ASSESSING CUSTOMER LOAN QUALIFICATION
Keywords:
predictive analytics, identifying loan defaulters, machine learning techniques, performance metrics, sensitivity, specificity, credit approval processesAbstract
Predictive analytics plays a crucial role in identifying potential loan defaulters. Data for this analysis is sourced from Kaggle, where it's used for predictive modeling. Various machine learning algorithms are applied, and their effectiveness is evaluated based on
performance metrics like sensitivity and specificity. Comparison of these models reveals that their outcomes vary. Utilizing machine learning techniques makes it feasible to pinpoint the ideal candidates for loans by assessing their probability of defaulting. The
findings suggest that banks should consider more than just a customer's wealth when making lending decisions. Other significant customer attributes also critically influence the ability to foresee loan defaults. This approach highlights the complexity and importance of multiple factors in credit approval processes
