Preventing Bank Fraud Using Intelligent Machine Learning Models

Abstract

The presence of vulnerabilities in banking systems has rendered us susceptible to fraudulent activities, resulting in significant financial and reputational harm for both clients and the bank. Financial institutions suffer substantial financial losses each year due to financial fraud. Early identification of this issue aids in mitigating fraudulent activities by formulating a proactive approach and recuperating any monetary damages incurred. This research introduces a machine learning methodology that hass the potential to greatly assist in the precise identification of fraudulent activity. The use of AI-driven technique will expedite the process of verifying checks in order to combat counterfeiting and minimize the consequent harm. This paper provides a comprehensive analysis of many intelligence algorithms that were trained using a publicly available dataset. The objective was to determine the relationship between certain characteristics and the occurrence of fraudulent behavior. In this study, the dataset undergoes resampling to tackle the issue of class imbalance. Afterwards, the suggested method is used to assess the dataset in order to improve accuracy.