Thought Leaders in Artificial Intelligence: Charlie Delingpole, CEO of ComplyAdvantage (Part 5)

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: That is more within money laundering risk. The key part of finance is the pricing of risk. Just because there is a risk of default or a risk of a claim, it doesn’t mean that you have to abandon that fully. A key part of finance is understanding the risks.

Thought Leaders in Artificial Intelligence: Divyabh Mishra, CEO of CrowdANALYTIX (Part 2)

Divyabh Mishra: We do about 10 million API calls per day for one of our customers. For many others, it’s not that huge, but it is still pretty large. We also work with a lot of distributors. We have a lot of B2B clients like electrical or industrial distributors. They have a huge catalog.

For anyone that has a huge catalog and cannot structure this content by hand, there is no other alternative. It’s expensive to do it manually. Our approach of 80% to 85% automation and the rest manual is driving the growth. Those are our current use cases.

Thought Leaders in Artificial Intelligence: Divyabh Mishra, CEO of CrowdANALYTIX (Part 1)

Super interesting crowdsourced data model conversation!

25,000 data scientists participating in building algorithms for specific use cases.

Fascinating conversation!

Thought Leaders in Artificial Intelligence: David Talby, CTO of John Snow Labs (Part 7)

David Talby: We have two customers and two different specialties who do that on top of what we do. Quite a few companies have been building on top of our library to sell to different markets.

We don’t intend to compete with our own customers. We intend to stick to that pass layer and focus on data science. There is a lot more work to be done on multi-model learning and understanding images. There is a lot more that we can do in the space. 

Thought Leaders in Artificial Intelligence: Charlie Delingpole, CEO of ComplyAdvantage (Part 4)

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 those out?

Charlie Delingpole: There are government policies around sanctions. You have different factions programmed. You have North Korea, Venezuela, and South Sudan, for example. Part of it is geopolitical to the extent that there is wide UN or US disdain for a particular group. That then merits the nuclear option of sanctions. 

Thought Leaders in Artificial Intelligence: Charlie Delingpole, CEO of ComplyAdvantage (Part 3)

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.

Thought Leaders in Artificial Intelligence: David Talby, CTO of John Snow Labs (Part 6)

Sramana Mitra: How do you go to market? Is this a professional services go-to-market strategy? Technology-enabled services kind of strategy? 

David Talby: It’s a mix. It’s about half and half. We do software licensing and we also do professional services. 

Sramana Mitra: Is the professional service yours or is it outsourced or tied up with system integrators?

Thought Leaders in Artificial Intelligence: Charlie Delingpole, CEO of ComplyAdvantage (Part 2)

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.