Data Integrity

The usefulness of IRR measures depends on the integrity of the data on current holdings, validity of the underlying assumptions, and IRR scenarios used to model IRR exposures. A model’s accuracy depends on the assumptions and data used. Like any model, inaccurate data or unreasonable assumptions will render unreliable results.

Examination of the system’s input process should focus on the procedures for inputting and reconciling system data, categorizing and aggregating account data, ensuring the completeness of account data, and assessing the effectiveness of internal controls and independent reviews.

System data should accurately reflect the credit union’s current condition. When evaluating the adequacy of a model, management should consider the extent to which the model uses automated versus manual processes, how it interfaces with the credit union’s core systems, and the staff and expertise needed to run and maintain the model.

Per NCUA regulations § 741.3(b)(5)and part 741, Appendix A, Guidance for an Interest Rate Risk Policy and an Effective Program, the internal control process must be comprehensive enough to verify that data inputs are accurate and complete before running the system and generating reports. Inputs may be processed into the system manually, through data- extraction programs, or a combination of both. The credit union should establish internal control procedures to validate that input data, such as general ledger balances and contractual terms of transactions, are accurately captured. Further, the credit union should verify system inputs by reconciling the balances to other verified source documentation, such as the general ledger.

The most significant assumptions underlying the system should be documented and clearly understood by credit union management.

Last updated on December 06, 2024