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.
Charlie Delingpole: I am the founder and CEO of ComplyAdvantage. We are a company using machine learning and artificial intelligence to combat the risk and threat of money laundering, terrorist financing, human trafficking, and all other related financial crime challenges.
Sramana Mitra: How do you do that?
Charlie Delingpole: The key thing we are doing is that we are trying to build an index or a database of everyone who is high-risk. That number is something like 15 million people and companies involved in money laundering, human trafficking, and terrorist financing.
This also includes people who are sanctioned, corrupt, politically exposed, and also everyone who has adverse media against them. That’s the whole broad spectrum. We are trying to cover every person and company in the world and determine every risk about them.
Sramana Mitra: Who are your customers?
Charlie Delingpole: Right now, we have customers in over 80 countries from Venezuela to Papua New Guinea. We are also involved in companies in different industries. These include companies that have brokerages and lending.
It’s primary where the money is being moved but also where there are reputational risks in terms of onboarding clients, account parties, and employees. That’s the whole spectrum.
Sramana Mitra: This is a B2B solution?
Charlie Delingpole: Exactly. We work with companies who then onboard consumers or businesses. The primary thing that we offer is an API. We produce data that is then exposed by an API. When you are onboarding a customer, you are monitoring their behavior. You want to know on day 1 if the customer is a bad or a high-risk client.
Sramana Mitra: Let’s double-click down one level and let’s do some use cases. All these different types of scenarios that you just rattled out, let’s take a few and let’s double-click into each of them. Explain to me what is happening and also explain the technology that is powering this kind of impact.
Charlie Delingpole: What we are looking at is hundreds of thousands of data sources. We go from the simplest with sanctioned list from the US Treasury Office of Foreign Assets Control that is used to determine which counterparties have asset freezes or travel bans.
That could be the prime minister of Venezuela, an Iranian scientist involved in a nuclear program, and it could be an associate of Vladamir Putin. We are looking at local government in China where an official could be exploiting funds to every newspaper article or every website in the world. We then automatically extract all the entities using a thing called natural language processing.
If you are reading bits of text, then you would want to be able to understand them. For example, you like football and the text says, “Lionel Messi terrorizes the defense and shoots a goal.” That is different if the text says, “Lionel Messi led a terrorist attack and shot a policeman.”
The key application of artificial intelligence is the ability to extract meaning from unstructured text and then build structured profiles of entities. This then can be used to triage and manage risks in the financial sector and other sectors.