What's Up with Tech?
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With over three decades in telecom and IT, I've mastered the art of transforming social media into a dynamic platform for audience engagement, community building, and establishing thought leadership. My approach isn't about personal brand promotion but about delivering educational and informative content to cultivate a sustainable, long-term business presence. I am the leading content creator in areas like Enterprise AI, UCaaS, CPaaS, CCaaS, Cloud, Telecom, 5G and more!
What's Up with Tech?
Data Giants Aren't Dinosaurs: Experian's AI Leap
Interested in being a guest? Email us at admin@evankirstel.com
Experian's dramatic evolution from traditional credit bureau to technology innovator takes center stage in this fascinating conversation with Kathleen Peters Chief Innovation Officer who leads Experian's Innovation Lab. She reveals how the 15-year-old lab has become the beating heart of technological advancement at the company, bringing together PhD-level scientists, engineers, and data experts to tackle the financial industry's most challenging problems.
The discussion unveils Experian's surprisingly deep history with artificial intelligence – their teams have been developing machine learning models and neural networks for over a decade, long before generative AI captured public attention. This foundation gave them a significant advantage when implementing cutting-edge solutions like their Experian Assistant, an agentic AI tool that can autonomously complete complex tasks for business users without requiring specialized data science knowledge.
What makes Experian's approach particularly noteworthy is their dual commitment to pushing technological boundaries while maintaining rigorous data protection standards. As Kathleen explains, being responsible stewards of sensitive consumer information is embedded in the company's DNA. Their AI Risk Council and dedicated compliance frameworks ensure innovations remain ethical and unbiased, especially crucial when developing credit risk assessment tools that impact consumers' financial lives.
Looking toward the future, Kathleen shares exciting developments in human-AI collaboration, energy-efficient computing approaches, and even quantum computing research that could revolutionize encryption technology. The conversation challenges the common misconception that established financial companies can't lead in technological innovation – Experian proves legacy organizations can transform themselves into digital pioneers while leveraging their unique data assets and global reach.
Curious about how AI is reshaping financial services or how established companies can successfully navigate digital transformation? This episode provides invaluable insights from one of the companies at the forefront of this revolution. Subscribe now to hear more conversations with technology leaders who are building tomorrow's financial ecosystem.
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Hey everybody, Fascinating chat today, looking at a side of Experian that you may not be familiar with. Kathleen, how are you?
Speaker 2:I'm doing really well. It's great to be with you, Evan.
Speaker 1:Well, thanks for joining, really intrigued by the work you're doing. And let's start with the big picture. Of course, you were once known as a credit bureau, but that's evolved over the years, over the decade. Tell us about that transformation. How would you describe Experian these days?
Speaker 2:Yeah, these days, definitely, Experian is a technology company and our mission is really to serve both our clients as well as consumers, to help open new opportunities and really create financial inclusion for all. I know we've had a reputation in the past as being a data company, but certainly over the last decade plus, we've gone through a transformation in that regard where we really are technology first and it drives a lot of what we do in order to achieve our mission for sure.
Speaker 1:Fantastic and you lead Experience Innovation Lab. Maybe talk about the lab, what its role is and what its idea is for the future?
Speaker 2:Yeah, in my current role I'm really excited to be able to lead that innovation and strategy.
Speaker 2:I partner very closely with our chief scientist, and that's Dr Shanji Zhang, and Shanji leads the team of PhD engineers, data scientists and software engineers who comprise our innovation lab.
Speaker 2:So we just recently celebrated the 15th anniversary of the lab, and even that has gone through a transformation.
Speaker 2:So when Shanji co-founded the lab, we called it Experian Data Labs and the idea was to bring together these PhD level data scientists from top universities to really understand what were the insights available from the data that we were collecting and caretaking.
Speaker 2:But then the idea was to ensure that our clients could feel comfortable and confident in reaching out to Experian to help solve some of their trickiest problems, to Experian to help solve some of their trickiest problems. And so a number of our clients, especially in financial services industry, would have data. They would have some problems that they were trying to solve and said, experian, you've got data also, but what could we do here? And by bringing the greatest minds together from both sides, we were able to experiment and research and find some really creative new ways to tackle those trickiest problems, and that ended up being insightful and beneficial to our clients, adding value to them, and then ultimately also to the consumers that they serve, as well as the consumers that we serve ourselves. So that's been a real key part of attracting the talent that has led to technology teams and groups and focus across our business and not just in the lab today.
Speaker 1:Fantastic, and could you share an example or an anecdote on something that went from idea to making an impact in the lab?
Speaker 2:Sure, absolutely. I mean. A lot of it really started with AI, which is obviously a very hot topic for all of us these days. At Experian, with the lab, the data scientists have actually been working with machine learning, artificial intelligence, neural networks for over a decade, and so some of the initial work that they did there led to a lot of the predictive analytical models that are used in financial institutions across the world today for predicting credit risk, risk of fraud, etc.
