CREATE A SYSTEM THAT USES MACHINE LEARNING TO DETECT FRAUDULENT ACTIVITIES IN FINANCIAL TRANSACTIONS

The complexity and number of financial transactions have grown dramatically in today's quickly changing digital economy, which has resulted in a rise in fraudulent activity. This project suggests creating an intelligent system that uses machine learning methods to efficiently and accurately identify financial transactions. The system makes use of supervised learning algorithms that have been trained on transaction data from the past, which includes information on the location, time, amount, and user behaviour patterns. By identifying anomalies and learning from both legitimate and fraudulent patterns, the model can detect fraud in real time, reducing false positives and guaranteeing prompt action against threats. To adjust to changing fraud strategies. training, and feature ongoing assessment. To find the best strategy, important machine learning models including Random Forest, XGBoost, and Neural Networks are benchmarked. Because of its scalable design, the system can be implemented in settings with significant transaction volumes, including banks and e-commerce sites. This solution improves operational security and user confidence while lowering financial losses through faster and more accurate fraud detection. Future developments could involve the use of unsupervised learning for the early detection of fraud patterns and blockchain integration for improved traceability. Future developments could involve the use of unsupervised learning for the early detection of fraud patterns and blockchain integration for improved traceability.