Welcome to the Executive Data Series.
DXC created this new program to provide advanced insight into the data domain. In a series of conversations, DXC experts will explore data-driven decision making, and offer their perspective about what it takes to be successful in data, information and knowledge activities.
Mohammed 'Khal' Khalid, global advisory director at DXC Technology, will moderate our series. Discussions will draw on research conducted by DXC and upon our executives' experiences working with customers.
In this third conversation, Khal welcomes DXC's Head of Banking and Capital Markets (EMEA) Andy Haigh. We invite you to listen to their full conversation or, if you don’t have time now, to a short extract about the rise of the customer channel. You can also find a full transcript of the discussion below.
The conversation
Q. It's my absolute pleasure today to introduce Andy Haigh. Andy, would you like to introduce yourself?
A. I look after banking and capital markets for DXC across EMEA. I've been in the financial service industry for almost 23 years now, both on the capital markets side, working directly with the customers, and more recently into the retail sector via DXC. So financial services has been my home all the way through my career, effectively.
Q. The topic of today is data and living in a data-rich world. So, Andy, paint a picture of what the future could look like in banking.
A. Let's start off with the end consumer, and then we can flip over to what that looks like from inside the banks themselves. I really want to see better analytics for the end-consumer in terms of modeling and forecasting spend.
A lot of effort is spent inside the banks themselves around analytics, but they've got a long way to go to actually properly model trend and spend, even going as far as really incorporating true credit card payments into spend trends, rather than rolling placeholders from prior payments. So you can get a real grasp of where you're spending money and your disposable income off the back of that, and that's also really important for vulnerable customers.
With the net disposable income at the end of month, there's a lot more work that can be done to bring products to the consumer in a dynamic way – month by month – so they can almost instantly model what they could do with that disposable income and that disposable spend.
So, actually spend the additional capability into the customer experience on mobile devices so that analytics can really work for the consumer and products can be modeled for the consumer. It relieves the pressure on the contact centers, which can then add and maintain that human touch.
But what you really want as a consumer is not with a bank, but more actually with a hub: with a virtual wallet, where the wallet understands what I do, where I can interchange products from different banks instantly, maybe tactically. So I might want to use products short term and throw them away, subject to where I'm at, where my family is at, and what my disposable income situation looks like month by month. And again, no forms, seamless, it understands me. That human touch at your fingertips is really, really important.
What does that actually mean within the banks themselves? If you look at product onboarding, the banks still have a long way to go to have a centralized view of that customer base. Again, you've got current accounts, credit accounts, unsecured lending, secured lending, motor finance, asset management, wealth management. Bringing that together in a harmonized way allows the banks to use that information to benefit their bringing new products into the market. So ultimately they can cross-sell more of their products to that consumer in a digitalized way, while then simultaneously managing the cost envelope of data in the systems for current products that are currently servicing their consumer markets today.
Q. But all of this is underpinned by data. Through our data metabolism research, we identified that there were three decision states – discover, develop, and defend – but they have certain categories of data underneath them.
So if we look at the different types of data from a banking perspective, what do you see as the associated challenges and opportunities that come into play?
A. In terms of running the bank today, there's obviously the data required to transact. Banks do it today – they know their systems well, the products are there – but it's more to do with the efficiency in dealing with that scale so that the key focus is around optimization, archiving and housekeeping to keep those platforms performant.
Behind those, taking out the cost of that data, moving more from high-cost to low-cost infrastructure solutions – whether it's on prem or via the cloud – and ultimately not only changing or reducing the cost envelope of running transactions and running the hub of the business, but also moving towards the ESG agenda of the banks.
Outside of the transactions, it is obviously managing risk. That data is scaling; the ability to "burst process" that is more prevalent than ever, and actually risk is moving more towards an immediacy – being able to process known complicated algorithms quickly and effectively in real time, to allow banks to make decisions internally and also for the consumer products that people apply for.
There's still a big challenge in absorbing regulatory changes into the bank’s roadmaps. So it's an integration problem, it's a stitching problem and it's a test automation problem. Until that is refined, then regulatory just clogs up all of the roadmaps that release new business functionality to platforms. So again, there’s a lot of data mapping, and a lot of data enrichment that has to run through all of the platforms end-to-end. There’s a lot of work to do around the ability to bolt new data across trades, flow them through the whole system, and do that in a way that's low-touch (from a test automation perspective).
