The Credit Process Throughout the Years

The loan approval process is one of the most critical activities for lenders. Accurate and timely assessment of applications is essential to minimize the risk of default and ensure the bank’s profitability. However, this process is often manual and requires a considerable amount of time and resources. Due to the challenges that have characterized the credit market and the evolution of credit risk policies and procedures, this chapter explores the evolution of the credit process, emphasizing the critical role of technology in transforming loan approval systems. Conventional manual credit assessment techniques are resource-intensive, time-consuming, and susceptible to bias. These limitations highlight the need for automation to improve operational efficiency, enhance decision accuracy, and ensure transparency. In this perspective, this chapter employs the HistGradientBoosting Classifier , an advanced machine learning (ML) algorithm, to automate the loan approval process, with the goal of providing accurate and explainable decisions. This algorithm is based on the boosting technique, which combines several weak learners to create a stronger model and uses histogram-based techniques to improve the efficiency of gradient boosting. The chapter also assesses some challenges such as the balance between predictive accuracy and explainability, in light of regulatory frameworks such as the EU General Data Protection Regulation (GDPR), which mandates transparency in algorithmic decision-making.