A Comparative Performance Assessment of Ensemble Learning for Credit Scoring

Yiheng Li,Weidong Chen

doi:10.3390/math8101756

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.

Highlights

Charging interest on loan is the main business income way of financial institutions and in the granting loans has great contribution in socioeconomic operation
The comparison is based on five aspects, namely, accuracy (ACC), area under the curve (AUC), Kolmogorov–Smirnov statistic (KS), Brier score (BS), and model operating time
The main insight of this paper is an experimental supplement in the debate on the preferred models for predicting credit risk

Summary

Introduction

Charging interest on loan is the main business income way of financial institutions and in the granting loans has great contribution in socioeconomic operation. Emerging and developing economies urgently need to strike a careful balance between promoting growth through lending loan and preventing credit risk caused by excessive releasing according to the Global Economic Prospects [1] from the World Bank in 4 June 2019. The purpose of credit scoring model is to solve problems of classifying loan customers into two categories: good customers (those are predicted to be fully paid within the specified period) and bad customers (those predicted to be default). Banks and financial institutions pay more and more attention to the construction of credit scoring models, because even a 1% increase in the quality of the bad credit applicants would significantly translate into remarkable future savings for financial institutions [3,4]

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