Predictive Analysis in Banking using Machine Learning

This paper discusses the utilization of machine learning for predicting loan approval and credit card fraud detection. Specifically, proposes use Random Forest Algorithm Support Vector Machine Learning achieving better accuracy. The banking sector's main objective is to ensure their assets are in safe hands, achieve this, a verification process carried out. However, takes long time, there no guarantee selecting deserving applicants. To address this problem, system has been developed that predicts suitability an applicant based on model trained using algorithms. achieved 92% accuracy Algorithm. user interface web application where users can input necessary details predict. system's drawback it considers multiple attributes, whereas, real life, may be approved single strong attribute, which cannot detect. With increasing number online transactions, usage become more prevalent. Losing physical cards or information result significant financial loss. Therefore, need detect fraudulent transactions secure them. issue, Algorithm, focuses analyzing preprocessing data sets deploying detection Credit Card Transaction data.