“Data is the new oil,” data scientist Clive Humby famously said. Many in financial services agree, as they consider the mountains of raw data that banks and credit unions have amassed. However, they struggle to turn this oil into profits. Finding no simple process to extract it from disparate systems, refine it, and develop a product for sale, many have no idea what to do with all that information.
Big tech companies have successfully used data to personalize their products and drive innovation, so financial institutions have focused on personalization as data’s main use case. The financial and fintech press publishes son this that you could easily conclude that many institutions already do it.
“Most financial institutions still use data like they did two decades ago. They build ‘rearview mirror’ reports to make decisions about the future.”
The reality: Most financial institutions still use data like they did two decades ago. They build “rearview mirror” reports that their leaders and managers rely on to make decisions about the future. Financial marketing teams pretty much follow suit, even for their vaunted “next likely product.”
The disconnect arises not because institutions cannot identify what is possible. They have no clue where to start. Typically, each institution’s IT organization has built a data warehouse or a data lake. This improves access to the data and eases development of reports. However, institutions lack resources to transform data into actionable insights, enabling creation of better products and customer experiences.
Often, institutions fail to properly define a business case. This stymies gaining approval to hire appropriate resources. Sometimes, a vendor promises a great solution. But then management hesitates to release customer data when the vendor will store it off-premises where the institution has no control over it. And some institutions work with vendors able to solve some of their problems but unable to answer any request beyond the features already provided.
So, back to square one: The financial institution remains stuck with the problem of lots of data and no new uses for it.
But not all banks and credit unions have stalled. Some have found technology partners, often fintech firms, that can help them leverage data and find creative solutions.
They have made three key shifts in their thinking.
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1. Collaboration Can Bring Greater Value
Technology gaps stand between institutions and ideal use of their data. Typically they need a tool and don’t have it yet. The classic impulse is to build it themselves. But a growing number of smart institutions now move outside of their comfort zone to collaborate with fintechs.
Leveraging machine learning and artificial intelligence represent prime examples. Financial institutions would be pretty good at building their own data categorization and enrichment engines in-house. After all, they’ve known their data for decades. But some consider this strategic question: “Is this the optimal use of our limited resources?” More and more choose a partnership.
Productive collaboration requires that value, between the financial institution and the fintech, can be identified and delivered against. For instance, the tasks associated with developing a product or a solution can be assigned to the subject matter expert. Having a service provider clean up and categorize the data — which is labor intensive and best done by specialized fintechs — frees the FI’s own experts to work where they can really create value, like building better models.
2. Embracing Collective Intelligence as Means to Innovate
The second shift in thinking: Seeing fintechs not as service providers nor competitors, but as innovation partners.
Our current context is marked by rapidly evolving consumer behavior, technological disruption, and a growing number of new players. Even big players can’t expect to keep up with consumer and business expectations on their own.
One-way relationships with firms seen only as service providers cheat banks, credit unions, and the potential fintech partners themselves out of much that innovation offers. Bringing fintech partners into the conversation allows institutions to explore new perspectives and opportunities to innovate faster. This may be via a separate workforce to work on problems the institutions lack the capacity for. Or it could mean wholly new avenues of market penetration that a fintech (and its technology) makes possible.
This is a lesson that HSBC takes to heart, as it announced plans to open a second artificial intelligence lab, in Toronto. AI firms are invited to perform analyses on a giant dataset, which the bank hopes will generate insights to fuel product development and improve customer experience.
Obviously, smaller institutions don’t have the luxury of creating whole AI ecosystems. Some may not even be able to dedicate teams to work on their data. But that does not mean institutions should refrain from attacking complex problems with fresh perspectives via fintech partners. All parties stand to gain.
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3. Regaining a Sense of Control with Cooperation
A common and limiting misconception is that engaging with fintechs means financial institutions will lose control over their data and market share.
Actually, this concern is valid — that is, for institutions with no strategic direction nor any vision of how new technologies can help getting there. However, partnering with fintechs will help institutions with vision and direction to better meet their goals, delivering a better result to those they serve.
Of course, engaging in partnerships means trusting external experts. Sound strategic planning can pay off here. Institutions know well the regulations and requirements that govern their businesses. They have a broader perspective over the customer relationship and have earned their trust.
What must be built is trust between fintech and financial institution. One key to this comes in not just buying a black box — build a relationship when partnering to create machine learning and artificial intelligence data tools.
Accepting continual change helps here. Being able to actually add or change attributes allows financial institutions to avoid being stuck with models that are no longer useful in an ever-evolving market and customer base.