While the on-paper goal for banks working toward their implementation deadline for the new Current Expected Credit Loss (CECL) impairment standard is compliance, the long game opportunity is much greater. Every decision in the process hinges on data.
The stated requirements for CECL compliance, as well as the documentation auditors and regulators will demand as part of the implementation process will require that your bank track, retain and document the underlying data behind the calculations. Especially for mid-tier and smaller ($10B and below) banks, this level of focus may feel like a challenge and a burden. In fact, it is a real opportunity.
From characterizing the size of each loan pool, the concentration exposure risks in your loan portfolios, your credit quality indicators (CQIs), or collateral types—it’s likely that you have access to massive amounts of valuable data that could position your bank for competitive advantages if dealt with appropriately.
We’re all aware of how much the banking landscape has changed in the past decade. The emergence and rapid adoption of digital banking, globalization, the use of machine learning like IBM Watson—all have changed the game. There’s no question that analytical intelligence has become a priority for banks looking at their long term and immediate operational strategies.
With that in mind, the intelligence, modeling, and reporting demands of CECL come at a particularly fortuitous time. Despite knowing how important data and analytics are to modern banking practices, many banks have been slow to establish a framework for identifying, gathering, retaining, and analyzing data. The new standard not only removes the sense that these things are optional; it forces institutions to prioritize and accelerate the process of putting format around intelligence related to their customers, lending products, and credit quality so they can begin to see the relationships between them and to build a historical basis for ongoing decision-making.
Outlining a Banking Intelligence Framework
It’s often said that “you don’t know what you don’t know.” That may be why banks—especially community banks—traditionally have made decisions based on personal, historical information. But particularly since digital platforms extend the perimeters of the customer base outside of who comes into a branch in your community, it’s neither strategic nor safe to lean on the idea that your bank knows the customer personally.
The growth, product mix, and capacity for services personalization in large banks like Wells Fargo and JP Morgan demonstrate the competitive value of an intelligence-driven strategy. In effect, by putting in place a framework to manipulate relevant data from across a varied customer base, they have created a capacity to see and understand new, useful correlations between needs, risks, and opportunities.
The ability to establish that kind of a framework is not unique to a global institution. Banks of all sizes can recognize a benefit simply by asking a core set of questions:
- What data do I already have?
- Do I have the data I need?
- How do I obtain the data I need?
- How do I retain and analyze the data so it becomes useful?
The answers to these questions line up directly with the steps your bank is probably already taking in the steps toward CECL implementation. You’re putting resources in place to house and secure the data, analyzing your loan portfolio and CQIs, establishing appropriate forecasting models, and keeping clearer records for reporting requirements. All of those efforts point directly to an intelligence base that has significant long-term ROI for your bank.
It’s important to set reasonable expectations. This is a long game play. It may take months or even a couple of years to see the correlations and impact of data intelligence on your loan portfolio. But even as those associations grow and solidify, you will be increasingly able to predict what might happen in your loan portfolio, be better able to identify risk and opportunity and be more agile with necessary adjustments.
Of course, you have the option to do the minimum to satisfy regulators and auditors in the CECL process. But by thinking beyond your implementation deadline, it becomes apparent that the more data intelligence you build, the more history, evidence, and correlations you’ll have for making confident decisions in the future.
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