Application of Machine Learning in Credit Scoring for Loan Approval Systems

Credit scoring forms a crucial part of loan approval systems, and it will evaluate the creditworthiness of applicants. Traditional methods sometimes make wrong judgments, for example, letting risky customers into the system and rejecting creditworthy ones, since this follow fixed, rule-based approaches. Machine learning will provide an evidence-based solution to these problems because, above all, it is adaptive. This paper develops a new hybrid ensemble model that integrates Gradient Boosting and Random Forest techniques to enhance the accuracy of lending approval systems based on credit scores. The proposed methodology enhances forecast accuracy by capturing complex relationships between financial attributes in addition to handling imbalanced information. The hybrid model is demonstrated to be better than standalone models of RF and GB in terms of precision, recall, accuracy, and AUC with experimental results on real-world loan datasets. Therefore, financial organizations are likely to minimize credit risks while simultaneously streamlining the efficiency and equity of the loan approval processes by implementing this integrated and scalable approach. The hybrid model achieved a accuracy of 92.3 % and performs better than RF and GB.