Bank Loan Prediction System By Using Machine Learning

In the banking and financial industry, the loan approval process is crucial and affects both lending institutions and loan applicants. This procedure has always been manual, depending on the knowledge of loan officers to evaluate different aspects and make wise judgments. On the other hand, manual processing takes time, is prone to bias, and can result in inefficiencies in the lending process. By enabling automation and optimization, the use of machine learning techniques has changed the loan approval process in recent years. This work presents the integration of the Artificial Bee Colony (ABC) method with Random Forest to further improve prediction accuracy, in addition to evaluating SVM and Random Forest. The population-based optimization algorithm ABC was inspired by honeybee foraging techniques. In order to improve the Random Forest model’s ability to predict loan approval status, ABC iteratively explores the solution space and updates candidate solutions based on their fitness. This process aims to identify the ideal set of hyperparameters for the Random Forest model.