How to find the remarkable value hiding in CECL compliance data

How to find the remarkable value hiding in CECL compliance data

By John Dalton, Director, Product Strategy Management, Financial & Risk Management Solutions, Fiserv

There is no “easy button” for CECL. Adhering to the new standards will take time, effort and considerable planning, but it is possible to turn the pain of compliance into the benefit of strategy.

The new current expected credit loss mandate (CECL)has made broad, sweeping changes to credit measurement and reporting. To meet CECL requirements, financial institutions must use historical information, current conditions and economic forecasts to estimate expected losses. The new guidelines require collecting, sorting and analyzing significant amounts of data from various sources as well as altering methodologies to estimate expected losses. These guidelines apply to financial institutions of all sizes, including banks to credit unions of all sizes.

The CECL requirements mark the first time this much data has been aggregated at the individual financial instrument level. But once that history – that instrument-level data – has been captured, good things can happen. With the right data, financial institutions can begin improving decision making around credit risk, interest rates and profitability.

Working 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 multiyear implementation period is intended to give organizations a chance to prepare, but time will go quickly. Instead of asking what might happen, instrument-level data can help your organization make something happen.
Here are six things you should keep in mind about CECL to keep pace with the rollout:

Build a team to support this process. Understanding the process and identifying the data needs is not something that should be done by one person. It should involve stakeholders from across the institution and be transparent.

Identify the data need. Each of the team members involved should have a unique perspective and understanding of data within the organization. Begin by identifying internal data components.  What are the aspects that allow aggregation of various types of loans and investments?  Then within each category, what additional elements allow for subpools?

Select your methodology for calculating ALLL. Decide what methodology you will use to quantify historic loan losses and how you will use that methodology to calculate current ALLL (Allowance for Loan and Lease Losses). Part of the calculation will involve a qualitative adjustment to loss levels, which can be a subjective level or can be determined using regression analysis.

CECL with forecasting and stress testing may allow your institution to improve and optimize long-term performance. With different forecasts in different stressed environments, forecasted ALLL may change. Understanding those changes and implications may enable better strategic and contingency planning to maximize profitability.

Gathering your data is just a starting point. One of the most difficult and time consuming parts of CECL compliance will be the data exploration, gathering and organization. Once that process is established on an ongoing basis, the process should be less onerous. However, it is important to continually review the loan loss evaluation methodologies, including how to utilize them to optimize performance.

If you haven’t started preparing for CECL, now is the time to catch up. Your financial institution may benefit from beginning the data collection step soon to keep pace with the CECL rollout.

CECL requires quantitative, measurement-based historical data through the contractual or behavioral life of a loan, rather than an estimate. Most auditors are advising financial institutions to collect seven to 10 years of data. Collecting and storing that amount of information can be daunting, which is why many financial institutions are partnering with third-party providers as part of their CECL plans. Employing a solution that enhances credit modeling also eases the burden, providing the ability to continually analyze data to optimize the required reserve amount for every loan.

Credit has largely been, and will continue to be, an art form balanced by financial institutions’ finance side, which has historically had 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. CECL ups everyone’s game.

The good news waiting on the other side of CECL

Although using data for better decision making has always been encouraged, capturing it prior to CECL requirements has been a step few were willing to take. Now that years’ of historical, instrument-level data will be collected and available to your organization, it makes sense to use it as a competitive advantage.

New insights will emerge that can move your organization from a reactive state to predictive or prescriptive analytics. Instead of asking what might happen, instrument-level data can help your organization make something happen.

Start by correlating data. Look at loan demand over time and other key factors for your institution. There are many ways to pool and correlate data – by collateral or type, including mortgages, auto loans, credit cards or others. 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, including how portfolios behave and where opportunities lie. 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 the data you’ve sorted and analyzed offers insights that go far beyond credit loss. Data generated for CECL can be used in conjunction with 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. Additional data also helps generate more accurate budget projections. With those analyses in mind, your organization can build a strategy to become more competitive and profitable.

With that level of credit data, your organization can further extend a risk-adjusted return on capital to include all of the credit elements that have previously been out of reach for quantitative analysis. That can affect decisions on the prices you’ll set or the 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 be.

Achieving 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 reputational, 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 go well beyond compliance to yield business-boosting results.

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