Making Anti-money Laundering Effective
The United Nations Office on Drugs and Crime estimates that between 2% and 5% of global GDP is laundered each year. That’s between $800 billion and $2 trillion in 2020/2021. Anti-money laundering efforts so far can be seen as ineffective - for every $1500 invested, only $1 of money laundering is recovered. A consortium of banks and industry leaders are suggesting a fundamental change in the approach by introducing distributed private graph networks and federated machine learning to improve the effectiveness of the anti-money laundering process within a pre-approved governance framework backed by legally bounding global data sharing agreements. To start tackling the problem, we suggest targeting individual challenges along the dimensions of outcome data, machine learning, data sharing, and agency interactions. This talk goes into some of the underlying causes as to why, covering multiple perspectives from machine learning to policy, and presents a strategy using brand new and existing technologies.
- Andreas Vermeulen, Consulting Director at Sopra-Steria
Bart Visscher, Head of Analytic Innovation at HSBC