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Experian Exchange is a series featuring interviews with business leaders. Sri Santanam, an AI and analytics expert, discusses the impact of AI in the financial industry. Experian is using AI to improve automation and decision-making. They believe generative AI has the potential to amplify the impact of their data. Experian is investing in grassroots innovation and ensuring employees have the right skills. They also expect their partners to integrate AI into their products. Experian is focused on AI ethics, safety, and privacy, and has set up an AI Risk Council. Business leaders should invest in AI and establish a framework for safe innovation. Experian has developed AI-powered tools like Ava and the Experian assistant to enhance customer experience. Future trends include advancements in generative AI. Welcome to Experian Exchange, a new series featuring exclusive interviews with all the latest news and insights with experienced business leaders. Experian Exchange will give you access to the latest thinking from inside the world of Experian, covering everything from finance to fraud and even technology to transformation. Welcome to Experian Exchange, I'm Della Rani. Artificial intelligence is transforming the business landscape. As with any rapidly advancing technology, there are great opportunities and risks. Sri Santanam is implementing AI and analytics at scale at Experian and he joins us now. Sri, great to have you with us, thanks for joining us. Thank you for having me. So in what ways is AI currently impacting your industry and how are you dealing with the constant innovations in the space? I think it's an appropriate question for the revolution that the industry is undergoing. Firstly, AI has been part and parcel of how financial services and how Experian has operated for decades now. We use artificial intelligence in automation, we use artificial intelligence in making better writing decisions. But what's happened in the last 12 to 24 months is we've started to see artificial intelligence really take a step up into a new area of capabilities, generative AI. And we think the impact on information services and data businesses is profound. We think it has the potential to really amplify the impact of the data we have in services of both businesses and consumers. And we are on a mission to help realise that impact. So in order to do that, how do you make sure that your partners, your business partners, but also your employees have the right skillset to be able to really tackle the challenges of AI or the developments in AI? There's a few principles as we think about this journey. So one of the core principles as we embarked on this journey was grassroots innovation. So we believe that in order to really unleash this potential, it's not about getting four or five of the smartest people in the room to solve the problem. We really need to put this in the hands of the several thousands of our employees and spark innovation. So given that principle, we've made investments and we've got the right people in our business working together to put this technology in the hands of all of our employees to start to innovate. And we need to do this in a safe and responsible way because we're in a regulated industry where we're handling probably among the most valuable consumer data sets and plans, so that's very important. On your question on partners, our expectation with partners is that they're leaning forward on this. We expect to see in their capabilities artificial intelligence embedded, innovation supporting products in that way, and we're starting to see that with many of our partners. So how do you go about building the right team when you are trying to tackle such big challenges with technology? I think it's a great question, particularly at a time when this is so young. There are thousands of people out there who are product engineers or AI experts. Apart from the foundational skills which we want to look for in top talent like leadership and innovation, there's three things I particularly look for. The first is a hands-on approach to innovation. I expect you are never too senior or too proud to actually get hands-on, so I expect leaders on my team and I expect them to hold me to a standard as well where we really get hands-on and understand what's happening at the classroom level. Second, I expect a lot of my leaders to challenge the status quo, never be afraid to dream. We're in a world where the traditions and the norms that we've seen for the last decades are being shattered every day, so we need eyes on the class. And finally, we need people with agility, people who are always willing to learn, experts who are really willing to upskill, and we're seeing that in a number of our leaders and employees. Some of our top talented generative AI are actually leaders and technologists in our business who've taken the time to upskill themselves. What about some of the concerns around AI ethics in particular? How concerned are you about this advancement of generative AI and the potential impact it could have on society? This is a very important topic for us, and particularly being in a regulated industry with a tremendous amount of consumer data, we take this very, very seriously. It's a topic of AI ethics, safety, privacy. And at the very outset of our journey, one of the things we did is we set up an AI Risk Council to help us understand the right position, to understand and define the right position framework around how we are going to innovate and how we're going to innovate responsibly. And out of that, a few things came out. First, a lot of our choices in this space are swift, they're very deliberate and intentional. So we ask ourselves about the policies, the principles we stand for, and then translate that into actions