Federal banking regulators have said that while all banks have to comply with the shift to a current expected credit loss model, or CECL, for calculating the allowance for loan and lease losses (ALLL), every financial institution isn’t expected to implement CECL the same way. Indeed, regulators have noted they don’t expect “smaller and less complex institutions will need to implement complex modeling techniques.”
Nevertheless, the accounting change will require different data inputs, and some financial institutions may also decide to evaluate more sophisticated methodologies. In addition, some banks and credit unions may decide that using software to assist with the change to CECL is a good opportunity to automate a challenging legacy process. These concerns can include time-consuming and repetitive data extracts, version-control issues, and difficulty walking auditors and regulators through the process and data.
One bank’s approach
Each institution must consider its own size and complexity in determining the most appropriate approach to CECL. However, one community bank that decided to shift to an automated approach ahead of CECL has identified several benefits from its decision.
Camden National Bank of Camden, Maine, serves customers at 60 banking centers and has $4.1 billion in assets. Recently recognized by Celent as the winner of the 2018 Celent Model Bank award for Risk Management, Camden National completed its ALLL automation initiative in 2016. Like many financial institutions, Camden was using a complex Excel-based model for calculating its monthly ALLL. It implemented Sageworks ALLL, in part because the platform includes methodologies appropriate for both the incurred credit losses model and for the expected losses model under CECL. Camden (NASDAQ: CAC) is an SEC-registered bank, and these institutions must comply with CECL in 2020.
Benefits of automation
By implementing software and customizing it to its specific operations, Camden realized at least five benefits:
- The bank saves time. Camden had relied on repetitive data extracts from the core system and entry into spreadsheets that were time consuming to create and that required troubleshooting in the midst of calculations. Using an Excel-based model, institutions on average spend between 6-20 full days per quarter on the allowance, extracting and importing data and running calculations. After ALLL automation, the time the average Abrigo user spends drops by 80 percent — to between .5 and 2 days per quarter. At Camden, time savings have been applied to more in-depth risk management processes like stress testing, ALLL backtesting and CECL scenario planning.
- The bank has more confidence in the accuracy of the allowance. An antiquated Excel-based system opened the door to possible version-control issues, since even restricted access potentially could be overridden and risk data corruption. It also left the bank exposed to succession issues if someone involved in the process changed roles. The automation initiative controls and customizes user access, and the web-based solution solved data backup and other security concerns as well.
- The streamlined process reduces audit and exam risk. When auditors or examiners needed to understand how Camden arrived at its allowance under the Excel-based system, it took a lot of time to walk them through the spreadsheets, and documentation was hard to follow. The automated ALLL eliminates the manual management of compiling supporting documents, and allows bank professionals, auditors and regulators to go in and review necessary information in one place, boosting transparency and expediency. Not only has it reduced the time devoted to exams, but the bank is also able to function with more of a sense of ease as it approaches these exercises.
- Improved management reporting and portfolio insights help the bank quickly get information that can feed its decisions on lending policy, growth objectives and risk appetite. The Excel-based system didn’t allow the bank to look at risk rating migration or easily test different methodologies under various scenarios. Less time spent querying data allows more time for data analysis.
- Camden is better prepared to navigate the transition to CECL more easily. It will be ready this year to run full CECL calculations so it can gauge the impact on reserve levels and capital and can assess different methodologies to find the most appropriate fit. Through the cross-functional integration team formed in support of the ALLL automation initiative, awareness and understanding of CECL were fostered across the institution. This collaboration will be important under CECL when the allowance becomes more of a bank-wide concern.
Learn more about navigating the CECL transition.
Additional Resources
Whitepaper: A Practical CECL Action Plan
Webinar: CECL Methodology Webinar Series