Asset/Liability Asset Liability Modeling

Asset/Liability Management by the Numbers: How Are FIs Approaching ALM Models?

Kylee Wooten

November 25, 2019

Read Time: 0 min

Key Takeaways

A dynamic asset/liability management (ALM) process can inform sound decision-making in both strategy and risk – but not many FIs approach ALM this way.
Most FIs outsource their ALM model, rather than using a self-run model. Both have their pros and cons.
The majority of FIs report being asset sensitive for earnings and value at risk.

Why aren’t more FIs using ALM as a decisioning tool?

Many financial institutions view asset/liability management (ALM) strictly as a regulatory requirement. From a functional standpoint, ALM helps financial institutions make decisions on what loans, investments, and borrowings the financial institution should pursue, as well as the rates to offer, in order to make profitable loans while mitigating risks. Financial institutions that run ALM models specifically to comply with regulations will often look at risks like credit risk, interest rate risk, and some liquidity testing. But are we actively managing risk, or are we measuring and attempting to mitigate potential risks without necessarily thinking about profitability and opportunity today, asked Dave Koch, Managing Director of Advisory Services during a recent webinar.

The ALM process can be so much more than simply “checking the box” to meet regulatory requirements. Rather, with a dynamic ALM process, financial institutions are able to inform sound decision-making in both strategy and risk. When done correctly, a dynamic ALM provides the right guidance on profit maximization versus risk. There is significant room for improvement in this area for most financial institutions. A recent informal poll asked 100 CEOs and CFOs if they use their ALM results and models to guide decision making, and approximately 70% indicated that they did not. During the webinar, half (51%) of respondents answered that their financial institution used a static approach to their ALM model, and 38% responded that their FI did indeed use a dynamic approach.

So, why aren’t more financial institutions using ALM as a decisioning tool if they already have a tool for ALM? One of the most common reasons is a lack of time and staff available to dedicate to ALM. Financial institutions want to make a decision quickly, and many do not have the personnel or models to drive those decisions. Another setback is the viewpoint that the ALM process is viewed as a “regulatory requirement” and not a “profit center,” Koch noted in the webinar. With no regulatory push to do so, financial institutions do not have the external incentive to use ALM as a decisioning tool.

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How do FIs approach ALM modeling?

Due to the fact that ALM can be time- and resource-intensive, many financial institutions look to outsource it. During the webinar poll, 56% of those surveyed responded that they outsource ALM, which is in line with the market, Koch explained.

“We typically see about 60 to 70% of the market continuing to ask for help in setting the models up to run, and outsourcing services to leverage outside time, effort, and expertise. It’s not easy to keep everyone up to speed, so outsourcing is a nice backup option for many institutions,” Koch said.

Outsourced and self-run approaches to the ALM model both have their benefits and shortcomings. Outsourcing the model can be an attractive approach for institutions that may lack the necessary staff and/or resources to run the model themselves. However, each outsourced provider varies in scope, cost, and expertise, as well as varied systems used for assumption development. Oftentimes, using multiple systems can create silos of data. Credit loss information can come in from one area, deposit sensitivity levels come in from a different area, and individual stress tests are run in different silos. The outsourced model does require internal resources to manage the relationship and ensure that the model is pulling out the value, Koch said.

A self-run model also aims to combine the institution’s internal data into one system. “So, the real question becomes: Does a self-run, self-administered model provide you with the necessary flexibility to do the things that we’re talking about versus the outsourced model?” Koch asked. “To me, the same model is being run either way, but who’s going to be responsible for making sure that stuff’s being put together and run properly?”

Interest rate risk and sensitivities in ALM

Interest rate risk is one of the most important risks that institutions measure. Interest rate risk takes into account the institution’s earnings and market value to determine whether the institution would make or lose money as interest rates rise and fall. It is important to note that interest rate risk is not one measure, but rather, short-term and long-term. Both short- and long-term measurements indicate the impact on earnings capacity, but the main difference is the horizon evaluated. These measurements become increasingly important when considering sensitivity testing.

Financial institutions are either asset sensitive or liability sensitive. If interest rates were to go up, and the institution is asset sensitive, then the institution would have more assets repricing than liabilities, so it would be expected that the institution’s value would also go up because those assets would be able to increase at a faster rate than funding costs. In other words, earnings/value move in the same direction as interest rates.

On the other hand, if an institution is liability sensitive, then the institution would have more liabilities repricing than assets. Koch has seen an interesting trend happening recently regarding sensitivities. “Over the short run, they’ll make more money if rates go up, but, in the long run, if rates go up and stay up, then they might be more liability-sensitive in the long term,” he noted. This particular phenomenon is interesting, considering where we currently are in the rate cycle.

During the webinar, 73% of respondents reported that their current ALM modeling indicated that they were asset sensitive for earnings and value at risk, falling in line with Koch’s expectations. “Most financial institutions have been and continue to present themselves as asset sensitive,” Koch said. “A lot of institutions were hoping that rates would have gone up and stayed up a little more because that would improve margins. Recently, those rates have declined with the Fed, and that was not something a lot of institutions were looking forward to, and it’s showing throughout industry results.”

Thirteen percent of respondents answered that their ALM modeling indicated that they were liability sensitive for earnings and value at risk, and another 13% responded that they were asset sensitive on one measure and liability sensitive on the other. For these categories, Koch encourages institutions to “really think about the direction of the rates and what that means for decision-making.” If an institution is asset sensitive today, it is critical that it has plans to prevent rates from hurting the margin.

The real ALM question becomes, are financial institutions managing risk for reward, or measuring risk to minimize potential loss?

Based on the results, most financial institutions seem to be leveraging static models and running asset sensitive banks and credit unions. If interest rates are heading down or staying relatively flat, then what’s the most important thing we should do today to reward ourselves for the risk that we take, Koch asks. “The risk for many of us is, if rates go down or stay where they are, then we will make less money than if rates go up,” said Koch. Oftentimes, asset/liability management tends to focus on measuring the risk of minimizing loss, which is, “I don’t want to lose if rates go up.” However, many institutions are actually poised to win if rates go up, Koch noted. “So, we shouldn’t be guarding against that risk; we should be maximizing the opportunity to reduce that risk in today’s environment,” he said.

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