Speaker 2:A lot of that initial work really helped us to develop a core expertise in AI to where it's in nearly everything that we do today. But then, if you think a couple of years back now, when the world was introduced to the publicly available generative AI generative AI wasn't invented then. It was around for some years and this is again things that our lab was experimenting with. But what really changed a couple years ago was a democratization of that technology. You no longer needed to have a PhD in data science to be able to harness the capability of generative AI and we also had the compute that was able to support it now as well.
Speaker 2:So when that first became available, we literally had thousands of employees who were jumping on to chat GPT and to Claude to try it out and see what that could do, and we knew that we wanted to be able to leverage and harness all of that curiosity that was really starting from the lab and spilling out across the company, because it was early days, which is really important to us and it's just been core to our mission for so long. So that led to the lab really germinating some of the earliest generative AI projects that we had, and now we have generative AI products in production with clients. So I think that's a great example. Our Experian Assistant, for example, is one of our B2B tools. It's agentic AI and that's one that really started with expertise in the innovation lab and has made its way to production use. That's benefiting our clients.
Speaker 1:Brilliant, wow, so exciting. Of course, there's a lot of buzz around agentic AI, ai agents in fintech and other areas. What's your take on how AI agents will shape financial services and underwriting or fraud prevention, or all the areas you're so active in?
Speaker 2:Agentic is really interesting because it takes what was an interesting capability of AI and starts to be able to collect those capabilities into autonomous reasoning multi-step processes. Autonomous reasoning, multi-step processes we want to be able to help clients not only better understand how to interact with their financial life and I think a lot of advanced chatbots and natural language are helping people do that today. Agents will take the next step. Agents will help you do it for me, and this can be a direct consumer or in the case of something like Experian Assistant, that helps the users at our clients as well. So, for example, our Experian Ascend platform is the technology platform where so many of our capabilities are.
Speaker 2:Capabilities are. We also have our Ascend sandbox that allows our clients to experiment, bring their own analytics models, create new ones, and so one of the things with Experian Assistant that's agentic is that at our clients, instead of having to require an advanced data scientist to explore in that sandbox, to take the time to create models, test them, see if they can be deployed, our Experian Assistant Agentic AI can do a lot of that for them. So, in natural language, even a business analyst can explain to Experian Assistant what they want to do, and that will launch a series of activities on behalf of that user to achieve what they desire. So I think that Agentic is so exciting in that regard, because it allows so many individuals to be able to take advantage of the capabilities.
Speaker 1:Wow, amazing. So you know many financial institutions fintechs don't have the deep bench of AI talent that you have and built. They don't have certainly innovation labs. So how do you help them potentially adopt these emerging technologies in a practical and secure way?
Speaker 2:Yeah, it's a great question and it's one that we have been reaching out and partnering with our clients in this regard, I find that the fintechs are some of the most technologically curious. They're always experimenting, they certainly continue to lead the way. We learn from them as well, and we strive to make that capability and these unique assets that we have. Experian has a wealth of clients, not just in financial services.
Speaker 2:I think some people aren't aware of the work that we do with health data to facilitate, for example, billing of health services between payers and patients and the providers. So you can imagine we've been touching all kinds of very regulated data across a number of different clients and verticals for a long time, of different clients and verticals for a long time. That gives us really unique insights that all of our clients benefit when we can see these different aspects of consumers' lives. So we're helping with not only providing access to anonymized data, for example, but also the tools so that these fintechs and our financial services clients can experiment themselves and be able to do that more quickly. I think that enabling that rapidity of innovation benefits everyone and it's been received very well by our partners.
Speaker 1:Very cool. Speaking of data, you're sitting on, I'm guessing, terabytes, petabytes of consumer data, probably all of mine and yours and everyone else here in the US listening or watching. How do you strike that balance between pushing the envelope with these new AI tools and services while staying compliant with all the data protection, data privacy regulations out there?
Speaker 2:Yeah, absolutely. I mean, it was one thing that from the very beginning, as I mentioned, we had thousands of people employees curious about using these tools. The very first thing we knew we had to do was how do we ensure that we maintain our practices around being good stewards of this data? We're heavily regulated by numerous agencies, not just in the US, but in all the countries where we operate, and it's something that we've prided ourselves on our earned reputation and respect for doing that well and protecting consumer and putting consumers at the heart of what we do. So that was there at the very beginning.
Speaker 2:The AI Risk Council was one of the first bodies that we formed in this regard. Risk Council was one of the first bodies that we formed in this regard, as well as a compliance body. And then thinking about the responsible use of AI. Even before generative AI, one of the risks of AI and machine learning was bias, and we know that when we're talking about things like credit risk, we need to be so careful that we are making sure that the data is being used properly and the models are being built in a way to prevent bias that can creep in when we're using these technologies. So that's something that's been part of our DNA from the beginning. It's part of our mission and who we are, and so, even as we look to these new technologies and how we can innovate with them, that's a culture that we brought alongside right with us, and so it is something that we remind ourselves of every day, because it's so critical to how we operate and how we serve consumers and clients so critical to how we operate and how we serve consumers and clients.
