Videos
Accelerating AI-Driven Change in Banking: Insights from Stanford with Infosys’ Bal Shukla
Insights
- AI in banking is approaching a “J-curve” moment, with unlocked enterprise data set to transform processes from fraud prevention to customer service.
- The biggest impact of AI will come from cultural change—rethinking inputs, outputs, and how talent and processes are aligned for the future.
- Partnerships between academia and industry are accelerating innovation cycles, solving problems years ahead of schedule.
How is AI poised to reshape banking, operations, and the culture of financial services?
Recorded at The Business and Economics of AI workshop co-hosted by Stanford University and Infosys on May 14, 2025, this interview features Bal Shukla, Head of AI and Transformation for Financial Services at Infosys.
Bal shares why the industry is on the brink of rapid AI-driven change, powered by the integration of structured, unstructured, and third-party data with advanced compute capabilities. He explores how organizations can reimagine processes by focusing on inputs and outputs, adopt AI as a catalyst for cultural as well as technological transformation, and leverage academia–industry partnerships to accelerate solutions for critical challenges like fraud and security.
Key takeaways include:
- Why banking is entering a steep AI adoption curve and how data is the unlock
- How AI changes not just workflows but organizational culture and talent strategy
- Why academic collaboration is critical to solving urgent industry problems faster
An essential discussion for leaders in financial services, technology, and enterprise transformation seeking to turn AI potential into measurable impact.
Bal Shukla:
I'm Bal. I head AI for the financial services and also transformation. And I've been working in broadly ML, AI, classical AI for 20 years now.
What’s the key thing you see in AI for banking?
The key thing that I see in AI is J-curve is about to happen. And there are, you know, banking industry, industrialists who are thinking of AI will take a while to come forward. Do we have access to data? Will we have enough transparency with everything? Will regulators try to push back on this and all? I think it's all going to open up, right? The moment enterprise have unlocked the data that they have, both structured and unstructured data, first party data to third party data and that's what banks get the advantage to really transform all the processes that we're looking at, starting from fraud to IT operations to security to business operations to front office, contact center, all the things it requires reimagination and transformation.
What was the most interesting thing you heard today?
The most important thing is we got to rethink the processes and LLMs from enterprise LLM perspective. We have to bring in relational data into the aspect and compute has really become much cheaper. So the processing power is high now. Apply the relation data with all the data to make it powerful for the organization. That's the key thing I learned today.
What’s the biggest shift AI brings to organizations?
See, today every organization is thinking of how can I accelerate faster? How can I deliver better products, faster products, efficient products? How can I create more revenue, new business models? How can I change the way things are working today? How can I upgrade my talent who are suited for future? If you look at AI, AI is not just a transmission. AI is about culture shift in the organization. And that's why, primarily, organizations have to look at it holistically to really reimagine everything that's going on.
It’s almost look at what is input and output going to be there and use the two things to redefine the process. Let's not get caught up to redefine the process first. Just input output is more important. As a persona, every person has look at how can I get my job done better and let the AI do all the inter-plummings for what needs to happen. It won't be done tomorrow. It's a journey. And organizations who have already spent their time, money, and thinking around scaling it to the level of comfort, they’re going to take it forward.
How do you see the Infosys-Stanford partnership creating value?
There's a huge potential and we learned today, a steam engine took 30 years to come to innovation, right? That's the case which industry takes. Now it's no more 30 years. Quantum will not be ten years, maybe in five years we can see quantum around us. So the idea is, in the AI world, software engineering is done by AI now for the future. It's coming slowly. Junior engineers work is getting transformed, maybe tomorrow senior engineers work will get transformed to AI. So transformation is happening much, much faster.
Hence, it's very, very important to bring academia and industry together because industry problem, used to be ten years down the line, is going to be solved in the next two, three years. The acute problem of fraud, security, is very, very unique. I mean, people are talking about how can we understand the network impact when I'm doing a payment transformation overall. So these things are very critical to be done today. So again, because AI is helping us solve problems, which are for future years, much, much sooner than next two to three years.
It's very important to bring academia and industry together. And also it helps academia understand what's a real problem in the ground that they can put their mind and focus on.
