Credit Card Fraud Detection Using Machine Learning

Credit card fraud poses a significant threat to financial institutions and cardholders, resulting in substantial financial losses and compromised security. This study focuses on the development of effective fraud detection systems using machine learning algorithms to mitigate these risks. Leveraging a comprehensive dataset containing transactional information, this research explores the application of various machine learning techniques to identify and prevent fraudulent credit card transactions. Credit card fraud detection is a critical task for financial institutions and merchants, as it can result in significant financial losses and damage to customer trust. Machine learning classifiers offer a promising solution for detecting fraudulent transactions in real-time. This project aims to develop a credit card fraud detection system using machine learning classifiers, which can accurately identify fraudulent transactions while minimizing false positives. The project involves collecting and preprocessing a large dataset of credit card transactions, training several machine learning classifiers, and selecting the best performing model. The final model will then be tested on a new dataset to evaluate its accuracy, precision, and recall. The resulting system can be integrated into financial institutions and merchants' existing fraud detection systems to enhance their ability to detect and prevent fraudulent transactions. This project has the potential to save millions of dollars in financial losses due to credit card fraud and increase customer confidence in the security of their financial transactions. In this project we can implement the framework to study the multiple classifiers such as Random Forest (RF), Linear Regression (LR), Decision tree classifier (DT) and Support Vector Machine algorithm (SVM) in credit card datasets that are collected from KAGGLE source and implement in Python framework.