Thank you so much for joining us in this interview series. Before we dive into our discussion our readers would love to “get to know you” a bit better. Can you share with us the backstory about what brought you to your specific career path?
I am engineer by education and soon after I joined a management consulting firm where I focused on advanced analytics technology and AI for the retail sector. After the financial downturn, I moved to delivering Silicon Valley-style tech, analytics, and AI for financial services companies. About five years ago, I came to Experian to help drive our mission around financial inclusion and amplify the impact of our data with businesses and consumers through analytics and AI. I’ve always been passionate about using Data, Tech and AI to drive real world impact and make a difference in peoples lives and Experian’s mission around driving consumer financial inclusion really appealed to me.
What do you think makes your company stand out? Can you share a story?
Experian has one of the most valuable data sets on the planet. For more than a century now, we’ve been using the power of data analytics and technology to drive better outcomes for businesses and consumers. We see AI as an enabler for us to further unlock the potential of our data to drive outcomes for our businesses.
What differentiates us in AI is our ability to drive innovation in a heavily regulated environment while maintaining the highest standards of data privacy, security, and safety. We have made a number of investments in people technology and explainable and responsible AI to enable this. This approach enables us to draw the right balance between managing risk and data privacy and supports our stewardship of protecting consumer data.
You are a successful business leader. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?
The first would be realizing and “unlocking the power of grassroots innovation”. This involves living and promoting a culture of natural curiosity and open-mindedness to explore new ideas and technology. As leaders, we can support innovation with a systematic process for harnessing the power of an organization through things like hackathons and rewarding the right behaviors. I strongly believe that the power of what grass roots innovation can unlock far exceeds that of any one smart individual.
A recent example of the power of innovation is how Experian responded to the emergence of generative AI. Instead of putting the technology in the hands of a few select people, we are focusing on providing our 20,000-plus employees access to the right tools in a safe and responsible way. And we’ve seen remarkable innovations come out of that bold step, ones we would never have contemplated.
The second trait is “not being afraid to dream” and not putting artificial limits on achieving what you or others are capable of. Experian has historically started as a credit bureau, but we are much more now and focus on data, analytics and tech. So, it is important for us to see the huge opportunity ahead of us. When I started first at Experian our teams took 6+ months to build and deploy models in regulated industries. Now we are doing that in a fraction of the time with the right AI, tech and automation — we never would have achieved this if we were afraid to dream and succumbed to the naysayers.
Third is the importance of leaders in tech and AI taking a “hands on approach” and staying in touch with the practitioners. I believe you’re never too senior an executive or too old to explore technology and “hack”. Carving out time to get hands on, whether it’s either with coding or using some of the generative AI tools, helps leaders stay fresh with technology, remain credible and remain effective in their roles.
Let’s now move to the main point of our discussion about AI. Can you explain how AI is disrupting your industry? Is this disruption hurting or helping your bottom line?
AI is serving as an accelerant to the disruption and digital transformation in our financial services industry, like the transformation we’ve seen in consumer retail over the past decade.
AI helps financial institutions make better, faster smarter decisions and also meet rising consumer expectations for a more expedient, personalized customer experience. Specific examples include faster retail banking, a more efficient process for approval of loans, more sophisticated underwriting models and smoother, more productive digital interactions with a financial institution’s website, customer service, online chat, and more.
Also more recently, Generative AI, which is getting a lot of attention, has a tremendous potential to drive productivity and enhance and amplify existing products and customer engagement.
Finally, it’s also getting much more universally accessible and even smaller financial institutions can take advantage of AI capabilities which only a few years ago were limited to the top banks.
Overall, AI is going to have a profound impact on financial services and significantly accelerate the digital transformation.
Which specific AI technology has had the most significant impact on your industry?
Machine learning is the most significant, especially in its use for underwriting models and pattern recognition as well as a number of areas related to automation. We’re seeing greater adoption of AI and machine learning as the financial services industry looks to further modernize how it does business.
Generative AI is also poised to produce a significant impact, though it’s still early days in developing applications based on the technology’s large language models. There are a number of POCs in this area and we are starting to see now see use cases in production which require careful handling of data privacy and regulation.
Can you share a pivotal moment when you recognized the profound impact AI would have on your sector?
Early last year, my 11-year-old son came to me and asked for a sit down with my wife and I. Like most 11-year-olds he is loves playing his favorite game on his Nintendo Switch but unfortunately for him his parents don’t allow him to play video games during the week. To our surprise, he had written a 2-page detailed essay titled “The case for 15 minutes of Screen time during the week”. The essay was thoughtful eloquent and well researched. It had specifically addressed concerns he had heard from his mother like the risks of dopamine hits and referenced specific articles which suggested that in moderation and at frequent intervals the risks were minimal. It had the style and eloquence of a professionally written article and would easily have put some of my college essays to shame. His mother was floored and being a techie at Amazon he had appealed exactly to her penchant for doc writing and reviewing. She had no option but to acquiesce. I realized in that moment if that a 11-year-old unprompted can use this to amplify his message and impact there is something profound here and we really need to find a way to get this in the hands of as many of our people at Experian as possible to unlock grassroots innovation.
