BankBeat talks with Kim Snyder, founder and CEO of KlariVis, about better ways for community banks to process data.
What are community banks missing in their current data practices?
Kim Snyder: Banks need to be acutely in-tune not only with the state of the economy, but with the particular challenges their customers may face in a downturn. Already, millions of Americans are struggling to afford basic housing costs. According to a recent LendingTree report, one-third of consumers have paid a bill late in the past six months; 61 percent said it’s because they didn’t have the money. This trend can signal long-term unfavorable impacts on a bank’s bottom line. But timely and situationally appropriate intervention can help customers — and ultimately, their banks — weather the storm.
Doing so requires data. Banks can identify potential problems early, addressing issues and helping customers before problems escalate, as well as better understanding the exposure to risk. But the challenge is accessing that data.
Banks must be able to aggregate data and glean insights in near real-time to address problems early. They need to be able to see trends quickly, anticipate their potential impacts, and take action immediately.
What are some of the challenges banks face in that regard?
K.S.: Today’s banks are paralyzed by the mass volume of data generated by various siloed and antiquated processing systems and the inability to efficiently access that data. Consequently, most institutions rely on manual processes to aggregate data. It can take hours to review and analyze raw data in reports.
How relevant is that data, though? Because writers are spending an enormous amount of time compiling and analyzing rapidly-changing data, any insights derived may be outdated by the time they’re finished.
Adding to this challenge is accuracy. In manual processes, data collected is prone to errors. Banks are then operating with data that is not only old, but potentially riddled with mistakes. Report writers are competent to pull data together but often lack the true understanding of the business unit they are serving, causing multiple rounds of revisions.
As banks grow, the data problem only magnifies. According to a study from McKinsey & Co, the volume of data across the world doubles in size every two years. As the amount of data increases, it requires even more time to compile and analyze.
What are some advantages of enhanced data? How can it be presented to help banks and customers?
K.S.: Economists warn that we are close to — or already in — a recession, making it crucial to have a complete view of customers. By reviewing that data, banks can better identify early warning indicators on both the lending and deposit sides. This is vital, particularly for community bankers whose differentiator hinges on their ability to know their customers on a much more intimate level compared to their larger competitors.
As an example, a banker may uncover anomalies, like spikes in business credit usage, through timely data analysis. The bank can then pair the spikes in credit usage with activities such as an increase in past due loans, NSF fees or a decline in deposit balances to identify customers who may benefit from a phone call from their commercial relationship manager. Not only could this proactive outreach have a positive impact on both the client’s and bank’s bottom line, but it helps the bank build and foster stronger relationships.
Could AI play a larger role here?
K.S.: Banks are reimagining the customer experience and digging deeper into the potential of AI and predictive analytics to grow relationships. However, AI and predictive analytics are only effective when the data is complete and accurate.
Data is the foundation for AI. It requires large sets of information to render true value. This also means that bad data paired with AI could lead to missed opportunities for banks — or worse, it could lead to abandonment by trusted customers.
From emails promoting first-time homebuyer programs for retirees, to student loan offers sent to recent graduates, disconnects can lead to a perception of ineptitude. To prevent these types of errors, banks must prioritize data management efforts to ensure AI initiatives are successful.