A Partially Interpretable Adaptive Softmax Regression for Credit Scoring

Lkhagvadorj Munkhdalai,Nipon Theera-Umpon,Oyun-Erdene Namsrai,Keun Ho Ryu

doi:10.3390/app11073227

Abstract

Credit scoring is a process of determining whether a borrower is successful or unsuccessful in repaying a loan using borrowers’ qualitative and quantitative characteristics. In recent years, machine learning algorithms have become widely studied in the development of credit scoring models. Although efficiently classifying good and bad borrowers is a core objective of the credit scoring model, there is still a need for the model that can explain the relationship between input and output. In this work, we propose a novel partially interpretable adaptive softmax (PIA-Soft) regression model to achieve both state-of-the-art predictive performance and marginally interpretation between input and output. We augment softmax regression by neural networks to make it adaptive for each borrower. Our PIA-Soft model consists of two main components: linear (softmax regression) and non-linear (neural network). The linear part explains the fundamental relationship between input and output variables. The non-linear part serves to improve the prediction performance by identifying the non-linear relationship between features for each borrower. The experimental result on public benchmark datasets shows that our proposed model not only outperformed the machine learning baselines but also showed the explanations that logically related to the real-world.

Highlights

Credit scoring is a numerical expression of a borrower’s creditworthiness that is estimated by credit experts based on applicant information using statistical analysis or machine learning models
We summarize the strengths and weaknesses of current credit scoring models, which used machine learning models, and drew some practical issues that serve as a foundation in this work
We aimed to propose an interpretable credit scoring model that can achieve state-of-the-art predictive performance using softmax regression and neural network models

Summary

Introduction

Credit scoring is a numerical expression of a borrower’s creditworthiness that is estimated by credit experts based on applicant information using statistical analysis or machine learning models. Many machine learning models have been developed to achieve higher predictive accuracy for classifying borrowers as bad or good [1,2]. The inability to explain these machine learning models is one of the notable disadvantages. Financial institutions usually want to understand decision-making process of machine learning models to trust them [3,4]. There is still a need for credit scoring model that can improve the predictive performance and its interpretation [5,6]. Machine learning algorithms cannot be adopted by financial institutions and would likely not be accepted by consumers [7]

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Conclusion