Validating your asset liability management model beyond checking the compliance box

By Sean Statz, senior manager, Baker Tilly

Financial institutions are required to have their asset liability management (ALM) process and model validated by qualified third parties. Model validations can include a wide range of procedures including policy reviews, sample testing, and assumption and results reviews. To maximize value, validations can be taken one step further by also providing a full replication of the model being used. Typically, ALM models are considered “black boxes” where the institution inputs the data and assumptions, and the model outputs the results. The outputs from ALM models are then used to drive major business decisions. Financial institutions should be able to have full confidence in their model and its ability to produce results that are reasonable for their organization. In order to do so, financial institutions should perform full replications of their model to not only better understand the fundamental aspects of a model but also to gain complete assurance in the outputs. Following is a discussion of the four main areas to focus on throughout an ALM model validation.

Model governance and compliance

ALM models often require complex methodologies which in turn require full support and documentation on every step of the process. Validations generally start with an evaluation on the model’s policies, internal controls and model documentation. The validation assesses if the model governance framework is sound and regulatory requirements are covered as well as provides any recommendations for enhancements with management. As compliance requirements continue to expand, transparency around the model, process and assumptions is key.

Data inputs

To produce meaningful results, the data that goes into a model needs to be clean and accurate. The next step in a validation includes a full review of not only the input data but also the historical financial performance data. Understanding the historical financial performance provides insight to how the institution has performed in the past and helps the validators better understand certain aspects of the model. For instance, analyzing historical trends in various loan and/or deposit categories provides the validator with a baseline of the institution’s operational strategy. This information can then be used to compare against the model’s assumptions for projecting forward.