Ensemble Learning Approaches for Fraud Detection in Financial Transactions

Banks must adapt to an ever-changing fraud landscape. This research employs ensemble learning techniques to increase the precision of fraud detection. This study demonstrates that the Random Forest ensemble method is effective at detecting fraudulent financial transactions. Using a large dataset and cutting-edge feature engineering, this demonstrates how Random Forest can be used to improve fraud detection by identifying subtle connections and patterns in transaction data. The proposed work findings indicate that ensemble learning is effective in this context, substantially enhancing model precision, recall, and overall performance. This research augments the antifraud resources already available to financial institutions. Using Random Forest as its foundation, the proposed work suggests a path forward for advancing fraud detection systems.