Some at financial organizations and other lending institutions may have breathed a sigh of relief when the Financial Accounting Standards Board announced a temporary reprieve from the current expected credit loss standard, or CECL, a new accounting standard.
With the deadline now set for January 2023, the extension minimized the sense of urgency for compliance for some. But don’t rest yet: it’s more important than ever that lending institutions understand just how long the preparation and implementation of new forecasting models can take, sometimes up to 12 months. And lenders should leave plenty of time after that to run the system in parallel with any existing forecast procedures.
Time will fly, so organizations impacted by the new standard need to assess and create an action plan sooner rather than later. Unfortunately, some may be left wondering, “Where do I start?” The answer: Start with data.
At its most basic breakdown, the new current expected credit loss standard is designed to move accounting practices closer to the realities set forth by the economy — that means ensuring accounting values consider the ongoing shifts in the economic market, as well as the idea that with financial reward comes financial risk. The only way to conveniently and accurately accomplish that is through historical and current datasets.
Data: a critical gap
In the past, institutions didn’t have a good enough reason to store historical data — particularly account-level information dating back to the previous recession. In fact, many lending institutions likely lack adequate and quality data to feed into forecasting models. Either historical data wasn’t stored in an easily accessible manner, or newer portfolios, such as fintechs, simply don’t have the historical data necessary for compliance. In any event, lending institutions of all sizes need to identify the gaps in data they will need to address in order to meet the requirements of the new accounting standard.
Regardless of the forecasting model lending institutions plan to use — whether it’s built in-house or leverages a third-party, such as Experian and Oliver Wyman’s Ascend CECL Forecaster — these models are only as good as the data fed into them. If data gaps do exist, these organizations need to determine sources for that data or adopt a modeling methodology that requires fewer data assets. Keep in mind, an ineffective model can lead to reserving more allowances for credit losses than needed, leading to lower profit margins.
What do lenders need to ask themselves?
Our economy is cyclical, and different portfolios perform better and worse under different economic conditions. As lending institutions look to fill in the data gaps, there are a few questions executives should ask themselves.
• Is the data complete and timely?
• What are the data governance practices?
• Does the data reflect the most recent recession?
• Does the historical data reflect portfolio-level or loan-level losses?
• Are there common data definitions across portfolios?
Secondary components to close the gap: infrastructure and executive support
Beyond accessing adequate and quality data, lenders should also assess their IT infrastructure. Compliance with the new accounting standard will require stronger support for an increased volume of data, enhanced security and integrity procedures, and more effective analytical capabilities. Additionally, the infrastructure should support data governance policies that will open the door for higher data quality.
Proper implementation of updated processes and procedures also requires strong executive support and a dedicated steering committee. Institutions must secure an internal commitment to provide the talent and technology necessary to ensure continuous compliance — that includes resources for risk management, finance, IT, accounting, auditing and operations.
That said, financial organizations and other lending institutions cannot be satisfied by identifying the gaps at the onset of compliance — it’s a journey, not a destination. Lenders need to continually identify potential gaps and search for new datasets, as well as test and refine their models under various economic scenarios. This simple mindset will help ensure lenders use the most up-to-date and effective models.
While time remains to comply with the new current expected credit loss standard, smart lenders won’t wait too long. A number of steps and processes need to be completed prior to selecting the forecasting models that meets their needs, and the sooner these organizations can identify any gaps, the better positioned they will be to succeed under the new standard. More importantly, these lenders can continue to offer affordable products and help customers improve their financial health.
Jim Bander is a senior business consultant at Experian.