and decisions. The second is a lot of this is new. The risk frameworks, the governance frameworks around these things are very, very new. So we've been quite successful at getting historically siloed teams working and innovating together. So our risk teams, our legal teams, our technologists and AI teams are now on a periodic basis sitting down and really thinking about how we govern this. And coming out of that, we've had some very powerful innovation, which has allowed us to be at the forefront of this. I'll give you an example. We realized that in certain domains, the traditional frameworks for how we manage risk, like a customer service agent, don't quite work. And in fact, we have to think about the frameworks we apply today for humans, like training we give, or the policy we ask them to learn, or the questions we ask them. And I think there's a lot of innovation in this space we're going after as well. So given the fact that there is a lot of innovations, but of course they come with associated risks, what can business leaders do to be better prepared to handle these risks, but at the same time, obviously seize the opportunity that AI as a technology provides? Absolutely. I think two big things. The first is get ahead of it and make some investments. If, as a business leader, you're waiting for something to go wrong and then deciding how to respond and react to it, then you're probably a bit behind the curve. The second thing I would actually say is formalize a framework to innovate in a safe way. This doesn't mean that you're introducing unnecessary bureaucracy, but it does mean that you're making a set of intentional choices on how you're innovating. Both those things are important. Getting the investments up front, as well as having the right governance framework around this. What's your advice to business leaders? What are some of the core principles that they should use or think about to guide their development when it comes to data analytics functionality? I'll play back some of the things that we've been doing, which I think are important as I step back. First, I think grassroots innovation. If you're a business looking to innovate in this space with generative AI and AI, you're not going to solve it with one or two smart people in a room. You really want to unlock grassroots innovation. I'd encourage business leaders to think about that. The second I would actually say is this space is moving tremendously fast. It presents an interesting challenge of what do you do versus what do you build versus buy versus pass. The principle that we've often talked about is think about if you're walking through a one-way door. If you're walking through a one-way door on a decision you're making, then be much more thoughtful about it. If you can make a decision without walking through a one-way door, so if it's a partnership or a product or something you're trying, which you have the option of switching away from, then that's probably a safer, easier choice to innovate. The third principle is back to what you actually said. Make investments in AI ethics, responsible AI, and governance early and upfront. Those three principles, I think, would help businesses grow faster, innovate in a responsible way. Sushree, what's Experian doing in this space? How are you leveraging AI and some of the technological advances we're talking about in your line of work? We recognized soon after the Cat GPT meteor hit Earth that this technology was going to have a profound impact on our industry. We've made early investments in products, productivity, education, and platforms. One of the examples I'd like to call out is Ava, a digital financial assistant, which is now available in our Experian marketplace. We've embedded over 5,000 documents and a huge amount of credit education knowledge in Ava. Historically, if a consumer wanted to understand questions about how to improve their credit, financial education, they would have to go to our website or look up the information. Now, they can have a conversation with Ava, powered by generative AI. The other example, which I'm personally quite excited about, is our Experian assistant. We've got products like the Ascend Sandbox, which is essentially a powerful analytical environment with our data available for many of the large financial institutions to innovate and run analytics on. As powerful as it is, it does have challenges around requiring the users to deeply understand the data. Often, these users will reach out to us to understand truly how to use the data. I'm proud to announce that we will soon be having a co-pilot to help many of our customers really embed and understand 20-plus years of our experience through an assistant, which can help them answer those questions. Given the fact that we are seeing so many advancements in this space, what are some of the future trends that you are looking out for? It's interesting to see in the space of generative AI, we're seeing these large models and the technology becoming more and more hungry for data. I read some studies that the publicly available data will probably all be consumed over the next 12 to 24 months. What does that mean? It means that data becomes even more valuable than it ever was. For us, a company which has some of the most valuable data sets on the planet, it increases the responsibility we have on how we manage the data, curate it, and how we ensure it's used and really driving greater impact for our consumers. We think that's an important trend in unlocking powerful data, and we take that responsibility very seriously. Sri Saldar, thank you so much for your time. Thank you very much for having me. We hope you enjoyed this episode of Experian Exchange. Please subscribe to get access to all the videos in this series. To learn more, go to experian.com forward slash exchange. Thank you.