Now that FASB Topic 326 - Financial Instruments – Credit Losses has been released, the real work begins. Financial institutions are beginning to perform due diligence internally and with vendors. Regulators are beginning to review the FASB standard to determine how they will address future reviews. Third-party vendors from core providers to specialty enterprise risk management firms are also fine tuning their services. A review of the new accounting standard will quickly lead us all to one key element necessary for success – data integration.
Data integration is defined as a strategy of combining disparate data sources from different technologies in order to provide a unified view of the resulting data. More than any other task associated with CECL compliance, this is likely to be the key. During the past ten years, financial institutions and their partners have made significant strides in regard to data storage and integrity. The problem still remains, however, that it resides in disparate systems that currently do not communicate with each other. Integration of this data during the next 24 to 48 months will not only be critical to CECL compliance, it will be good for the industry in general. Consider this excerpt from FASB Topic 326. I have presented some of the more important data elements in red type for emphasis:
326-20-55-4 Because historical experience may not fully reflect an entity’s expectations about the future, management should adjust historical loss information, as necessary, to reflect the current conditions and reasonable and supportable forecasts not already reflected in the historical loss information. In making this determination, management should consider characteristics of the financial assets that are relevant in the circumstances. To adjust historical credit loss information for current conditions and reasonable and supportable forecasts, an entity should consider significant factors that are relevant to determining the expected collectability. Examples of factors an entity may consider include any of the following, depending on the nature of the asset (not all of these may be relevant to every situation, and other factors not on the list may be relevant):
Most financial institutions already have access to the data mentioned above. The issue is that it resides in multiple systems such as the core, the commercial loan system, the consumer loan system, investment tracking systems, vendor sites for underwriting and documentation, non-electronic credit files, and others. Development of a comprehensive CECL strategy is going to take a significant effort to pull these data sources together and then fine tune your organizations approach to the new standard.
It seems that it would make the most sense to compartmentalize first and then to bring the data into one common CECL summary database. For example, commercial loans will be analyzed using data from the core, the commercial loan system, the collateral management system, on-boarding/application tools, credit files, and a few more resources to pull in industry data for forecasting. Development of an expected loss standard for commercial loans may be much different than for consumer or purchased financial assets. Since CECL is not mandating a specific loss determination method, it makes sense to determine future losses at this higher level before integrating the data for “top of the house” evaluation.
The good news regarding CECL is that the standard allows for very subjective reasoning to determine expected credit losses. This is mentioned within the standard itself as shown in the excerpt below.
326-20-55-7 Because of the subjective nature of the estimate, this Subtopic does not require specific approaches when developing the estimate of expected credit losses. Rather, an entity should use judgment to develop estimation techniques that are applied consistently over time and should faithfully estimate the collectability of the financial assets by applying the principles in this Subtopic. An entity should utilize estimation techniques that are practical and relevant to the circumstance. The method(s) used to estimate expected credit losses may vary on the basis of the type of financial asset, the entity’s ability to predict the timing of cash flows, and the information available to the entity.
So where do you start?
In most cases, it makes sense to start with your core provider and then work through all of the data sources that are already integrated with the core. From there, you move outward to look at your core-agnostic systems to determine how that data will make its way into your integration strategy. Finally, you develop a strategy to bring in non-electronic sources of data as well as data from third-party sites for industry, demographic, and regional risk factors. Once all of the data for any given segment, such as commercial loans, is housed and evaluated, the loss forecast can be completed. Then the loss analysis from other lines of business can be added for a complete CECL analysis. Of course, all of this data is feeding continuously into the system revealing changes over time.
Now is the time to begin developing and implementing your strategy. With the first institutions expected to comply in less than 48 months, the clock has started and your work has begun.aaaaa
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