What the Data-Driven Bank of the Future Looks Like
Bryan Yurcan | Via Financial Brand
Banks have not effectively put the customer data they possess to work, much less the external data customers are also willing to share. With open banking redefining the entire financial landscape, failure to tap the power of data will become a competitive drag.
It is often said, banks and credit unions sit on so much relevant data about their customers that they should be able to anticipate every financial need and deliver highly personalized, targeted messaging and offers.
Two things inhibit that outcome. One, the data housed in most financial institutions is often not easily shared within different parts of the organization. Two, today’s consumers and small businesses have financial relationships with more banking providers than ever — many of them nonbank companies — making secure and efficient data sharing increasingly a core competency.
Dealing with those realities is more crucial than ever because the effective use of data in banking goes even beyond sales and marketing. In the near-future the most effective financial institutions will have data embedded in every decision, interaction and process.
“Organizations are capable of better decision making as well as automating basic day-to-day activities and regularly occurring decisions,” wrote McKinsey in a 2022 interactive report on data-driven organizations of the future.
The Financial Brand followed up with questions to three of the authors of the McKinsey report, adding other viewpoints as well. From that emerged characteristics of the data-driven bank of the near future, as well as some challenges that will need to be overcome.
Facilitating BaaS and Open Banking
Data is critical in enabling multiple use-cases for open banking, banking-as-a-service (BaaS) and the closely related concept of embedded banking, McKinsey observes.
In open banking, for example, one of the key use cases is for PFM (personal financial management), where data aggregators connect with a range of financial institutions to assemble customer financial data, explains Ritesh Agarwal, a partner at McKinsey. The aggregators then provide this data and analytics to both bank and non-bank clients so that these companies can train their risk models and provide personalized recommendations, says Agarwal.
Traditional institutions are coming to recognize not only the inevitability, but the advantages of such arrangements.
When it comes to BaaS, data can power partnerships such as with retailers for point of sale lending. In such cases banks can leverage data to suggest personalized financing terms for the customer based on analysis of that customer’s shopping patterns or behavior, Agarwal states.
Indeed, tech-savvy financial institutions can fend off the encroaching threat of fintechs by moving into the BaaS space to share their data and infrastructure.
“In a matter of years, access to this level of information will become table stakes for digitally native customers — so banks that begin now will be ahead of the curve, and likely rewarded with high demand,” Insider Intelligence observed.
Creating More Efficient and Effective Employees
Currently, many business problems that could be automated continue to get solved inefficiently by traditional, manual approaches, the McKinsey report noted. However, by leveraging data to support their work, financial institution employees are empowered to use innovative data techniques to resolve challenges in hours, days or weeks, rather than by developing lengthy — sometimes multiyear — road maps.
While regulated institutions can’t (or shouldn’t) throw caution to the wind, unnecessarily slow development work is no longer feasible up against bank and nonbank competitors using data-driven agile development practices.
Getting to this state means banks and credit unions will have to re-train current employees, hire new employees with varied skills sets and partner with data-savvy third parties in some instances, says Antonio Castro, a McKinsey partner. Some banks are starting to do this, he adds.
“The most ambitious banks have taken the position that developing and building internal [data] capabilities is of the utmost importance,” Castro states.
Banks will also need to make data a priority at the executive level, and create a Chief Data Officer role if they don’t have one already. That step could be the key to enhancing employees’ data skills.
“Banks will continue to need to evolve their leadership roles around data management especially in the context of evolving regulation, as requirements get more local and tailored within regions,” says Castro.
Real-time Targeted Communications
The rise of instant credit decisioning options — including buy now, pay later programs — is strong evidence that consumers want relevant, real-time product offers and messaging from the financial institutions they do business with. However, most banks fall short of meeting those expectations.
That’s why it’s critical for financial institutions to take advantage of real-time data at every customer touch point, says Kayvaun Rowshankish, a senior partner with McKinsey.
“It will be important to orient this toward creating value for customers — not exclusively [value] for the bank — including supporting client journeys beyond interactions with the bank, such as in retailer interactions and in the supply chain of commercial clients.”
Real-time data is the driver behind the proactive insights offered by the more sophisticated mobile banking and neobank apps. Huntington Bank was among the earlier institutions doing this with the launch of its Heads Up app that warns customers about possible shortfalls in covering expected costs in the next period, as well as reinforcing when they achieve a savings goal.
RBC’s NOMI chatbot and Bank of America’s Erica digital assistant and others do this and more, using artificial intelligence to make predictions based on spending dynamics.
Other applications include letting users know when a subscription free trial ends as well as flagging double charges at merchants.
Real-time communication APIs, or application programming interfaces, can facilitate an instant flow of data and chats between banks and customers. Key to their success is the ability of an institution to have the communication in one channel — say a chatbot — be available easily and immediately within another channel a consumer may shift to, such as a call center or branch.
Overcoming the Data Challenges in Banking
Of course, banks and credit unions can’t change overnight, and need to take a measured, but high-priority approach towards becoming a fully data-driven organization.
First, they need to get alignment and clarity around their business and data strategy. Or in other words, “what will they do with their data that will be distinctive and focus their efforts,” says McKinsey’s.
Then, institutions will need to move toward business-aligned, cross-functional data teams focused on key data assets, Castro states. They should also invest in automation to reduce the time spent on data clean up and “stitching” and to drive re-use, the analyst recommends. (“Stitching” refers to the process of acquiring and aggregating data from different sources.)
Real-time data (and the insights derived from it) should be shared across the organization and not shut up in silos, observes Jim Marous, Co-Publisher of The Financial Brand and CEO of the Digital Banking Report.
“When data and analytics are in the hands of a single unit within an organization, marketing and the overall customer experience are negatively impacted,” Marous states. “This is because data insights are no longer available in real time and there is a lack of data and insight democratization where all units of an organization have access to data for business decisions.”