How are you preparing your workforce for the integration of AI, and what skills do you believe will be most valuable in an AI-enhanced future?
We will continue to offer generative AI to our workforce for use in safe, responsible ways and believe that AI will continue to play a pivotal role in amplifying the impact of our data.
As for the skills that are required, it is largely an openness and a propensity to engage with and try this technology. I don’t think coding and engineering are going away anytime soon, for example, but I do think that coders and engineers who are generative AI-enabled will quickly start out-performing those who are more reluctant to adopt and embrace this technology.
What are the biggest challenges in upskilling your workforce for an AI-centric future?
Our biggest challenge is finding the most effective way for people to learn and train each other. A lot of this is new and requires domain expertise to be effective. So, while continue to bring in AI and tech talent a key part of our challenge will be accelerated upskilling.
First thing, we launched a companywide generative AI academy where people can engage with the technology, experiment with it, and learn. Early adopters can share their stories, mostly via video, on what they’ve done. Those with exceptional skill and a propensity to understand this technology will help us raise the average of adoption across Experian.
Our technology and Innovation Lab organizations and our data analytics community are helping raise the level of understanding and knowledge of generative AI across our enterprise. We want to encourage use and create an environment where people who have these skills and capabilities can thrive.
What ethical considerations does AI introduce into your industry, and how are you tackling these concerns?
Experian holds as its core principles the secure protection of data to ensure data privacy and to protect our consumers. Any use of generative AI is subject to upholding our principles. Creating the right governance measures around AI is necessary for Experian or any business using the technology. The key is having the appropriate security protocols already in place to guard against privacy violations. Lastly, it’s important that all users in an organization take responsibility for adhering to privacy and security controls when working with GenAI produced data sets.
What are your “Five Things You Need To Do, If AI Is Disrupting Your Industry”?
There are three things in terms of aggressively driving innovation and two things in terms of managing and balancing risk.
The first thing is to find a way to unlock grassroots innovation with AI technology and make generative AI available to many of their employees as they start thinking about what they want to do with it.
The second thing companies need to do when using AI is to work backwards from impact. So focus on carefully identifying and solving specific business problems and driving tangible impact for businesses and consumers.
The third is to set up a continuous feedback loop of pilots and experiments that leverage the technology and iteratively improve. If a company does not already have a culture where experimentation is actively encouraged, they should start piloting a few things, setting clear timelines on when you decide if it’s working or not.
On balancing risk, the first piece to get right is breaking down silos and getting the right personnel from risk and technology in AI in a room jointly collaborating and thinking about solutions. Historically risk and legal functions haven’t been charged with innovation but to get this right we need to evolve our approaches to adapt to the changing AI landscape.
Finally, companies should make specific and active investments in responsible AI. Setting up a Risk Council would be an option to review your use cases. This way, they can understand the risk associated with what they’re doing. One example is establishing AI security “guard rails” to protect data privacy.
What are the most common misconceptions about AI within your industry, and how do you address them?
There are two common misconceptions of AI.
First, when we look at this technology, there’s an assumption that it can quickly be leveraged to automate and replace humans. The fact is, we get more value if we use AI to enhance the process of how humans work. For example, when a company is thinking about deploying new applications to improve customer engagement and customer support, it’s much more effective to think about how an AI agent can support the dialogue of an existing human who is dealing with a customer.
The second misconception, particularly with generative AI, is underestimating what it takes to build products that work in production at scale (beyond prototypes). With GenAI, it is quite easy to produce a prototype or what may seem like a working product which might appear to work well at first glance and answer questions for a couple of instances. However, when you start to really think about moving into production and scaling to an enterprise level to support hundreds of thousands of users, then you realize that there are a whole number of issues that need to be addressed. Developers need to consider the proper security guard rails to put in place and different use case scenarios, manage hallucination, increase reliability, and ensure the responses being generated are accurate.
Can you please give us your favorite “Life Lesson Quote”? Do you have a story about how that was relevant in your life?
It would be “never stop learning.” My strong belief is the only sustainable advantage you can have as an individual or a company is the ability to learn and adapt.
Off-topic, but I’m curious. As someone steering the ship, what thoughts or concerns often keep you awake at night? How do those thoughts influence your daily decision-making process?
For me, it is less about concerns and more about sometimes waking up and thinking about how we can capitalize on the business and technology opportunities that lie ahead of us and move at pace. We are on the eve of a significant transformation driven by AI, one that requires a thoughtful balance between how organizations work today, managing the risks and the realizing the benefits.
You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people, what would that be? You never know what your idea can trigger. 🙂
The movement would be around education. There is a tremendous amount we can do for our future generations in helping them understand AI and how to responsibly use this technology. The faster we do that and the more effectively we do that with our younger generation, the greater the impact will be for humanity.
How can our readers further follow you online?
They can follow me on my LinkedIn page.
Thank you for the time you spent sharing these fantastic insights. We wish you only continued success in your great work!