Sramana Mitra: You are based in London, right? Charlie Delingpole: I’ve been in the same room where I started it all. I’ve been in my garage for the past 14 years, but yes, I am in London. We have clients in 80 countries. We have a team in New York, Transylvania, and Singapore. Sramana Mitra:
Sramana Mitra: It seems like there is one class of accounts or data objects that is rejected out immediately based on heuristics that you have. There is also another body of behavior monitoring and actions based on behavior monitoring. Are these the only two categories or is there any other kind of intervention? Charlie Delingpole:
Sramana Mitra: I’m thinking about what you said about these lists of people who have been sanctioned that you can access easily. What are the spheres of influence of these people? There are corruption rings all over the world. There are terrorist rings, drug cartel rings, and all other questionable groups. How do you map
Sramana Mitra: How easy is it to access the sanctioned list in the structured category? Charlie Delingpole: That is easy because the US Treasury makes it publicly available. The real challenge is name matching. In Latin alphabets, it’s easy to name match because you can do simple heuristics.
Sramana Mitra: Am I understanding it correctly if I say that you are doing natural language processing on publicly-available news coverage from around the world to identify names of people who are involved in questionable activities? Charlie Delingpole: That is correct.
This conversation explores the use of AI to create a database of questionable players to address money laundering and other shady behavior. Excellent PaaS strategy! Sramana Mitra: Let’s start by having you introduce yourself as well ComplyAdvantage.