Asymmetric risk poses a constant threat to banks, fintechs, and other heavily regulated businesses. An incomplete due diligence review on a single customer that misses their involvement in money laundering or other crimes can lead to multimillion-dollar fines, reputational damage and regulatory action at the highest levels of leadership. Because even small errors can produce these outsized consequences, eliminating small gaps in know-your-customer (KYC) processes is essential to protecting both institutions and their stakeholders.

Traditionally, effective KYC and anti-money laundering (AML) compliance has required a comprehensive evaluation of customer risk during onboarding, followed by scheduled monitoring for changes in risk profile or behavior, often through exceptionally manual processes that are prone to delay. Now, AI and automation make it possible to strengthen KYC and enhance AML oversight by using real-time data and enabling a more proactive approach to financial crime prevention.

Operational errors and penalties are happening despite banks’ substantial investment in AML/KYC processes and solutions. Juniper Research put 2024 global KYC spending at $30.8 billion last year. Yet many institutions still rely on manual processing and updating of customer data, which slows down onboarding and delays updates that could flag changes in risk profile.

Automating some of these processes using rules-based robotic process automation (RPA) can speed things up, but may generate high rates of false positives that require more time for manual reviews. Meanwhile, criminals are using advanced technology to avoid getting caught by KYC and AML processes. With AI and stolen or false identity data, they can create documents and histories that look real enough to fool analysts and basic automated systems.

Adding AI-enabled automation and GenAI to RPA can help banks address these challenges in multiple ways.