CREDIT CARD TRANSACTION ANALYSIS AND FRAUD DETECTION USING MACHINE LEARNING

Abstract

The analysis of credit card transactions has become increasingly important in today’s digital financial ecosystem, where online payments and cashless transactions are rapidly growing. Financial institutions generate large volumes of transaction data daily, containing valuable insights into customer spending behavior and potential fraudulent activities. Identifying patterns and detecting anomalies in such data is essential for improving financial security, enhancing user awareness, and supporting better decision-making. This project presents a Credit Card Transaction Analysis and Fraud Detection using Machine Learning system developed using Python programming language. The system analyzes transaction data to understand spending patterns, classify expenses, detect fraudulent activities, and segment customers based on their behavior. The dataset includes transaction details such as time, amount, and anonymized features, which are processed and transformed into meaningful information for analysis. The project follows a complete data analytics pipeline, including data preprocessing, exploratory data analysis, feature engineering, behavioral segmentation, and anomaly detection. Machine learning techniques such as clustering (K-Means) are used to group customers based on spending behavior, while statistical methods like Z-score and anomaly detection algorithms are applied to identify suspicious transactions. Additionally, the system incorporates interactive data visualization using tools like Streamlit, enabling users to explore insights such as spending trends, fraud patterns, and customer segmentation through dashboards and charts. These visualizations make complex financial data easy to understand and interpret.