Proactive Money Laundering Preventing with Transactional Network Monitoring

Money laundering is a significant and far-reaching financial crime that jeopardizes global security and financial integrity. Money laundering facilitates crime by disguising illicit proceeds as legitimate while also manipulating the financial system through various illegal schemes, including terrorism financing, commercial bribery, international corruption, drug trafficking, and fraud. Although anti-money laundering (AML) programs do exist, currently deployed transaction monitoring systems that detect money laundering activity attempt to address the financial crime but are overwhelmed by excessive false positives, reliance on high-touch processes for detection, and an inability to readily adjust transaction monitoring protocols for the ever-evolving method(s) of laundering money. This paper describes ML-Bot, an advanced, early warning method of detecting and deterring suspicious behaviour in (near) real-time with transactional network analysis (TNA) and deep learning. ML-Bot will utilize Long Short-Term Memory (LSTM) neural networks to highlight the transaction analysis using LSTM modelling to identify anomalous transactional behaviour associated with money laundering through transactional pattern recognition. Additionally, the framework includes complementary elements associated with behavioural analysis and feature engineering that contribute to a parallel process adapted for real-time alerting systems for regulators. The results of the experiment show that this approach is much more efficient and effective than traditional rule-based monitoring systems, as ML-Bot has higher accuracy with lower false positive rates in distinguishing between legitimate transactions and "illegal" transactions. The study shows that AI based compliance monitoring systems can strengthen compliance protocols, reduce operating costs, and enhance a financial institution's protection from known financial crimes.