Prior to 2018, regulators resisted recommending the use of Machine Learning (ML) based Artificial Intelligence (AI) for AML compliance. There was a mindset shift in mid 2018 indicating that proceeding with caution in implementing AI approaches for AML is appropriate. Regulators realize the adoption of recent innovation, such as the use of AI-ML and robotic process automation (RPA) techniques, enables AML compliance improvements not otherwise attainable. A risk-based approach to compliance, underpinned by AI/Machine Learning, creates opportunities for governance and process refinement as well as identifying potential untapped revenues. Reliance on box-ticking approaches familiar to users of legacy rules-based compliance systems is no longer sufficient.
Westpac Bank in Australia is facing one of its gravest crises from systemic AML/CTF failures over a 5-year period that contravened the AML/CTF Act on over 23 million occasions.
The Legacy Approach
In last week's article, we discussed Rules Engines vs Machine Learning for AML/BSA/CFT compliance. In this article we differentiate legacy AML process from modern a Agile Compliance approach. The legacy AML process consists of on-boarding customers, transaction monitoring and reporting. These processes are hard-wired into the software by technology companies and have been used for years.