Best Practices for Responsible Machine Learning in Credit Scoring

For individuals and families, access to affordable credit is essential as protection against financial volatility, financing and education, pursuing business opportunities, and building equity. From the lender's perspective, there is a delicate balance between improving access to credit and higher costs due to defaults on payments. Creating responsible credit concession models requires maintaining this balance [Kozodoi et al., 2022] while ensuring fair outcomes across different groups of individuals, improving access, and helping applicants understand factors that influence rejection so that they can take action to improve their credit potential. Credit concession models are created using a variety of data, such as employment history (for example, occupation and income), demographic data (such as age, marital status, and education), and financial data (for example, checking account balance, credit card usage, and bill payment history). Given these features, models such as logistic regression, gradient boosting, and decision trees can be trained to predict whether a new customer will default on a loan over a period of time [Louzada et al., 2016].