A Novel Approach for Credit Scoring System for Loan approval using Ensemble learning Algorithm

In order to manage credit risk for financial institutions and help with loan acceptance decisions, credit scoring is essential in determining an applicant's creditworthiness. This research provides an extensive analysis of credit rating for loan approval utilizing ensemble learning algorithms. The project develops and assesses ensemble credit scoring models using a variety of datasets containing financial, demographic, and previous credit data about loan applicants. In order to improve credit scoring, this article looks into feature selection strategies, ensemble parameter adjustment, and model interpretability. The outcomes show how ensemble learning algorithms outperform individual classifiers in credit scoring and produce accurate judgments of credit risk.