Prashant Jajodia knows more than most about banking’s tech anxieties. As IBM’s financial services sector lead, Jajodia has daily conversations with the sector’s best and brightest on the possibilities and practicalities associated with deploying new technologies. In the first of a new series of interviews with tech industry movers and shakers, Tech Monitor quizzes Jajodia about how the financial services sector is balancing the potential of cloud, generative AI and quantum computing against the one thing that matters most of all for its customers – confidence in the institutions that hold their hard-earned money.
Tech Monitor: For years now, the meta-narrative about technology adoption in the financial sector is that the nimbler, neobanks were stealing a march on more established institutions with cumbersome legacy infrastructure. Is that still the case?
Prashant Jajodia: Yes and no. My view is that the established banks were initially taken off guard by their digital competitors, like Monzo or Starling. They certainly stole a bit of a march on their legacy competitors at the outset.
Now, though, the established banks have mostly caught up. The digital propositions these neobanks came up with – make sure that your account can be easily accessed on your mobile, and that any app you use is simple and easy to understand – are now replicated by high street banks.
The next chapter is really all about enabling what I call “hyper-personalisation” for the customer – using the data which we have within financial services on our customers to provide meaningful, useful and timely services to our customers. It’s also going to be about facilitating instant fulfilment for customers. On both, I think the digital banks and the high street banks are probably on level pegging. But the differentiator now will be how quickly either side can adopt the newer, much more expensive and complex technologies – AI, quantum computing, cloud – in order to bring these services to market.
TM: Tell us more about what you mean by “instant fulfilment”.
PJ: Today, if you look across most of the banks, when it comes to account servicing, checking your balance, making a payment or changing your address, all of this is instant. Most of it can be done at the click of a button on your mobile phone. You don’t need to call up anyone.
It gets a bit more complicated on the sales side. It can still take ages to open a new account with a new bank, for example, and that’s a problem that derives from the legacy technology many institutions are continuing to employ on the back end. It’s also a regulation challenge. There are a lot of rules about keeping customers and the sector at large safe from fraudsters that slow down the account onboarding process – and justifiably so.
But there are ways around all this. So much innovation is happening on the cloud, especially when we look at the kind of microservices architecture which is driving instant fulfilment. And from a regulatory perspective, too, there are plenty of solutions in the spheres of identity verification and validation, which are also speeding up these processes.
TM: How, then, is cloud adoption accelerating this change?
PJ: Banks have been on a journey to cloud for years, as your readers will appreciate. But that embrace has really tightened in recent years. Any concerns about regulatory challenges involved in moving to the cloud that might have been holding banks back have really disappeared, now. As such, since 2022, adoption has really scaled up and significantly accelerated digital transformation across the sector.
Because, once you’re on the cloud, you can sit in a technological ecosystem that will allow you to offer multiple bundled products to the customer. I’m seeing a huge number of things getting unlocked purely because, once you are on the cloud, the availability of compute gets multiplied just so many times.
In fact, I like to say to our clients that, as far as the sector goes, we’re in the third chapter of its digital transformation. The first involved the banks initially launching their digital shops on mobile apps, and even then all you could do was check your balance or make a payment. The second chapter, meanwhile, was all about automating the back end. Now, we’re very much in the era of hyper-personalisation and instant fulfilment, facilitated by levels of cloud adoption we just haven’t seen before.
For example, the State Bank of India is one of the largest banks in India. Two years back, they partnered with IBM to build a mobile app called YONO, which stands for You Only Need One. It’s a super app: it has all the customer journeys, around 200-plus, all completely automated. Not only that, but it also provides the customer access to over 120 merchants, including Amazon, Flipkart and a plethora of insurance providers. It’s more than a banking app – it’s a marketplace.
None of that would have been possible if we hadn’t taken the State Bank of India fully into the cloud. And with that massive increase in compute power, they’re now in a position to launch new AI services, too – all of which, we hope, will end up contributing toward a better digital experience for the customer.
