Published Paper Details:

Credit card fraud detection is a system that detects and prevents fraudulent transactions before they cause financial harm. Credit card fraud refers to fraudulent activities involving payment cards, such as credit or debit cards. It can include physical theft, electronic interception, and online transactions. As credit card usage increases, financial fraud and scams increase, leading to significant losses for banks and customers. Credit card fraud detection is essential to protect users from financial loss, and these approaches help increase the security of financial transactions. Fraud detection and prevention help maintain trust in the payment system. Consumers trust credit cards for their convenience and security. Credit card fraud data is highly unbalanced, with few fraud cases. Extract relevant features from the dataset. Some common features include transaction amount, time of day, and location. Randomly remove instances from the majority class (valid transactions) to balance the dataset. Create synthetic instances of minority class (unauthorized transactions) to balance the data. Advanced algorithms and machine learning techniques analyze transaction data in real time. Credit card fraud detection based on machine learning (ML) such as Decision Tree (DT), Logistic Regression (LR), K- Nearest Neighbour (KNN) algorithms. The choice of machine learning algorithms and the performance of the evaluation metrics used are important factors that influence the accuracy of machine learning algorithms.