Assessing the Impact of Credit Score and Employment Stability on Loan Approval Using Logistic Regression

This study investigates the determinants of loan approval decisions using a Logistic Regression approach based on applicants’ financial and employment characteristics. The dataset consists of key predictors, including income, credit score, loan amount, years employed, and points, which were analyzed to assess their influence on loan approval outcomes. Data preprocessing was conducted through z-score normalization, and the dataset was divided into training (80%) and testing (20%) subsets. The Logistic Regression model demonstrated exceptional predictive performance, achieving perfect values across all evaluation metrics, including Accuracy (1.000), Precision (1.000), Recall (1.000), F1-score (1.000), and ROC-AUC (1.000). These results indicate that the model was able to perfectly distinguish between approved and rejected loan applications. Further examination of model coefficients and odds ratios revealed that credit score and points were the most significant predictors positively influencing loan approval probability, while loan amount exhibited a negative relationship. The findings emphasize that creditworthiness and institutional scoring systems play a dominant role in financial decision-making, whereas income and employment history have a moderate but supportive influence. Although the model’s perfect performance highlights strong predictive capability, it may also reflect a highly structured or synthetic dataset, suggesting the need for validation using larger and more diverse samples. The study contributes to the growing literature on data-driven financial analytics by demonstrating that Logistic Regression remains a powerful and interpretable tool for assessing credit risk and improving loan approval transparency.