Credit Card Fraud Detection using Machine Learning

Credit Card Fraud Detection using Machine Learning

D.Ravi Chand B.tech,Student,22951A04D8 Deprartment of Electronics and Communincation Enginnering Institute of Aeronautical Engineering Hyderabad,India mailto:22951a04d8@iare.ac.in

Sabiya B.tech,Student,22951A04E4 Deprartment of Electronics and Communincation Enginnering

Institute of Aeronautical Engineering Hyderabad,India mailto:nn4021250@gmail.com

Mr.V.Devender Assistant Professor

Deprartment of Electronics and Communication Engineering.

Institute of Aeronautical Engineering Hyderabad,India mailto:devender.voodara@gmail.com

A.Sai Deepak Reddy B.tech,Student,22951A04E6 Deprartment of Electronics and Communincation Enginnering Institute of Aeronautical Engineering Hyderabad,India mailto:22951a04e6@iare.ac.in

Abstract—The early identification of credit card fraud is an essential need in today’s banking and financial environment, as the number of fraudulent transactions is steadily rising with the growing popularity of online payment systems, e-commerce sites, and digital wallets. The fraudulent transactions not only result in economic losses but also impact the customers’ confidence in online payment services. This paper proposes a comprehensive framework for the identification of credit card fraud based on the analysis of past transaction data through machine learning algorithms. The proposed method aims to detect unusual transaction patterns, including erratic spending habits, sudden geographical shifts, and irregular transaction rates, which are usually linked to fraudulent transactions.

The system combines data preprocessing, feature selection, normalization, and the use of several supervised machine learning algorithms to enhance the performance of fraud detection. Different classification algorithms are trained on past transaction data to enable the accurate separation of valid and fraudulent transactions. Given the fact that fraud transaction datasets are imbalanced, the necessary data balancing methods are employed to ensure that the models are able to learn the rare instances of fraudulent transactions. Feature engineering is an important component of the system aimed at improving the performance of the models by identifying important transaction features. The performance of the system is measured using different metrics such as accuracy, precision, recall, and F1- score.

The proposed fraud detection system is developed using a machine learning approach and is capable of supporting real- time transaction analysis in banks. The system compares the performance of various machine learning models to identify the most efficient and scalable solution for fraud detection. The proposed system can help banks make faster and more accurate decisions during the transaction process. This research work proves that machine learning-based fraud detection systems can play an important role in enhancing financial security and customer confidence in digital payment systems.

Keywords— Credit Card Fraud Detection, Machine Learning, Fraudulent Transactions, Financial Security, Class Imbalance, Supervised Learning, Random Forest, Support Vector Machine, Digital Payments