FraudFort: Harnessing Machine Learning for Credit Card Fraud Detection

Abstract

Credit cards are a type of payment cards, issued by banks so that a person can purchase goods or withdraw the required cash. It is a way to make purchases, but as the use of credit cards has increased, particularly for online transactions, the quantity of fraud cases has also risen up. The arrival of inventive technologies and communication methods, like contactless payment, has helped in the increasing problem of credit card fraud. This research paper explores how the fraud of credit card can be detected. Algorithms like Logistic Regression and Random Forest were used in creating Fraud Fort, an advanced system designed to detect credit card fraud. The paper has aimed to enhance the preciseness and efficiency of fraud detection mechanisms by using Logistic Regression as well as Random Forest. After the comprehensive analysis of these algorithms' performance within the context of credit card transactions, this study illustrates the efficacy of integrating both models in Fraud Fort. The results indicate the combined advantages of logistic regression and random forest so that fraud detection system can become strong, eventually leading to a more secure and reliable economic ecosystem.