From Comply to Apply: How to Find Major Value in CECL Compliance Data11.05.2018
There is no "easy button" for dispatching with CECL. The new current expected credit loss standard will take time, effort and considerable planning. To be sure, CECL has resulted in broad, sweeping changes to credit measurement and reporting—and to meet its requirements, financial institutions must use historical information, current conditions and economic forecasts to estimate expected losses.
That’s not all. The new guidelines require institutions to collect, sort and analyze reams of data from various sources as well as alter methodologies to estimate expected losses. These guidelines apply to financial institutions of all sizes, including community banks and credit unions.
But what if it were possible to turn the pain of compliance into the benefit of strategy?
If data is the new oil, electricity or rocket fuel—pick your metaphor—then consider this: The CECL requirements mark the first time this much data has been aggregated at the individual financial instrument level. And once that history—that instrument-level data—is captured, good things can happen. With the right data, community banks and the industry at large can start to improve decision making around credit risk, interest rates and profitability.
Working (Hard) Toward CECL Standards
With less than two years to go, financial institutions should be working through the necessary steps to adhere to the new standards. The multi-year implementation period is meant to give organizations a chance to prepare. But time will pass quickly.
Instead of asking what might happen, instrument-level data can help your organization make something happen.
Rather than an estimate, CECL requires quantitative, measurement-based historical data through the contractual or behavioral life of a loan. Most auditors are advising financial institutions to collect seven to 10 years of data. The mandate to collect and store that much information can seem daunting, which explains why many financial institutions plan to partner with third-party providers as part of their CECL strategy. Employing a solution that enhances credit modeling also eases the burden; it also enables continual data analysis that optimizes the required reserve amount for every loan.
Credit has largely represented (and will continue to represent) an art form balanced by financial institutions' finance side—which has historically leveraged more insight and access to models, solutions and analytics. Unlike other requirements, CECL requires input, adjustments and new, higher levels of rigor from multiple teams throughout a financial institution. But for all the ramping up required, CECL ups everyone's game.
The Other Side of CECL: A Path to Good News
Though data usage for better decision making has always been encouraged, capturing it prior to CECL requirements has marked a step few wanted to take. Now that years of historical, instrument-level data will be collected and available to your organization, it makes sense to utilize it as a competitive advantage.
New insights will emerge that can move your organization from a reactive state to predictive or prescriptive analytics. How can financial institutions take charge?
Start by correlating data. Look at loan demand over time and other key factors for your institution. You can pool and correlate data in many ways: by collateral or type, including mortgages, auto loans, credit cards and more. You can further segment by cost center, loan officer, FICO score or geography. Consider what level of detail provides meaningful information for your organization. Does the data tell you something that might alter your strategies?
Analyzing data provides a solid foundation for understanding your markets and metrics. This includes how portfolios behave and where potent opportunities lie, and can help answer questions such as:
- Where will the market go?
- How will that affect your ability to earn a reasonable return on your asset base?
- Do you need to change your strategy to protect against potential rate changes?
You'll soon realize that the sorted, analyzed data offers insights that go far beyond credit loss. It can inform budgeting and planning for more strategic risk management. With risk analysis into interest rates, liquidity, credit, market and regulatory capital, additional loan and credit data helps forecast and reduce losses. It also helps generate more accurate budget projections. With those analyses in mind, your organization can build a strategy to become more competitive and profitable.
Your organization can also extend a risk-adjusted return on capital to include all of the credit elements previously out of reach for quantitative analysis. That can affect decisions on the prices you'll set or products you'll offer. Using data to drive strategic decisions can lead to lower overall risk and better managed return for every stakeholder, including borrowers. That's a remarkable place for your financial institution to land.
CECL Success: True Strategic Risk Management
Because credit risk has significant enterprise-wide implications for an organization, it's one of the most significant types of risk a financial institution takes: perhaps bigger than reputation, compliance, regulatory or market risks. To mitigate that risk, invest in people, processes and technology that will move your organization from the low end of the risk management curve—where compliance doesn't drive value—to true strategic risk management.
It is to your benefit to implement CECL processes now to help your organization surpass compliance and uncover business-boosting results. With the right data in place—and the strategy to maximize it—you can look at the goal of beating your competitors and hit that “easy button” after all.
John Dalton is director of product strategy management, in financial and risk management solutions at Fiserv. Reprinted by permission from BAI Banking Strategies, a publication of BAI, a Chicago-based financial services association and a leading industry partner for breakthrough information and intelligence.