Fraud Detection in Financial Transactions Using Machine Learning Techniques

With the fast growth in online payments, detection of fraud in financial transactions has become an essential problem. Rule-based systems no longer work because fraudulent patterns change over time. This research discusses the use of machine learning algorithms, more specifically ensemble models such as Random Forest and XGBoost, in identifying fraudulent transactions in financial data. Focus is laid on data preprocessing, handling class imbalance by SMOTE, feature engineering, and multi-model evaluation. The suggested methodology provides a scalable and adaptive solution in line with real-time requirements of fraud detection systems for financial institutions.