Developing machine learning models for automated credit scoring and loan approvals

In the financial industry, the automation of processes has become more prevalent in the last decade. More specifically, credit scoring and loan decisions, traditionally dependent on subjective decisions by credit officers, have also begun to follow automated processes, especially in the case of smaller credit or loan requests. Automated procedures to assign credit ratings or make credit decisions are largely based on the statistical evaluation of previous events, and statistical models have served to structure and inform the decision of whether to grant credit in the proportional sense, related to the size of the decision, or as a binary decision taken with the model results and threshold values. The recent trends generally aim to reduce turnaround time of credit applications and the probability of changes in loan approval decisions. Nevertheless, from a consumer’s perspective, accurate credit scores are also essential in avoiding excessive rejection rates or the risk of over-indebtedness due to lenders being too lenient.