Articlegold2020

A Comparative Performance Assessment of Ensemble Learning for Credit Scoring

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Financial Distress and Bankruptcy Prediction Forecasting Techniques and Applications Imbalanced Data Classification Techniques

Abstract

Extensive research has been performed by organizations and academics on models for credit scoring, an important financial management activity. With novel machine learning models continue to be proposed, ensemble learning has been introduced into the application of credit scoring, several researches have addressed the supremacy of ensemble learning. In this research, we provide a comparative performance evaluation of ensemble algorithms, i.e., random forest, AdaBoost, XGBoost, LightGBM and Stacking, in terms of accuracy (ACC), area under the curve (AUC), Kolmogorov–Smirnov statistic (KS), Brier score (BS), and model operating time in terms of credit scoring. Moreover, five popular baseline classifiers, i.e., neural network (NN), decision tree (DT), logistic regression (LR), Naïve Bayes (NB), and support vector machine (SVM) are considered to be benchmarks. Experimental findings reveal that the performance of ensemble learning is better than individual learners, except for AdaBoost. In addition, random forest has the best performance in terms of five metrics, XGBoost and LightGBM are close challengers. Among five baseline classifiers, logistic regression outperforms the other classifiers over the most of evaluation metrics. Finally, this study also analyzes reasons for the poor performance of some algorithms and give some suggestions on the choice of credit scoring models for financial institutions.

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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1966Financial Ratios As Predictors of Failure 2015Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research 1997Statistical Classification Methods in Consumer Credit Scoring: A Review 2000Neural network credit scoring models 2017Machine learning models and bankruptcy prediction 2006Credit scoring with a data mining approach based on support vector machines 1996A comparison of neural networks and linear scoring models in the credit union environment

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