What is the role of artificial intelligence in identifying AML typologies?
During this webinar, in partnership with Napier, we looked at financial crime typologies, predicate crimes and how artificial intelligence can help.
About the event
A huge amount is known about predicate crimes and how criminals use the financial system to legitimise their ill-gotten funds, though there are still unsatisfactory recovery rates of these gains with as little as 1% of illicit transactions in Europe being recovered.
For both financial institutions and law enforcement, crime convergence makes it harder to make the links between money laundering red flags and predicate crimes, exacerbating the already challenging process of following the money. Having a better understanding of how these crimes manifest in patterns of money-laundering behaviour will improve risk identification and investigative activities.
During this webinar, in partnership with Napier, we explored:
- The crimes behind the crime – with a focus on corruption and kleptocracy and the revelations from the Pandora papers
- How typologies of financial crime are currently researched and shared
- What some of the limitations are of these methods
- What the implication of this is on the effectiveness of AML controls
The role that artificial intelligence can play in identifying existing typologies and identifying new ones
Speakers at the webinar
Dr Sian Lewin
Co-founder and Head of Client Delivery
Dr Janet Bastiman
Chief Data Scientist,
Investigative Journalist and author of ‘Moneyland’
Senior Managing Director – Technical Services,
This event is in partnership with
Expert sessions with Oliver Bullough
Hear from Oliver Bullough, investigative journalist and author of Moneyland, as he discusses living in Russia, kleptocracy and the steps we need to take to help with anti-money laundering efforts.
Watch the highlights from the webinar
During this one hour webinar, we’ve pulled the key takeaways from each section including: