Credit Card Fraud Detection Using Machine Learning

Credit Card Fraud Detection Using Machine Learning

Dr.S.Gnanapriya, R.Arun Kumar

 Assistant professor, Department of Computer Applications,

Nehru College of management, Coimbatore, Tamilnadu, India

ncmdrsgnanapriya@nehrucolleges.com

2Student of II MCA, Department of Computer Applications

Nehru College of management, Coimbatore, Tamilnadu, India

arunkumarr0678@gmail.com

Abstract : Credit card fraud detection using machine learning has become an essential tool in combating the increasing threat of fraudulent transactions in today’s digital economy. Traditional rule-based systems often fall short in identifying sophisticated fraud patterns, leading to either missed fraudulent cases or false positives. Machine learning (ML) offers a more dynamic and scalable solution by analyzing large volumes of transaction data to detect anomalous patterns and classify them as legitimate or fraudulent. This approach leverages various algorithms such as decision trees, logistic regression, random forests, support vector machines, and deep learning models to accurately predict and identify fraudulent behavior. Additionally, advanced techniques such as anomaly detection and ensemble learning can further enhance detection performance. By continuously learning from new data, ML models can adapt to evolving fraud patterns, improving the accuracy and reducing the rate of false positives. This research highlights the role of machine learning in enhancing fraud detection systems, offering a more robust and real-time solution to safeguard financial transactions while minimizing customer inconvenience

Keywords : credit card, criminal transactions, and random forest algorithm