Statistical Methods in Credit Risk Prediction: Analyzing Risk through Data Analytics

Abstract

This study explores the factors influencing loan approval decisions by financial institutions using logistic regression analysis. By examining a dataset of loan applicants, we identify key predictors that significantly impact the likelihood of loan approval. The analysis reveals that higher income, larger family size, higher education levels, possession of a CD account, use of online banking, and having a credit card are positively associated with loan approval. Conversely, the presence of a securities account has a more complex relationship with loan approval likelihood. These findings suggest that financial institutions favor applicants with stable financial profiles and higher levels of financial engagement. Understanding these factors can help improve risk assessment models, ensuring more accurate and fair loan approval processes.