Financial crimes such as money laundering, fraud, and cybercrime pose a significant threat to both individuals and financial institutions. To effectively detect and prevent these crimes, it is essential to have robust financial crime controls in place. These controls range from manual review processes to advanced artificial intelligence (AI) systems. Financial regulators are increasingly cracking down on financial institutions with poor Anti-Money Laundering (AML) controls. This is because AML is a critical part of financial crime prevention, and regulators are responsible for ensuring that financial institutions take the necessary measures to detect and prevent money laundering. The lack of effective AML controls can result in significant financial losses for institutions and harm the financial system's reputation as a whole. This is why financial regulators are motivated to issue significant fines to institutions with poor AML controls. Using synthetic data to test the effective

Just as vaccines protect our physical health from harmful viruses, synthetic data can protect our financial well-being from financial crimes. In the same way that vaccines use a small, harmless sample of a virus to build immunity, synthetic data uses real-world financial data to create a realistic, yet safe environment for AI systems to learn and detect fraudulent activity. Financial crime, such as money laundering, fraud, and cybercrime, is a growing concern for individuals and institutions alike. These crimes not only harm the victims, but also undermine the stability of the financial system. Traditional methods of detecting and preventing financial crime, such as manual review and rule-based systems, are becoming increasingly ineffective as criminals become more sophisticated. One example of financial crime is the use of mobile money services to launder money, which is a problem that financial institutions are facing globally. One way to tackle this problem is to use synthetic data