Application and improvement of algorithms in machine learning based loan review system

Currently, there is a considerable growth in the number of applications for loans of all forms. This is attributable to increased economic activity and lower interest rates on loans. However, although these loans create substantial money for financial institutions, they also pose a huge risk. The traditional approach to loan applicants includes auditors assessing them based on their prior employment experience; however, this method has the problem of being sluggish and imprecise. This article describes the creation of an intelligent loan evaluation system by inventively using oil consumption data. The system uses a decision tree model to fulfill the aim of constructing a system that can predict the rate of bad debt acquired by loan customers. As a requirement for system building, solutions to the following innovative difficulties have to be discovered. To get substantial results, a feature derivation approach and formula are used to the decision tree model. While significantly increasing review speed, the poor rate falls from 7.8% to 1.6%, while the approved rate rises from less than 17% to more than 45%. At the conclusion of this study, we attempt to enhance the method by using an ensemble approach, resulting in an overall performance improvement of roughly 5%.