Explore how generative AI is revolutionizing finance with trends like increased productivity and enhanced consumer experiences.
Shri Santhanam,EVP and GM, Global Analytics and AI Products at Experian
Shri leads Analytics and Artificial Intelligence at Experian and is responsible for the Analytics business in North America as well as the Generative AI agenda. His focus is on using Analytics and Artificial intelligence to maximize business and consumer impact. This includes productizing Analytics to drive scalable growth, commercializing Analytics to maximize financial impact, and using AI to drive productivity and enhance products. Prior to Experian, Shri was a senior partner at Oliver Wyman where he was a founding member of Oliver Wyman Labs (Digital and Analytics business). The practice concentrated on impacting large financial institutions with Silicon Valley-style technology and analytics. Shri holds a Master’s degree in Engineering from Stanford University.
Shri, can you please share with us a brief overview of your professional journey, particularly how you transitioned into your current role as EVP and GM of Global Analytics and AI Products at Experian?
I’m an engineer by education. I was hired into the world of management consulting as firms were trying to turn engineers into consulting partners. At the start of my career, I focused on bringing advanced analytics technology and AI to the retail sector. Post financial crisis, I transitioned to delivering Silicon Valley-style tech, analytics, and AI to financial services. About five years ago, Experian approached me to use AI and analytics to drive our mission around financial inclusion. My role at Experian is to amplify the impact of our data with businesses and consumers through analytics and AI.
As a leader in the field of analytics and AI products, what personal strategies or approaches do you find most effective in ensuring the successful integration of generative AI within financial institutions?
The balance between seizing generative AI opportunities and managing associated risks is critical in regulated industries like financial services. Right now, our priority with financial institutions involves three strategies. First, we encourage the use of generative AI for grassroots innovation that supports greater productivity in a safe and responsible way. Second, it’s important to set up the right corporate governance for vetting use cases, connecting cross-functional areas such as risk management, legal, technologists and engineers, customer support, and others. Third, tying all AI investments to tangible, measurable business impacts for our customers or consumers.
In your opinion, what are the primary benefits that financial institutions can derive from utilizing generative AI in their operations? How does it contribute to enhancing fintech products, efficiency, and overall quality?
Generative AI’s early benefits are predominantly in productivity and improving core processes, speeding up the time to perform traditionally manual or cumbersome efforts. This includes enhancing consumer engagement, especially in the areas of information delivery for things like credit education, and automating internal manual, repetitive processes that are historically prone to error. More recently, we are seeing engineers relying on generative AI to help accelerate their development of new products and services through querying best practices in coding. These improvements allow businesses to operate more efficiently, benefiting both the institutions and their consumers.
What are some of the potential challenges or obstacles that financial institutions may encounter when integrating generative AI into their systems? How can these challenges be addressed or mitigated?
The primary challenges include ensuring privacy, integrity, and adherence to regulatory policies during the product or service development process. It’s crucial to have the right governance in place to ensure that software coding is accurate and tested, that data-driven analytics and models provide the right information at enterprise scale and are not biased or don’t produce hallucinations, and that there are always “humans in the loop” to serve as the ultimate decision-makers.
Considering your extensive experience, what advice would you offer to banks, credit unions, and fintech organizations that are exploring the implementation of generative AI? Are there any key considerations they should keep in mind?
We are seeing the democratization of capabilities in the financial services sector, with small to mid-sized financial institutions employing machine learning-driven models and generative AI to automate their decisioning and drive productivity. One consideration is to use generative AI to support credit education of their customers through online instructive forums and streamlining customer support on questions regarding products and services. Another is customer engagement for standard processes such as loan applications and other common service functions.
Given the increasing emphasis on risk management in the financial sector, how can generative AI be leveraged to effectively balance risk and innovation within these institutions?
In regulated industries such as financial services, governance over generative AI and its outputs is key. The same regulatory standards that apply to manual risk-control processes must be applied to the development of generative AI agents, prompts, and training associated with both of them. Adhering to this regulatory standard will require active dialogue across departmental silos, working backwards from specific outcomes that you want generative AI to produce, then setting in place the right people and technology infrastructure to make it happen.
Can you provide examples of successful implementations of generative AI in the financial industry that have achieved a balance between risk mitigation and innovation? What are the key factors contributing to their success?
We’ve successfully deployed generative AI tools for a large number of our engineers, enhancing productivity in product and service development. Additionally, we’re using generative AI in customer engagement, particularly in credit education, through chatbots and other hyper-personalization tools to engage consumers, which are in production and being evaluated for broader use.
As the landscape of AI continues to evolve rapidly, what emerging trends or advancements do you foresee having the most significant impact on the future of generative AI in financial institutions?
There are three macro trends that we’re following closely. First, with financial institutions, the market is seeing elevated interest rates, which creates a degree of volatility, uncertainty, and cost pressure among financial institutions. As a result, many of them use generative AI to increase internal productivity across multiple operational systems, mostly notably customer support, marketing, and sales.
Second, like the digital transformation that occurred in retail where consumers now expect faster, smoother, and more personalized experiences, financial services – especially banking and lending – have the same consumer expectations. Generative AI is empowering this transformation, and the technology is driving greater inclusion and accessibility.
Third, a lot of solutions providers are developing front-end interfaces that dramatically simplify the use of generative AI for non-technical users. There’s a fun industry saying here: “English is becoming the new programming language.” This ease of use will dramatically accelerate the business and consumer adoption of generative AI in financial services.
Are there any lessons learned or insights gained from past projects involving generative AI that you believe would be valuable for our audience to consider as valuable lessons or takeaways?
I’ve been impressed both by the pace at which the AI-driven innovation has moved and how the software ecosystem around it has responded in bringing tools and capabilities to the market. In my two-and-a-half decades working in this space, I haven’t seen technology and solutions move at this pace, and financial services companies of all sizes need to plan how they respond to the promise that this emerging technology holds.
Also, financial services companies must carefully evaluate how the products and solutions they develop with generative AI can scale at an enterprise level. For example, conceptually, it’s not that hard to stand up an AI-enabled online customer service agent. But to do it for the expansive list of possible consumer needs and uses, and have it perform consistently, will require competency in developing the technology for millions of different use cases as well as addressing the complexity of covering all the possible questions.
What concluding thoughts or key messages would you like to impart to our audience regarding the strategic utilization of generative AI in the financial sector?
The potential of generative AI in driving productivity and creating new products and business models is tremendous. It’s important for financial institutions to embrace this technology responsibly, ensuring a balance between innovation and risk management to maintain consumer trust and comply with regulatory requirements.
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