International Journal of Science, Engineering and TechnologyFraud Detection in Bank Sector Using Machine Learning
Authors- Mohanraj G, Associate Professor DR. C. Meenakshi
Abstract-– This project explores a comprehensive machine learning approach to detect anomalous financial transactions, specifically focusing on credit card fraud. Utilizing a simulated dataset of customer transactions, the system applies key steps such as data cleaning, exploratory data analysis, feature engineering, and model building. Techniques like SMOTE are used to handle class imbalance, which is common in fraud detection scenarios. Multiple models—including Logistic Regression, Decision Tree, and Random Forest—are developed and evaluated using accuracy, precision, recall, and F1-score. Among them, Random Forest showed superior performance in identifying fraudulent patterns. The project demonstrates how combining data preprocessing, visualization, and robust machine learning algorithms can lead to an effective, scalable, and adaptable fraud detection solution, paving the way for real-time applications and future enhancements using deep learning.
DOI: /10.61463/ijset.vol.13.issue2.410
