Data collection for ALLL calculations
Receiving accurate and reliable data has been a major challenge for banks calculating their reserves. Gathering data such as appraisal values, charge-offs or final months’ end reconciled numbers can delay the ALLL calculation. As referenced in Compiling the Best Data for the Reserve Calculation, there are four types of data critical within ALLL:
* Loan portfolio information
* Collateral valuations and loan data for FAS 114 (ASC 310-10-35) calculations
* Historical loss data for FAS 5 (ASC 450-20) calculations
* Supporting data for qualitative adjustments to FAS 5 calculations
Since banks must be prudent and document historical data to justify their ALLL reserves, the best way to prepare for the ALLL in the new year is to shore up data collection processes to provide for more granular analysis.
Obtaining data for the ALLL reserve calculation can be a time-consuming and error-prone process. Archiving historical data and capturing loan level detail is essential for financial institutions in forecasting related loss reserves and explaining trends in recoveries, deviations from historical patterns, sensitivity to variation and trends in delinquencies. By storing key data, it will inherently support the institution’s overall ALLL, especially for banks utilizing a migration analysis model. Banks working off spreadsheets might find it difficult to track changes in data and are more prone to data manipulability, which can compromise the integrity of the derived analysis.
Many institutions have been able to streamline the process by utilizing automated systems that integrate with a bank’s core to project ALLL reserves before quarter-end and even automate for up-to-date balances. Web-based ALLL solutions allow multiple users to work simultaneously rather than having one static spreadsheet that must be passed back and forth between bank personnel.
By using third-party vendors, banks are able to archive historical data information, eliminate the cascading effect over spreadsheets, increase consistency, provide defensibility and help prepare for future policy changes through greater data granularity.
For more information about prudent data collection for the ALLL, disclosure reporting and how to bring consistency in methodology, download the whitepaper ALLL: 3 Ways to Prepare for Year-End.