Organizations understand their KPIs, their SLAs and how they measure performance. But I think there's still a lot of work to do for organizations to be able to capture – in a standardized way, and in order to visualize them – the pain points within the bank so that you can address them and hunt inefficiencies in the organization. That could be at the app level, it could be within a contact center, or with straight-through processing.
We talked about cross-products and being able to cross-sell. The new challenge that the major banks are trying to overcome is the ability to bring together data from different departments of that organization to have that real 360° view of the customer that's actually structured in a meaningful way and a secured way so that they can then work with the business to formulate the products that get pushed into that consumer marketplace effectively.
So, there are challenges that sit in the business today: to maintain the current product base, to be conformant, and to manage risk. But, equally, there's a new dawn of being able to centralize data – and I think the key thing is in a meaningful way, so that the data can be trusted (and then with the business processed) to form the products of the future.
Q. So, as we think through the categorization of the types of data that you mention, what do you think are the key changes that have to be made in the approach to data – and potentially to the applications that underpin that data – to make all of this possible?
A. Technology is there to containerize data, so that's not a problem, and a lot of IT areas are itching to play with that. The main challenge is making sure the right data is being centralized for that purpose. And the business has to have a much closer proximity to technology to work together in order to understand where to use AI and machine learning and what that is trying to build as a new product sitting on that data.
The operating model between the business and technology has got to clash and reshape itself so it can very quickly leverage that data in a meaningful way – one that drives a business outcome that ultimately results in a product suitable for the market here and now.
Q. That shift of operating model that you mention, where business and technology come much closer together – you touch on AI and ML, all topics of the moment – based on your experience, what would be the one big cultural change it really has to make?
A. Cultural change aligns itself, in a simplistic way, towards agility and DevSecOps, and actually looking at how technology "squads" itself to be quite fluid in achieving output-based delivery. When we look at the business in its product formation, it has to do something very, very similar alongside the technology in order to maximize the ability to create those products.
AI and machine learning is only going to get so far. The technology and the tool kit available for IT and developers is there today. It's really about sharpening that, so that it's actually getting the business outcomes that the businesses need. It's greater squadding, MLOps – with the business infused – which will be a change in gear on the business side, in order to have the vehicle to go and achieve that.
Q. One thing that resonates for me, and the term I would bring out, is this one of trust: the trust between business and technology. But I think the trust equation moves from business and technology… and data.
It's that trust between all of that ecosystem that enables the powerful outcomes. What strikes me is that, as we drive more data to enable hyper customer personalization and customer experience – where potentially customers can swap between accounts and services almost immediately, in a seamless, frictionless way – then potentially that drives a challenge for the banks where they’re losing a lot of that customer intimacy that they've previously held onto.
So how do you think that tension plays out, gets managed between data being used for customer experience versus the reduced intimacy that comes from customers having more choice?
A. That's a good question. I think it's an arms race and a "be careful what you wish for" scenario. All of the banks are chasing down a path that the consumers want – to have zero-time onboarding for everything. The ability to pick a product up, use it and potentially throw it away, subject to how their life choices pan out. With the immediacy of those products and the ability to almost zero-time onboard (which is the utopia), that actually creates a bit of a disconnect between the banks and the consumer because you've just given them unparalleled choice.
I can be moving from bank to bank and jumping between different products, because I can quickly onboard them and I can throw them away. So actually, if a bank doesn't keep up with the ability to zero-time onboard (or close to it), it will fall foul to the competitors that can. But when it does, does that mean then actually moving away the consumer intimacy with the bank to a marketplace. And what if I take back what I want as a consumer? I don't necessarily want a bank; I want a digitalized wallet – a hub that I can interchange current accounts, credit accounts, mortgages, child vehicles, anything in there – and I'd rather have it in a marketplace or wallet so that I can jump between it.
The key thing, though – the touch point that banks have got to make sure that they keep – is the human interaction at the contact center, so they're able to keep that connection to the customer and be careful about what's automated out of the contact centers.
