Benchmarking a credit union’s performance against other financial institutions is supposed to reveal insights to help accurately measure the credit union’s financial and operational well-being.
And it does when using the right peer groups.
Choosing a faulty peer group for benchmarking can be worse than not benchmarking at all because it can lead to reaching for standards that aren’t applicable to the credit union. For example, the credit union might end up focusing on a weakness that doesn’t really exist or maintaining the status quo in areas it mistakenly thinks are strengths.
The most common mistake credit union managers make when selecting peer groups is in not refining the comparison sets enough. Many credit unions rely solely on the broad asset ranges defined by the NCUA, but those don’t account for the numerous factors that impact performance.
Take the example below. These are two actual credit unions within the NCUA-designated asset range of $100 million to $500 million. Credit Union B is more than three times smaller than Credit Union A, has one-sixth the number of branches, places far moreemphasis on non-interest income, and has a significantly different loan portfolio composition.
| Credit Union A | Credit Union B | Average for Credit Unions $100M-$500M | |
|---|---|---|---|
| Assets | $479.6M | $137.2M | $223.1M |
| State | NJ | CO | – |
| Field of Membership | Multiple Common Bond – Primarily Healthcare | Community | – |
| Branches | 13 | 2 | 5 |
| Non-Interest Income To Total Income | 19.9% | 43.0% | 28.7% |
| Net Interest Margin | 2.9% | 2.1% | 3.0% |
| Return On Assets | 0.0% | 1.54% | 0.5% |
| RE/Auto Concentration | 75.6%/16.5% | 48.3%/37.3% | 45.4%/37.9% |
Source: Callahan’s Peer-to-Peer software.
These two credit unions are part of the same peer group, but they couldn’t be more different.
Although this is just one extreme example, it clearly underscores how an ill-defined peer group can become rife with ill-fitting benchmarking comparisons. To ensure peer groups account for more nuanced factors that impact the performance of your specificcredit union, combine some of these eight criteria when constructing a comparison set:
1) Narrow The Asset Band
Asset size helps account for the economies of scale that impact a credit union’s performance. However, broad asset bands like those provided by the NCUA pulls in credit union comparisons that don’t have similar buying power and efficiencies.Generally speaking, the narrower the asset range, the more apt the comparison.
2) Go With Geography
Local market conditions, such as real estate prices and unemployment rates, have an outsized impact on credit union performance. So whether it’s at the state or even county level, take geography into account.
For example, an Arizona credit union with a delinquency rate of 0.59% might feel good about its position compared to the industry average of 0.69%. However, Arizona credit unions boast a 0.46% delinquency rate on average. The new data sheds new light.
3) Focus On Field Of Membership
The demographic a credit union serves can have a substantial impact on its performance. For example, a government-based field of membership won’t feel the impact of an economic slowdown as much as a manufacturing-based one. Conversely, a governmentshutdown will have a greater impact on a government FOM than an airline one.
4) Bet On Branches
A credit union’s branch footprint definitely impacts its operations, which is why benchmarking against credit unions with a similar number of branches can be illuminating. The income and expense profile of a credit union can vary greatly based onthe costs associated with maintaining multiple branches.
Create More Accurate Peer Groups
For peer groups that offer appropriate data-based insights, try Callahan’s Peer-to-Peer. Quickly filter through peers using financial criteria, geography, core provider, business model, and more.
5) Consider Core Providers
Of all the suppliers a credit union uses, its core solution provider probably makes the biggest impact on performance. Efficiency, operating expenses, and even loan originations all tie back to the core processor. Using core provider as the basis of apeer group helps show whether the credit union is keeping up with others using the same provider or if another core solution seems to lead to better performance.
6) Look At Loans
Loan concentration data indicates business focus and internal goals. This is a helpful data set to bring in to identify credit unions with similar strategies and operations. Loan concentration also ensures loan-dependent ratios aren’t skewed. Forexample, a credit union that focuses more on autos will typically see higher loan yields than one focused on real estate. Therefore, a real estate-focused credit union might seem to be underachieving in terms of yield if enough credit unions witha high auto focus are skewing the average.
7) Income (Interest vs Non-Interest)
How a company generates income, whether primarily through non-interest channels like fees or CUSOs, or interest-based channels like loans signifies business model. To benchmark against credit unions that operate more like yours, define your peer groupbased on how your credit union generates the majority of its income. This impacts key performance and earnings ratios like ROA and net interest margin.
8) Recognize Strengths And Weaknesses
Every credit union has strengths and weaknesses, so take advantage of that. For example, if a credit union is struggling with auto penetration, it could be helpful to compare it against other credit unions in the same predicament. Alternatively, it couldcompare itself against institutions that are outperforming national averages in auto penetration to tease out the difference between its operations and those that are more successful.
Build benchmarking peer groups using the criteria above in combination or separately with Callahan’s Peer-to-Peer software. Within minutes, you’ll be benchmarking your credit union’s performance against peer groups you can rely on for accurate insights. Learn more about Peer-to-Peer.