Banks and credit unions have amassed vast stores of data gleaned from their customers and other sources. So much data, in fact, that figuring out what’s truly valuable, how to leverage it and then actually doing that effectively ranks among their foremost challenges.
Of course, the key to making good use of available data is in its analysis. This means having a plan that asks the right questions of that data. It means the many action verbs: gathering, checking for input quality, visualizing, modeling, measuring, double-checking for biases and errors, and more. And it hopefully means arriving at insights that lead to smart decisions or valuable new products or applications.
In response to customer desires, a top priority for many financial services organizations is developing more personalized products and services in their digital channels. To try to get there, they are tapping into their data reservoirs. Future success in this pursuit depends to a large degree on having the right people in the right positions—and they are using data and analytics for this as well.
In this month’s Executive Report, we focus on how some banks and credit unions are putting their data to work on their journey toward a more digital future.
Our lead article by contributing writer Dawn Wotapka provides a good overview of the core role that data and analytics should play as financial services providers seek that higher level of personalization in their online and mobile banking offerings, given the behavioral changes due to COVID-19 and rising customer expectations.
She makes clear that harnessing Big Data is a formidable task for a number of reasons, among them that the data is commonly stored in a decentralized fashion and in varying formats that require substantial work to find and then to standardize. The challenges arising from project scale and execution create many ways for things to get bogged down—as one of her sources puts it, “data lakes” can become “data swamps.”
While data science tends to be thought of in terms of bottom-line impacts, it is also being used to generate insights on the “softer” side of banking.
Our story by contributing writer Katie Kuehner-Hebert focuses on the use of data to identify gaps and inform decision-making affecting employee recruiting, career-pathing and retention. This can be valuable as financial services organizations work toward greater diversity, equity and inclusion in their workforces. Analytics can also measure how new employees fit into a bank’s corporate culture.
Jan Schwarz at Visier writes that fundamental changes underway in banking as a result of the pandemic require banks and credit unions to assess their workforce needs for the coming years and compare that to their current staffing and capabilities. This effort stands to be more of a job than it may appear, given the many moving parts. Data and analytics can play a critical part in charting a course.
Looking more broadly at the industry, the increasingly competitive landscape makes it ever more important for individual institutions to know where they stand relative to peers down the block, across town or across the country.
BAI analyzes account-level data collected from banks across America— including most of the 50 largest institutions—to create a series of benchmarking programs. In his article, Karl Dahlgren, managing director for research at BAI, writes about the value of benchmarking in not only providing snapshots in time, but also in uncovering market opportunities.
Other data and analytics themes featured in this Executive Report:
Carrie Stapp from Harland Clarke writes about how institutions can better use their “voice of the customer” programs to improve retention and boost profitability. Central to such an effort is gathering data that is both inclusive and actionable.
J.J. Slygh from Total Expert challenges banks and credit unions to “play for keeps” in their push to improve customer experience. As envisioned, this approach includes personalized interactions from the very start, along with concentrating more on anticipating customer wants and needs.
And Chris Stanley from Moody’s Analytics spells out how financial services providers can use analytics capabilities to create strategic responses to the new Current Expected Credit Loss (CECL) standard and, in doing so, help them manage risk and enhance customer experience.
Competition in banking will only intensify as tech firms and other deep-pocketed players enter the market. These nonbanks depend on data to establish efficiencies and make key decisions, and traditional financial institutions can do the same.
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