Work

Machine Learning Models and Data-Balancing Techniques for Credit Scoring: What Is the Best Combination?

Year: 2022

Type: article

Abstract: Forecasting the creditworthiness of customers is a central issue of banking activity. This task requires the analysis of large datasets with many variables, for which machine learning algorithms and feature selection techniques are a crucial tool. Moreover, the percentages of “good” and “bad” customers are typically imbalanced such that over- and undersampling techniques should be employed. In the literature, most investigations tackle these three issues individually. Since there is little evidence about their joint performance, in this paper, we try to fill this gap. We use five machine learning classifiers, and each of them is combined with different feature selection techniques and various data-balancing approaches. According to the empirical analysis of a retail credit bank dataset, we find that the best combination is given by random forests, random forest recursive feature elimination and random oversampling.

Source: Risks

DOI: 10.3390/risks10090169

Authors: Ahmed Almustfa Hussin Adam Khatir, Marco Bee

Institution: University of Trento

Language: English

Topic: Financial Distress and Bankruptcy Prediction

Subfield: Accounting

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $1,466

Funder: Università degli Studi di Trento