Speaker 1:Brilliant, and, of course, ai is in the news every day. Top of the fold, as we used to say about newspapers. But what are some of the misconceptions, misunderstandings that you think exist in credit or finance or data management that you read in the press or the media?
Speaker 2:Yeah, I think that there are perceptions about the legacy companies or consumer reporting agencies. Data companies are going to be left behind by these technologies, and that's certainly not true.
Speaker 2:I think that we are good evidence of the contrary of that. One of the most important factors in the advancement of AI is going to be data, and we are part of a collective dialogue that the public and our government officials are certainly involved in. What are the roles of companies, of data stewards, of data providers, as we think about how AI and generative AI is consuming this? A whole new dialogue around the training of the public models. There's been a lot of discussion, especially with AI of late and how much energy it's consuming, for example.
Speaker 2:I think that the natural market forces are also going to steer us toward innovation in the most efficient ways to use AI. Certainly, as the GPUs and the chips continue to evolve, I think we'll see more processing capability cheaper, just as we have with standard GPUs for computers for years. I think the same will hold true. We'll find new ways to use more compute, but at the same time, because of the costs associated with it, I expect to see a lot of innovation about how we optimize our training data, how we optimize the use of that compute so that we can make innovation quicker and take advantage of not everyone has access to the same unlimited, vast resources, so I think there's a lot of misconception that our energy demand and use and demand for these capabilities will only just exponentially increase will only just exponentially increase. Certainly, innovation will drive that and adoption, but at the same time I believe we'll see a lot of innovation around efficiencies and optimizing the use of these capabilities.
Speaker 1:Brilliant. Let's talk a little bit about the lab itself and the culture of innovation that you're helping lead. How does that look? What does it look like behind the scenes and what's working there? Give it all of your history and regulatory requirements and global reach. How do you manage that?
Speaker 2:We really benefit from a great reputation as Experian, I think when people think of us, especially data scientists. We still have some legacy reputation of having a lot of data, so that makes any data scientists excited. But at the same time, I think there's also recognition of the technology developments and that footprint that we have. It doesn't hurt that we have really strong leaning in consumer facing organizations, our direct to consumer advertising and the tools that we use to help consumers, like Boost here in the US that helps you boost your credit score. That has created an awareness of who Experian is and the different parts of lives that we touch. And so when we are interviewing candidates and hearing from people who want to come work at Experian, they are familiar with the place that we're playing in the world and how we might already be touching their lives.
Speaker 2:And so the idea of being able to make a difference and to be part of a technology and innovation group that's so close to the business, not just tucked away buried in an R&D center somewhere, has proven to be really attractive for talent. We run an organization that's very open, very communicative. We allow a hybrid way of working so we find the best talent where they are. And yet, because we have these capabilities and people across the country in the world, there's usually an Experian office relatively nearby where people can come together in person as well. So I think that ability to combine the access to data, the access to consumers directly, the access to the business leaders who are engaging with our clients, naturally attracts talent, technology talent not just to the lab but also to our businesses, to be a part of something meaningful and to work with like-minded people and feel like you're making a real difference in the world.
Speaker 1:Wonderful. It's a competitive landscape out there with big tech especially. So, well done. I used to ask people where do you see things going in three to five years, and AI it's more like three to five days but short term long term for AI and Experian. What are you excited about this year and maybe next?
Speaker 2:Yeah, one of the things I'm really excited about is this sort of navigating human and AI collaboration, and I like newspapers too, and just earlier this week in the Wall Street Journal, one of the big New York banks was talking about how they're deploying these autonomous agents and giving them email logins and logins systems, and that every senior manager will have on their staff these autonomous agent assistants. And so navigating that human and AI collaboration interaction. How do we monitor that? I really think it's important to have that human in the loop when we're working with AI, and this is an area that I think we can explore and maybe lead the way in some of the experimentation around this, especially in the businesses where we operate so excited about that human and AI interaction, collaboration, risks and how we navigate that. That's one area. I already talked about the optimization and energy use and how we might think about compute going forward.
Speaker 2:I think another area that is really interesting is the rise in quantum computing that is starting to accelerate. I'm sure that this new compute power is helping advance the research at an accelerated pace. This is an area that our innovation labs have been researching for a while. Particularly interested in, we have experts who have spent their PhD time in this area. Spent their PhD time in this area and being also very personally passionate about fraud detection techniques, I'm watching this area very closely. Quantum computing has the potential to force a rethink in a lot of the encryption technologies we use today. Technologies we use today, and so this is an area of great interest as well.
Speaker 1:Well, I can't wait to dive deeper into some of these topics in the future, maybe with some of your experts. In the meantime, be keeping an eye on all these new use cases and services that you'll be coming out with Congratulations. Thanks so much, evan. Thanks so much for joining. Thanks everyone for watching and listening, sharing and be sure to check out our new TV show at techimpacttv on now on Bloomberg and Fox Business. Thanks very much. Thanks, kathleen.
Speaker 2:Thank you.