Heroes in Business - Experian Identity Report, Identity Graphs with Kevin Chen, Senior Vice President and Chief Data Scientist for Experian DataLabs in North America

Episode Date: December 12, 2022

Experian Identity Report focuses on Identity Graphs with Kevin Chen, Senior Vice President and Chief Data Scientist for Experian DataLabs in North America, interviewed by David Cogan famous host Heroes Show and founder Eliances entrepreneur community. The Experian DataLabs are at the forefront of the company's efforts to scan the horizon for opportunities to disrupt and transform the business with data and more in this episode.    

Up in the sky, look, it's captivating, it's energizing, it's Eliance's Heroes. Eliance's is the destination for entrepreneurs, investors, CEOs, inventors, leaders, celebrities, and startups, where our heroes in business align. Now, here's your host flying in, David Kogan, founder of Eliance's. That's right. And I love flying in because it's just incredible because one of my favorite parts of the show is the Experian Identity Report. That's right. That's where we speak with the world's leading experts about the game-changing technology and impact of identity and the need to use reliable data, really, to make confident decisions that safely accelerate

customer engagement. That's what it's about. And today we have again with us Kevin Chen. He is the Senior Vice President and Chief Data Scientist for Experian Data Labs in North America. Now, the Experian Data Labs are at the forefront of the company's effort to scan the horizon for opportunities to disrupt and transform the business with data. Data is the key. So Kevin, I'm fascinated by our topic today and I really want to jump right into it and talk about the word that I'm hearing about identity graphs. Please explain what that is. Sure, David, before we talk about identity graph, I think we need to talk about what is identity. The identity,

the meaning of identity has evolved over time. So identity right now really is anything that can be used to identify you as a person, right? So for example, traditionally we have the so-called offline identity elements such as name, address, social security number, and so on. But we also have online identity such as cookies, IP address, mobile advertising ID, your Twitter handles, and so on. Now, there's also geolocation of your mobile phones and your biometrics and so on. All these information, some of them are very static and some of them are quite dynamic. And if we are able to bring those identity information together, we'll then be able to identify a consumer and provide

the appropriate service to a consumer. And that's why this identity information is very important. However, if you look at each of the identity elements, such as a name and so on, it's not really sufficient enough to identify a person. For example, if you think about Kevin Chen, there's probably tens of thousands, hundreds of thousands of Kevin Chen in the United States. If you were to look at the address, let's say my address actually have four people in it, an apartment address probably have hundreds of people

living over the past 10 years and so on. Then if you look at the online activity, for me, I have multiple devices, I use multiple browsers, so there's so many different cookies that's associated with me. So it's really hard to just have one identity elements to pinpoint to a specific person. And the other thing is, therefore,

we have to really try to bring multiple pieces of information together in order to figure out who the identity is. And the other issue is for the companies, when they gather the information about identity, depending on how they use the identity information for, they may gather different pieces of information, right? Sometimes for the financial type of information, they may gather your name, address, and social security number. But for, say, CRM-related information, they probably focus more on the phone number, email, and so on. So this identity graph is really trying to bring everything together, trying to create a 360-degree single view of customers so that the business can act on it no matter where your information comes from. Got it. Great. Give us an example, though, too, of how it helps to project people and businesses, how that works. How that works? So let's talk about Experian Identity Solution. Experian Identity Solution, what we call a SYNC identity platform, the core of it is this identity graph that we just discussed.

billions of consumer records that we gather from many authoritative data sources that we collect across business units, plus some very trustworthy third-party data so that we can stitch together this 360 degree view that I mentioned earlier. So how that works is whenever a business that are interested in learning the identity of their consumer, they can send us the consumer's information, which could be a simple combination of all those identity elements that I mentioned earlier, whether it's address, email, name, phone number, and so on. Send us through real-time, through API, and then we'll be able to resolve this identity

and return back to you whether we have seen this consumer or not and how confident we are about these matches in real time so the business can make decisions. And the other benefit of this identity platform that we developed is that because we provide this confidence score. So really, it depends on what you use this information for. For example, if you were to make a LinkedIn decision, you wanted to be very certain about this identity. So you can make sure that you only accept this identity if the confidence score is very high.

However, if you were to do marketing, you're trying to reach out to mass majority of the people as much as possible. Then you will probably lower the confidence of the identity, trying to reach out to the people as much. So that's how usually this identity graph works. Excellent, excellent. And again, we have with us Kevin Chen, Senior Vice President and Chief Data Scientist for Experian Data Labs in North America.

You can reach him at Experian.com because you're listening and watching me. That's right. Host of the Alliances Hero Show. So, you know, the only place to go is Alliances.com. So go after the radio show here and podcast to Eliance's dot com. That's E-L-I-A-N-C-E-S dot com. So, Kevin, this is great. And again, extremely innovative. How did though Experian come up with this whole innovation part? Well, Experian really pays great attention to our customers. pays great attention to our customers. As our customers continue to engage with their consumers, increasing in diverse channels, they realize they really need to have a single

consolidated view of the consumer across the channel so they can offer the service in this omni-channel sense. And they also realize that they wanted to be able to identify the consumers with very small subset of identity information, you know, whether it's name, address, phone number, and so on, so that they can reduce the friction when they interact with the consumer. And we also, you know, experience as a business, we actually have a large business in the fraud detection side. We know that we need to constantly look for ways to bring our knowledge about the consumer's identity. So that's why we wanted to build this identity solution. And we realized that we need to have this holistic solution in order to encompass all the problems I just

described to you. When we started our project, we know that the identity information across Experian is actually quite fragmented and siloed, just as many other businesses, right? And we also know that in Experian, although we have very solid ways of identifying the consumer within each business unit, but each solution is very different from each other. And they cannot really easily be generalized to tackle the problems that I mentioned earlier. Therefore, our lab, when we approached this problem, we decided to take a very holistic view. And then we started to bring in machine learning, trying to figure out how we can consolidate all those information together

to come up with a single solution so that we can handle any kind of data sources that come into our experience for identity resolution, and also any kind of combination of identity elements that's been provided by our customers. And lastly, we also have paid great attention about regulatory constraint, right? Because depending on how you use this solution for,

there's certain data sources you may not be able to use, whether it's due to regulation or it's due to consumer contracts with the clients. So, this identity graph that you and I just talked about earlier actually allows us to very flexibly to remove certain data sources from the participation of this resolution of the identity. Fantastic. Boy, Kevin, you're a wealth of information.

You definitely know your stuff and I really appreciate the time that you've taken out today to come and to share this information. Once again, you've been listening and watching David Coates with S&P, host of the Alliance radio show and podcast. So make sure you go to alliances.com. Thank you again to Kevin Chen, Senior VP and Chief Data Scientist for Experian Data Labs in North America. Make sure that you go to Experian.com, E-X-P-E-R-I-A-N.com. Once again, that's Experian, E-X-P-E-R-I-A-N.com. Thank you so much again, Kevin. Thank you, David. Thank you, everyone.