of machine learning (ML) in credit risk prediction has been hindered by the imbalanced nature of credit datasets. This study proposes an improved approach for predicting credit risk using a stacked ensemble method combined with a hybrid data resampling technique. The ensemble comprises random forests, logistic regression, and a convolutional neural network (CNN) as base learners, with the multilayer perceptron (MLP) serving as a meta-learner. To address the data imbalance, the Synthetic Minority Over-sampling Technique and Edited Nearest Neighbors (SMOTE-ENN) technique was applied. The proposed approach is benchmarked against other well-performing classifiers, including random forest, logistic regression, MLP, and CNN. The integration of hybrid data resampling with a robust stacking ensemble significantly enhanced credit risk prediction, with the proposed approach achieving sensitivity and specificity of 0.921 and 0.946 for the Australian dataset and 0.928 and 0.891 for the German dataset. Also, the stacked classifier achieved a sensitivity and specificity of 0.000 and 1.000 before data resampling for the Credit Risk Classification dataset with an accuracy of 0.7644. After data resampling, the accuracy, sensitivity, and specificity are 0.8056, 0.7989 and 0.8125, respectively. On the other hand, using the credit risk analysis for extended banking loans dataset, the accuracy, sensitivity and specificity of the stacked classifier before data resampling are 0.8429, 0.6316, and 0.9216 respectively. After data resampling, the accuracy, sensitivity and specificity score of the stacked classifier trained using the credit risk analysis for extended banking loans dataset are 0.9632, 1.0000, and 0.9242 respectively. This shows that after data resampling, the performance of the stacked classifier trained using the credit risk analysis for extended banking loans dataset outperformed other models.

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References

2020Examining non-linear built environment effects on elderly’s walking: A random forest approach 2011Two credit scoring models based on dual strategy ensemble trees 2020Machine learning techniques for credit risk evaluation: a systematic literature review 2022An hybrid deep learning approach for depression prediction from user tweets using feature-rich CNN and bi-directional LSTM 2019Integration of unsupervised and supervised machine learning algorithms for credit risk assessment 2019Application of new deep genetic cascade ensemble of SVM classifiers to predict the Australian credit scoring 2022Assessing credit risk of commercial customers using hybrid machine learning algorithms 2020A Comparative Assessment of Credit Risk Model Based on Machine Learning ——a case study of bank loan data

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