Graph databases are being used by companies to solve complex problems, including anti-money laundering efforts. Traditional technologies struggle to connect the dots across multiple intermediate steps, requiring manual laborious effort from inspectors. Graph technology is better suited for this task, as it can recognize complex data relationships in real-time and accommodate new data sources without rewriting the data model. A money transfer service company used Neo4j graph database to detect "smurfing" activity, which thrives on splitting large sums of illicit funds into a hidden network of beneficiaries, allowing them to pursue criminal cases 20 times faster than with traditional tools. The use of graph technology provides a powerful weapon against the murky world of money laundering and embezzlement, enabling companies to improve detection and investigatory processes.