Banking on gen AI
TM: Our readers will note how generative AI has taken the world by storm. But to what extent has it factored into the conversations you’ve been having with financial institutions?
PJ: It is absolutely at the top of the mind of my clients. Our research shows that 64% of financial services CEOs are under pressure to adopt generative AI solutions. I mean, just about everyone is playing with it, from kids in school to grandparents and even banking staff themselves. No wonder boardrooms are also putting pressure on our clients in the financial sector to adopt it.
The challenge comes with finding an application where you can trust generative AI to deliver results in a secure way. That, and any outputs need to be accurate. It’s no use adopting generative AI in a banking environment if it’s producing responses that are not only incorrect, but betray customer data during operation.
This is where we come in as IBM. We have been working with financial institutions to implement conversational AI applications for the past six years now, and more recently generative AI, too. In fact, we’ve been working with Lloyds Banking Group since August on using generative AI to improve their customer service, improving the search function for customers and staff alike. That was even before ChatGPT made it cool. And we launched our new enterprise data and AI platform, IBM watsonx, earlier this year to help clients build and tune foundation models tailored to their businesses.
TM: Can generative AI play a positive role in enabling hyper personalisation? And to what extent does that question of individual trust factor into those conversations?
PJ: If banks want to provide effective customer service to millions of actual and potential clients, then they need to use AI. On the agent side, it can prove invaluable in first supporting the customer in basic queries and, where necessary, escalating the matter to a human agent and then supporting the latter in their conversation with the customer. In the end, you end up delivering the kind of personalised outcomes that the customer wanted all along.
I mean, nobody likes waiting for a contact centre agent to pick up the phone for ten or 15 minutes, right? I certainly hate it. But when I have a simple problem, I’m happy for it to be dealt with by a machine. And that’s something that an AI can very easily handle.
In fact, just to give you an example, we’ve been working with ten of the top UK banks to implement conversational AI solutions along these lines. So, if you bank with NatWest, Lloyds Banking Group, or even Virgin Money, you can sort out minor problems like a change of address and balance checks, and some larger ones, too, like basic fraud reporting, almost instantly. And for the banks, it’s a no-brainer – effectively a 24/7 service with consistent responses and low overheads.
For those bigger life moments, you’ll of course always need and get a human to speak to. But even then, AI can play a useful role in advising the customer service agent. A great example is the AI agent we’ve helped build in collaboration with NatWest, which we’ve called ‘Marge.’ Today, if you’re talking to NatWest about taking out a new mortgage or servicing a new one, that AI agent is standing up to assist their human colleagues in supplying the correct advice. That way, the human agent becomes that bit more efficient.
TM: Quantum computing is another new technological frontier for banking. How long do you think before we’ll see quantum computing services play a much more mainstream role in the industry, as opposed to the forecasting and cybersecurity applications they’re currently being used for?
PJ: I think are getting there. The power of the technology is increasing every day – just recently we had our own processing breakthrough with our Osprey quantum system. Our clients, too, are busying themselves with their own proofs of concept when it comes to quantum. It’s early days right now, but I believe we’ll begin to see many more use cases emerge within the next three years or so.
TM: In the conversations you’re having with financial services executives, what’s their familiarity with quantum versus AI?
PJ: I think the AI conversation is more urgent. We’re not only having briefings with our colleagues on it – we’re getting into real-time implementation when it comes to AI applications in a host of areas. In the case of quantum, we’re still largely at the briefing stage, though there is a huge amount of interest. And that’s understandable: the technology is still expensive to run, but we’re starting to work with many of the bigger banks on pilot use cases for quantum in Monte Carlo simulations and fraud detection. So, in simple terms, quantum will be more relevant in a couple of years’ time. But, AI? That’s absolutely vital to discuss and implement in the here and now.
Read more: Who are the early adopters of quantum computers? Big banks, that’s who.
