Fraud Detection System For Banking Transactions Using Machine Learning

The increasing digitization of financial transactions has led to a surge in credit card fraud, posing significant challenges to the banking sector.Traditional rule-based fraud detection systems, being static, struggle to detect sophisticated and evolving fraudulent activities.This research proposes a machine learning (ML)-based fraud detection system capable of accurately identifying fraudulent transactions in real-time.The system employs Random Forest, XG Boost, and Decision Trees within a dynamic, fault-tolerant architecture that automatically switches between models to optimize accuracy and performance.To address the class imbalance issue-where genuine transactions significantly outnumber fraudulent ones-the system uses SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic fraudulent samples.This enhances the model's ability to detect rare yet critical fraudulent activities.The data preprocessing pipeline includes missing value imputation, duplicate removal, and data normalization to ensure the dataset is clean and consistent.Additionally, feature selection techniques are applied to identify key transaction attributes such as amount, location, and frequency, which are essential for distinguishing between legitimate and fraudulent transactions.Experimental results demonstrate that the proposed system achieves high accuracy, precision, and recall rates, outperforming conventional methods.This research highlights the effectiveness of machine learning algorithms in detecting and preventing financial fraud, offering a scalable and real-time solution for banking institutions to enhance security and mitigate financial losses.