What happens if right after you finalize your financial models and get ready to make critical pricing or balance sheet decisions, your head of risk modeling leaves your institution with little notice, and to your dismay, little documentation? While your former model developer was brilliant, your institution is now left with a range of financial models that may or may not be accurate or effective.
So many decisions and transactions affect liquidity, which is why financial institutions are taking extra steps to implement a robust liquidity risk-management strategy that will help identify, monitor, measure and control the institution’s day-to-day liquidity management and ensure they are adequately prepared for unforeseen liquidity demands.
If you have followed the news about MoviePass and its recent financial downfall, you may have read about declining stock prices and angry customers, and made a mental note about what not to do in business. However, there is one critical but less obvious lesson to learn from the MoviePass pandemonium – and it is one that just might keep your financial institution from going down the wrong CECL road.
If credit unions want to stay on track for implementing the Current Expected Credit Loss (CECL) standard, they need to have already completed several milestone tasks and to complete several more by the end of the year. Credit unions also need to be sure they have allocated sufficient time to complete the tasks that need to be finished between 2019 and 2022.
Regulators require the model validation process to ensure that financial institutions are properly modeling for risk. Beyond regulatory compliance, model validation also provides business leaders with confidence in their models and helps to reinforce or reassess business and balance sheet decisions shaped by model outcomes. Considering the importance of model accuracy and effectiveness, it is critical to understand hidden risks in model validation practices. Whether a financial institution develops its financial models internally or works with a third-party model software vendor, it is critical to ensure your model validation partner understands and considers hidden risks in model validation. Some such risks include:
Changes in driver rate relationships are key influences determining the Interest Rate Risk (IRR) position of most institutions. Today, it is commonplace for financial institutions to incorporate testing for basis risk and yield curve shape risk in their IRR analyses. Three elements are needed for a successful basis risk and yield curve risk analysis solution: Asset Liability Management (ALM) model setup and fine tuning; defining the appropriate rate tests; and effectively communicating the institution’s Net Interest Income (NII) IRR position
At banking organizations, financial model validations can be simply viewed as a necessary task on a checklist for following regulatory guidance. Some institutions also believe that the quality of a model validation is less important when the institution or business line is successful and when local, regional and national economies are all thriving.
As interest rates rise, financial institutions are revisiting whether an in-house asset liability management (ALM) model or a third-party (outsourced) ALM model is the best option for monitoring and assessing interest rate risk (IRR). Many variables and factors need to be considered when making such a critical decision. The following four factors will help institutions identify when it is best to implement an in-house model and when to outsource to a third-party vendor to ensure compliance with regulatory mandates associated with measuring and monitoring interest rate risk:
The 6 Tenets of Effective Model Risk Management
As financial institutions progress in the new economic cycle — a cycle defined by gradual interest rate increases, regulatory uncertainty and economic growth — it may be time to revisit the financial models and model processes used to facilitate interest rate risk (IRR) analyses and other risk analyses such as capital stress testing. The accuracy and effectiveness of a model is critical because its outputs may alter the accuracy and effectiveness of related models and impact strategic decisions.