As a bank executive, you know your company has access to all kinds of data, and your job is to find the most valuable ways to use it. Data is an opportunity to understand your customers better and get an idea of their needs. According to Srini Nallasivan, chief analytics officer at U.S. Bank, this involves looking at behavioral, demographic and attitudinal data from multiple consumer touch points. It means looking at, “millions and millions of customer interactions and the data points they create. But that’s important to build the long-term relationship.”
Unfortunately, most banks are not in the habit of using artificial intelligence and machine learning to mine both their own troves of customer interactions as well as publicly accessible data.
Traditionally, the big banking business model was a self-contained environment. You had your loyal customer base and you offered them your newest wares. The model was product-driven and data was used primarily to calculate risk, if at all. You’ve likely realized that utilizing data to the its fullest extent would be nice, when you can get around to it.
Of course, there’s a growing urgency to utilize rich data now as challenger banks and fintechs move in. Using cloud-based technology and agile processes, competitors nip at the heels of banking giants and their customer base. Without decades of red tape slowing them down, newcomers can laser focus on the customer’s experience, building out highly personalized products and services. Their end goal? Making people’s lives easier.
However, traditional banks have the advantage of being a long-term trusted resource. You can compete with the new kids on the block if you can embrace things like open banking.
Will traditional banks get ahead of trends like open banking, rich data, real-time payments and use them to their advantage?
Why rich data, why now?
There’s simply no way around it, banks need to adapt quickly and do so in a meaningful way. According to World Fintech Report 2020, the top reasons why customers adopt banking services from non-traditional players include those looking for better features (39%) and personalized products (39%). If banks could access their data using AI and machine learning, surely they could compete.
In his new book “Doing Digital: Lessons from Leaders,” Chris Skinner discusses the benefit of using rich data to personalize and predict customer needs using a personal anecdote:
My favourite example of this today is the bank that works out I catch the underground every day but always pay day by day rather than buy a season ticket. That is because I do not have the funds for a season ticket. In this instance, the bank texts “Hey Chris, you could save €1,000 a quarter with a season ticket for €2,500. Swipe here if you want one.” Okay, so the bank if wrapping up a personalised offer in a loan but at least it has mined my data, worked out why I can’t buy a season ticket and offered me one based on predictive and personalised contextual information.
By using rich data, you can offer customers truly innovative products and services.
For example, you can set up a series of APIs for partners to access. Partnering with other companies can both widen your influence and allow you to offer new functionality to your own customers. For example, BBVA collaborates with Uber in Mexico to offer digital bank accounts and other services to Uber drivers and delivery partners using the Uber mobile app.
Sounds great! What’s the problem?
The problem with accessing bank data
We can talk all day about what traditional banks could do with data to compete with challenger banks and fintechs, but how much does that help? The big elephant in the room is the difficulty in accessing the data.
As of 2019, 46% of financial services organizations admit that they could be making better use of customer data to improve product and services offers.
Traditional banks were built decades ago during a time when all payments were physical pieces of paper. That’s how their technology and processes evolved. For most of these banks, their systems of record reside on mainframe computers. Trying to get creative with a mainframe is, historically, a losing battle.
Luckily, modernizing your mainframe and integrating with newer services and applications opens a lot of doors. Let your line of business execs go crazy. You could even use access to your legacy data to improve your business processes. For example, the World Fintech Report 2020 shows how Swedish Bank, Nordnet, uses rich data to improve their middle and back office processes by mapping 15 key customer journeys and breaking them down into smaller segments. Change is possible and it doesn’t have to involve scrapping everything that you’ve done before. Make the most out of your legacy investments by simply connecting them to the modern world.