Loan Approval Prediction Based on a Hybrid Approach of Dynamin Thresholding Genetic Algorithm and Support Vector Machine

The loan eligibility prediction model utilizes an analytical approach that adjusts previous and current credit user data to provide forecasts.An important challenge in predicting loan eligibility is accurately forecasting loan outcomes via risk assessment and evaluation analysis.In the nowadays the Predictions of loan approvals now require the utilization of machine learning.Financial institutions are seeking methods to automate the loan approval process while minimizing risk in response to the rising demand for credit.This article introduces a novel application of machine learning methods to predict loan approval.The research centers on various algorithmic architectures, neural networks, support vector machines, decision trees, and random forests.Furthermore, the paper addresses the obstacles encountered in applying Artificial Intelligence (machine learning (ML) algorithms) for predicting loan approval.Moreover, for feature selection, the article proposes a dynamic thresholding genetic algorithm (DTGA) based on loan approval prediction.Besides, we emphasize the importance of data quality and feature selection in designing an effective ML model for loan approval prediction.Compared to conventional approaches, performance evaluation of the DTGA demonstrates the GA feature selection can substantially enhance the accuracy of loan approval prediction.Therefore, this article contributes to utilizing ML models for predicting loan approvals and the potential ramifications.Consequently, it enhances the decision-making procedures of financial institutions.