Since starting a new role at Teradata with the responsibility of supporting our financial services sales and consulting teams across the globe, I’ve decided now is a good time to document some thoughts on the challenges and opportunities before us. Can a coherent vision and agenda for analytics truly transform capabilities, particularly in legacy financial services environments? Read on and you’ll find out.
To set the scene, what are the major pain points for financial services? Well, that depends on your perspective, but for legacy institutions there are a raft of challenges. Simply listing challenges isn’t a massively value-adding exercise; however, the General Data Protection Regulation (GDPR), the Payment Services Directive (PSD2), the requirements for API-driven “open banking”, challengers from the fintech sector, changing customer expectations, and operational risk are just some of the areas that are likely to keep financial services professionals awake at night. But it’s not all sunshine and roses for other players either — Atom Bank, for example, recently delayed the launch of a current account in the UK market until possibly 2019, citing the impact of impending regulation as the primary reason.
Atom cites GDPR, PSD2 and the UK-specific Financial Conduct Authority’s (FCA) review of the current account market as the primary causes for concern. It is perhaps ironic that a startup finds itself grappling with concerns around PSD2, probably for very similar reasons to a legacy organization; the economics of running a current account product within a disintermediated and highly competitive environment may transform the market considerably.
My first data project
Interestingly, the economics of the financial services market as a whole is one of the key challenges and opportunities where intelligent and innovative use of analytics in the broadest sense is likely to make a difference. One of my earliest data projects involved the delivery of a customer value project in a large UK retail bank. Unsurprisingly, when this project was completed, it showed that approximately 10 percent of the customer base were extremely high value, whilst approximately 10 percent of the customer base lost money for the bank. The remaining 80 percent were pretty much identical in terms of value generated.
These insights may have been a ‘gut feel’ for management previously, but to have real data proving this intuition is incredibly powerful. A whole series of strategic discussions open up around understanding customer value and how to manage customer relationships once you have data-informed insights. Use cases range from a debate about whether customer value should be displayed to branch staff (and how); whether more valuable customers should be prioritized for call handling; and, most interestingly, whether current value was a reliable predictor of future value.
Over time, this data project led to a decision to consider what I would describe as a ‘proper’ optimization solution for the bank. This was an early attempt to take all the known information about individual customers, as articulated through a series of model scores (value, product propensity, channel propensity, cross-sell propensity, risk and fraud scores, etc.) to support a ‘next best offer’ approach. That doesn’t sound particularly innovative or surprising, but this was more than 15 years ago. The innovative component was to match all customer-level information against a set of marketing goals (product value, product volume, customer value, customer volume) and marketing constraints (budget, channel capacity, available product volume) and create a series of scenarios where business managers could choose, for example, whether to campaign with a target of increasing sales volume versus a target of increasing sales value. Again, perhaps unsurprisingly, selecting a group of customers for a volume-oriented campaign produces a very different campaign selection versus selecting for value increase.
The point about this example is this: Because of advances in technology and the commoditization of what were at the time highly advanced analytics techniques, we should perhaps set our sights higher when we start to consider the opportunities for optimization in financial services. Whilst optimizing at a marketing campaign level can lead to significant incremental benefits, would attempting to optimize at a profit-and-loss or balance sheet level now be unreasonable or unrealistic? Given current analytical capabilities, possibly not.
Impediments to change
Now clearly, there are a significant number of immovable constraints which would obstruct a purist theoretical approach to optimization at an enterprise level — many of them being regulatory — but the point is that for many large financial services organisations, particularly the legacy segment, it is often not identifying and solving individual analytics use cases that causes challenges within organisations but the ability and the economics of implementing organizational change to deliver the benefits identified from analytics.
I remember back in the late ’80s, when I was an enthusiastic and fresh-faced analyst programmer in a large UK bank, a discussion about a particular change project that was proposed in my area of the business. This would involve a change to a core operational banking system, and to even estimate the effort required for the proposed change, a project of 400 man-days would be required. That was the standard work effort for estimating any change to that particular system. As a core operational system, all changes — regardless of complexity — required forensic analysis to ensure the integrity and continued operational robustness of the system.
Eventually this became one of my reasons to leave financial services. Despite being part of a team that was generally very successful in generating new ideas and insight, despite identifying significant business cases — sometimes with projected benefits running into the tens of millions of pounds — we hardly ever saw ideas translate into production. We couldn’t change the business through insight and analytics, because it was too expensive to change the business. It was too risky.
The case for developing a business vision based on data and analytics is founded on the belief that analytics can transform that business, but it needs to be seen as one of the key drivers of business transformation. The organization has to believe that it has data at its heart and that failure to maintain a constant agenda of development and innovation is likely to result in the eventual decline of the business.