Enhancing AML Efficiency and Effectiveness: Artificial Intelligence Transforms the Rules of the Game
Financial institutions have started using RPA, AI, and machine learning tools in AML. Experience from initial use cases suggests potential for significant improvements.
Key research questions
- What are the critical considerations for adopting AI solutions in AML?
- How are AI solutions being adopted in AML?
- How should banks think about putting AI into action?
This report discusses how banks are adopting new tools and technology such as artificial intelligence, machine learning, and robotic process automation in financial crime compliance operations. Based on interviews with several banks, it highlights benefits seen from early adoption and offers lessons for those considering this journey.
Advanced analytics and intelligent automation are being applied at several stages within anti-money laundering (AML) operations. Large global and regional banks are driving AI adoption with early focus on implementing point solutions. With growing maturity of users and solution providers, we expect leading banks to expand scope and conduct even more complex tasks.
Efficient data management is essential because AI solutions involve voluminous and diverse data sets. Model governance, validation, and documentation are paramount because of strict regulatory requirements.