You may remember a scene in The Matrix, where Neo sees the infamous wall of green, vertically-oriented code for the first time. Neo's new companion, Cypher, explains to the newcomer "You get used to it. I don't even see the code. All I see is blonde, brunette, redhead…". Machismo aside, he's suggesting that while the data may look unordered and unfathomably complex, with sufficient practice one can train themselves to make sense of it.

Transitioning from the 1999 classic to real life, a similar situation now exists with Open Banking data.

Open Banking data is incredibly complex. Yet it has the potential to give banks valuable and unique customer insights. Fully understood, Open Banking data can paint a picture of what customers are doing with other providers, reveal gaps in the banks’ own service models, and ultimately inform appropriate defensive and offensive customer strategies. But, as in our fictional example from The Matrix, unlocking that insight is no easy task. Open Banking data is not only unusually complex, but there’s a huge amount of it to work with.

Analysing Open Banking data therefore requires more than good data analytics capabilities. Because of the complexity and volume of the data, embarking on an Open Banking analytics programme also requires a team with sufficient data engineering skills to first transform the data into a format that analysts can work with.

In this article, we’ll first explore why the data is so complex, then start to consider what banks stand to gain from translating Open Banking data into actionable customer insight.

On your marks, get set… API!

Open Banking came along in a hurry. The Open Banking API data standards were published in March 2017 and had to be live less than 12 months later in January 2018. Banks, suddenly mandated by the government to open up their data to customer-approved third parties, had little time to make it a reality. They had to set up secure APIs at break-neck speed.

As you probably know, an API is essentially an automated entry/exit point within a system or database. It lets organisations share data securely and represents a reliable way of establishing a live feed of data (often in JSON format) between their systems.

On the surface, Open Banking is an information security nightmare. Banks must rank among the most risk-averse institutions imaginable. They’ve spent years tightening security, upgrading firewalls, blocking USB ports, and explaining to staff why they're still using Internet Explorer 7.0 in 2020. And all of this to prevent third parties gaining access to their customers’ details. So the requirement to provide an automated means of making customer data available to third parties is the infosec equivalent of punching a hole in a prison wall. It’s a potential vulnerability. And the risks around APIs are very real.

When APIs are configured imperfectly, hackers can abuse them to gain access to people’s personal data. Two high-profile examples include Uber and Facebook. In the last two years, both organisations have gained unwanted media attention after flaws in their developer APIs were exposed. In both cases, uninvited third parties gained access (or could have gained access – Uber’s deficiency was actually spotted by a good Samaritan) to millions of users’ accounts and their personal information.

So for the banks, it’s safe to say that building their Open Banking APIs under time pressure was less than ideal. Naturally, their focus was on security. And they did a good job. There have been no leaks, no reports of data being stolen through the banks’ APIs. But the speed at which they were built has caused another issue, little known outside of banks’ analytics departments: the data that the APIs generate for the banks themselves is so messy, so complex, so borderline unintelligible that even basic customer data analytics presents a challenge.

But should banks care? The Open Banking regulator - the OBIE - requests monthly reports, but they don’t require detailed customer insight. These regulatory reports began as little more than technical compliance statements and only recently started requesting basic customer statistics. That’s because the primary purpose of the OBIE reports is to ensure that banks are maintaining appropriate standards and availability of their APIs.

For banks, however, experience tells us that the opportunities presented by Open Banking data are significant. Open Banking offers a new and unique way of understanding how their customers are behaving with other financial providers. And it’s not limited to ‘just’ the raft of new Open Banking apps…

The Catch 22 of Open Banking

Open Banking was dreamt up as a way to loosen the big banks’ grips on the UK marketplace. It was designed to give customers more control over their data, providing an easy way to share their financial data with third parties. It was ostensibly a threat to the big banks. But Open Banking overlooked (or simply couldn’t avoid) the Catch 22 in this situation - that the big banks are themselves third parties to other big banks. So if you’re a customer of Barclays, you can use Open Banking to share your data with HSBC or Halifax just as easily as you can with Yolt, Cleo, Sustainably or any of the other 100+ apps made possible by Open Banking.

And this type of peering over the fence has long been a tantalising prospect for banks. For example, let’s take the measure known as ‘share of wallet’. This is a relative concept of success that refers how much a customer uses one bank compared to their other banks. But before Open Banking came along, banks could only reliably see their own pieces of the jigsaw, not what else was going on in their customers’ often complicated financial lives. Open Banking changes that. Now whenever a customer grants access to a third party to receive their data, their bank knows exactly who they’re signing up with, and can begin to infer meaning from their behaviour. Banks can start to piece the jigsaw together.

Unlocking the Value in Open Banking Data

And here’s the often hidden value in Open Banking data. Open Banking gives banks a valuable lens onto what their customers are doing with other banks and providers. Viewed at scale, that data can highlight trends that point to gaps in the bank’s own service (are our overdrafts too restrictive?), new competitive threats (why is this new aggregator app attracting thousands of our customers all of a sudden?), or significant changes in behaviours and value (do customers using Open Banking use us less after 12 months?). Open Banking data can give answers to these questions, and hundreds more like them.

But because the data gathered through the APIs is so massive and so massively complex (thanks in part due to the speedy API set-up described above), actually using Open Banking data to answer those questions is much harder than it ought to be. It requires not only a skilled analytics team, but a team that can first perform a high level of data transformation on the Open Banking data. Data engineering is required to make the data usable, to build pipelines that automatically prepare it for reporting, and finally to allow analysts to extract the deep insights that Open Banking data is so inherently capable of providing.

Perhaps it’s no surprise that many banks are content just to be completing the OBIE’s monthly reports on time. But those that take their analysis no further are missing out on potentially invaluable contextual information about their customers, and are missing vital pieces of the jigsaw to turn the threat of Open Banking into a competitive advantage.

Here at Optima, we’ve been working with Open Banking data since the very start. We know the complexities inside out, and can help analytics teams extract insight at scale, quickly and efficiently. If you have questions about Open Banking data and what it could do for your organisation, get in touch.