Credit Card Fraud Detection using Machine Learning

HARIKA G; NAVEEN SAI V; GURU BABU V; DIVYA S; SIVA SANKAR S

Credit Card Fraud Detection using Machine Learning

Author(s)	Ms. HARIKA G, Mr. NAVEEN SAI V, Mr. GURU BABU V, Ms. DIVYA S, Ms. SIVA SANKAR S
Country	India
Abstract	Nowadays, we are using credit cards a lot for online and offline payments instead of carrying money. Because of this, fraud transactions using credit card has also increased. Fraud means using someone’s credit card details wrongly, which causes big money losses to card owner, banks, companies, and customers. So, it is very important to detect fraud quickly before losing more money. In this project, we use machine learning to check whether the transaction is real or fake. Here, we use real credit cards data is used for testing. Since fraud transactions are very few and different format compared to normal transactions. Different machine learning models are used to get better results and the system is tested using accuracy, precision. The results show that this method can detect fraud transactions better. Our research also explains that old rule-based systems are slow and not suitable for today’s large amount of transaction data. So, we used algorithms like SVM, Logistic Regression, Random Forest, XG Boost, Decision Tree methods to improve fraud detection. These models can find hidden patterns and suspicious activities more accurately. By using these algorithms, we build a machine that checks credit card transactions using trained data and helps to detect fraudulent transactions in order to prevent financial losses. In this Project, Machine learning techniques are used to identify whether a transaction genuine or fraudulent. Real transaction data is used for testing the system. Since fraudulent transactions are very few compared to normal transactions, detecting them is challenging. To handle this problem, different machine learning models are applied and compared. The system studies transaction details such as amount, time, location and spending behaviour. Algorithms Decision Tree, Logistic project Regression, Support Vector Machine, Random Forese and XG Boost are used to analyse the data. These methods perform better than traditional rule-based systems because they can learn patterns from past transactions. The results show that the proposed system can detect fraud more accurately and help reduce financial losses.
Keywords	SVM, Logistic Regression, Random Forest, XG Boost, Decision Tree, Credit Card, Transactions, Payments, Banks, Fraud Transactions.
Field	Engineering
Published In	Volume 17, Issue 2, April-June 2026
Published On	2026-04-03

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International Journal on Science and Technology

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Credit Card Fraud Detection using Machine Learning

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