ABSTRACT
This work focuses on the AI powered Transaction Network and Behaviour Analysis for Detecting and Preventing Money Laundering Activities. Materials and Methods: The present study involved two groups. Group 1 refers to an Ensemble algorithm that includes Cat-Boost and Light, while Group 2 refers to a Deep Learning method that uses Long Short-Term Memory (LSTM) networks to evaluate transactional data and detect suspected money laundaring activities. Result: The novel AI Powered Transactional Network and Behavior Analysis to Detect and Prevent Money Laundering Activities using LSTM involves analyzing and interprating the results obtained from the system's implementation and evaluating its performance and best frequency for greatest gaining percentage 99.871%. Conclusion: This research confirms that the novel is a sophisticated system designed to address the challenge of detection and preventing the money laundering activities in bank transaction.
