Reduce AML Investigation Costs with TigerGraph

Anti-Money Laundering (AML) involves laws, regulations, and procedures aimed at preventing and detecting money laundering. Financial institutions are increasingly using machine learning to sift through alerts for suspicious activity due to the complexity of financial transactions and the need to identify changing patterns used by criminals. Graph machine learning is particularly useful in this context as it analyzes relationships between entities such as individuals and flows of money between accounts, which can help detect money laundering activities. By incorporating graph features into traditional machine learning models or using native graph ML models like GNNs, financial institutions can reduce false positive alerts and enhance investigative accuracy.