Financial institutions are increasingly turning to artificial intelligence (AI) and machine learning to help combat money laundering. However, the use of AI also presents challenges, such as the difficulty of pinpointing the origin of data analyzed by AI. Other potential solutions include the use of biometrics, such as "selfie pay," where a consumer has to take a photo of themselves as part of enrollment, making it more difficult for a bad actor to complete a financial transaction using a fake or stolen account.
Key takeaways:
- Financial institutions are facing increasing challenges in preventing money laundering and illicit money movement due to ongoing conflicts, fast-changing regulatory environments, and evolving technology.
- Money laundering regularly reaches 5% of global GDP and financial institutions of all sizes have been hit with fines tied to Anti-Money Laundering (AML) lapses.
- As more money is moving electronically and transactions are being processed faster than ever before, adhering to know-your-customer rules becomes more complicated, especially with the involvement of digital firms, fintech apps, and social networks.
- Artificial Intelligence (AI) and machine learning are being increasingly used to improve AML, with 86% of organizations planning to increase spending on these technologies. However, there are challenges, such as banks disclosing details of suspicious activity that were formed in part by AI.