Q. And in this arms race that you mentioned, who do you think is going to get ahead? What kind of characteristics are they going to display to get the advantage over the competition?
A. Product onboarding needs to be fairly swift. And actually, if you think about where we've been over the last three to four years – with COVID, vulnerable customer management, then going into very high utility bills and actually responding to interest rate changes – the organizations have got to put out products that are attractive to those scenarios to consumers.
And it could be not just products per se that they're selling, but also vehicles that better handle, and better cater to, vulnerable customers.
One of the other key concerns there is not only making sure you've got the right hardware of the centraliized view of the customer – that then allows you to use AI and machine learning to formulate the products with the business and squads to push to the consumer market – but, equally, you need to make sure that the legacy that sits in the core banking platforms behind it can fast onboard those. You could be formulating products very rapidly, but you need to be able to push them into the marketplace. So, the choice of the core banking platforms that sit behind need to be able to accommodate that so you don't get dragged down in legacy.
Q. So, a really interesting perspective around a core capability to get ahead as a provider is the ability to encompass machine learning faster than anybody else and to leverage AI in a way that is used to inform the decision making – particularly on more of the transactional services to enable that focus on the hyper-customized experience.
So with that, let me ask, where is the opportunity? It strikes me that as these organizations get better at using data within their own industry, and as other industries look to improve their data flows, where do you see banks using data from adjacent industries to create new products and services?
A. They need to, because [data] builds the ultimate picture of the consumer. So, if you pick a couple of parallel industries that are in their own digital arms race around data, let's take automotive as a good starting point. The car is very fast becoming a digital asset, as opposed to a physical asset. My phone is my car. My car is my phone. But it's also an emitter of payments; it's a payments platform. It's got subscription services from the car. The car is paying its way, either through infotainment or charging points, or even subscription services on the vehicle itself. Auto and financing, in terms of a transactional perspective, are becoming coming closer and closer.
But you then jump into the world of insurance. If my cars are sitting on the driveway 20 hours a day, why am I paying the same premium as someone who's using their car 10 hours a day? So the use of the car flowing into the insurance company means insurance companies can dynamically change their provisionings, which means huge amounts of money.
The benefit to the consumer is having a better premium based on the actual usage of the vehicle or multi vehicles. So you've got interchange between the financial services, the auto world, as well as insurance, and that links back into auto finance.
But if you take the world of utilities: digital backbone, rolling out 30-minute settlement on utility use (gas, electricity and water), you’re getting a greater insight into what you're spending and how you're spending, and what would be better tariff products. And if that's shared with the financial services space, you get a better picture of choice to the consumer and, ultimately, disposable income, and you’re back into the world of cross-sell again.
We can look at many other industries alongside it, but the ability to harness the right data from different industries is going to be key to really evolve and get a better encapsulation of what you are using. What does Khal look like on a daily basis, and then what products are going to be relevant to Khal to spend all that money you've been harboring at the end of the month?
Q. Well that's a scary thought in its own right!
As we close out, you've obviously had and are enjoying a very illustrious career. But if you were to go back in time, what one piece of professional advice would you give to a younger you, knowing what you know now, about how the world is changing with data in the banking sector?
A. When I go back 23 years ago, the banks used to be the epicenter of technology. Everybody wanted to go and work in the banks. Huge technology spend. But a lot of other industries are taking over, where technology and how technology is used and evolved, from areas like Netflix to car automation, even gaming. So the banks have got to catch up a little bit in that space.
Now the good thing about some of the topics we're talking about – banks and not the banks they used to be, where it was all about experience on that platform. [There are] new technologies coming in, [there are] fintechs coming in. Banks are buying fintechs so they don't have to go and build that themselves. So it is an area, and an era, of young talent to come in that doesn't necessarily need to have the years and years of experience of that specific platform to add value into the banking ecosystem.
And the breadth of technology is wide. There are so many beachheads into a bank now in terms of the technology skill set that's needed for the bank to build the ecosystem it needs to service the consumer. It creates many vehicles for those younger technologists that don't necessarily carry legacy, because banks don't want legacy; they want a fresh way of thinking, looking at a new toolset to look at things in a new way, because they need to embrace that. Otherwise they'll fall behind.
Listen to more in the Executive Data Series
The value of procurement data (Session 2)