How Is Graph Database Used to Combat Money Laundering? ~ by Mingxi Wu, CEO, TigerGraph

Anti-Money Laundering (AML) practices are crucial in the financial sector for preventing illicit activities. Graph databases play a significant role in implementing these practices by analyzing relationships between commercial and consumer accounts, as well as their associated transactions. By using graph databases, financial institutions can identify suspicious activities indicative of money laundering more effectively. The steps involved in combating money laundering include entity resolution, generating alerts based on violated rules, calculating risk scores for account groups, and creating alert entities for further investigation by fraud analysts. Graph databases offer advantages such as easier entity resolution, avoidance of ad hoc joins, scalability, performance, advanced aggregation support, and mutability, making them a valuable tool in the fight against money laundering.