By Katie Horvath
Traditionally in the credit union industry, the branch where a member opens an account receives credit for that member’s business. But what about scenarios where a member opens an account, say near where she works, but primarily does transactions at a branch near where she lives? What if she changes employment and now engages with a different branch near her new job location?
For long-standing members, should business from a single human end up divided across multiple branches where accounts were opened? Should all business from the human be credited to the branch where the first account was opened? What about crediting each branch for the transactional work associated with that location? What about scenarios where two branches are used primarily by the member – one for personal transactions and a different branch for business?
Should ownership of the member remain with the first credit union establishing the relationship, or switch between credit unions, depending on branch usage? What happens when a member opens further accounts online? Does the online and mobile business get accounted for as a virtual branch? How does it factor into determining branch profitability where the online business may be attributed to personal relationships established at a branch location?
Getting Complicated
Branch profitability calculations can get mathematically complicated rather quickly. Because single branch balance sheets are often deposit heavy or loan heavy, they are unbalanced. Some branch calculations include a correction by FTPing the branch balance sheet to determine earnings credit on deposits and funding charges on loans, in order to allocate net interest income.
Some include indirect expenses and overhead allocation on branch specific GL expense calculations on an asset risk weighted basis in an attempt to be more accurate. This works for brick and mortar but misses the mark on channel, product, and member considerations that should be part of the analyses.
When trying to assess branch profitability to analyze locations where new branches should be opened or branches that should be closed, the transaction type and volume at that location should be considered. But due to the traditional industry norm of viewing business from a member as if it all came from the branch where the account was opened, operational systems typically lack the ability to easily include transactional data in branch profitability analysis. The set-up is flawed unless the member does all transactional business with the same branch where the account was opened.
The Paradigm No Longer Works
As members increasingly demand and use mobile and online banking, and credit unions encourage this to save on operational costs, the paradigm no longer works. But because systems and operations are set up based on account origination and are not dynamic as member behavior patterns evolve, most credit unions are unable to include highly informative transactional data in analytics.
In fact, most analytics solutions do not include transactional data due to its volume and complexity, yet some of the most important insights are derived from AI/ML data mining of transactions.
At the end of the day, spreadsheets aside, we seek the ability to analyze data about branches, members and products in a myriad of ways and the data all too often is not in the right form or format to answer the business questions posed. This creates a situation where executives are not set up to be able to optimize and lower operational costs or drive revenue with decisions backed by data.
Finding the Solution
The profitability measurement should be changed to assessing profitability by channel and take the focus away from brick and mortar, location-based calculations. Given ATMs, ITMs, online transactions, mobile transactions and the like, brick and mortar profitability becomes a dated way to assess success.
Other ways to analyze profitability include assessing product/service profitability and analyzing branch/channel by product saturation. In this manner, branches or credit union channels selling a lot of a less profitable product can be identified and shifted to promote a more profitable product.
Another way to assess profitability is by creating a member-centric view of data to glean a 360 picture of the value of the member to the institution and then analyzing member value by channel to determine which channels are effectively growing member relationships for added business. The member’s behavior patterns lead to insights on best outreach modes and methods for superior member experience and the institution can target outreach to the member to encourage transactions by channels having higher profitability.
White Glove Service
Yet mid-market financial institutions rely upon personalized member service and relationships to thrive and grow business. This white glove service is key to success in competing against larger institutions. The shift to digital financial channels has many cringing because the mid-market will never out tech the big banks.
In the current digital landscape, members will never say that they do business with a credit union because they find online channels to be superior to that of the likes of Wells Fargo, Chase or Citibank. But this does not mean that credit unions be left behind as members demand digital interactions, and they do not need to lag in the push for digital transformation.
In order to achieve this reorganization of data into dynamic relational models ready for analytics, credit unions need to invest in an end-to-end data analytics solution that includes access to data scientists, data engineers, business analysts and other financial industry technical specialists.
Because community banks and credit unions will not typically have a data science department, or a team of data engineers to integrate, cleanse and manage bringing vast volumes of transactional data to analytics, there exists a gap between the technology and the ability for the mid-market to use it. This is why an end-to-end solution with built in access to experts is imperative. With access to experts as part of an analytics platform subscription, credit unions can level the playing field against the big banks to gain business outcomes from AI enabled insights.
Not About Being Cool
Digital transformation is not needed for the sake of offering a cool mobile financial services platform. Digital transformation is needed to analyze true branch profitability in the modern financial services channel landscape. Digital transformation is needed to leverage the data gleaned by years of personal member relationships (that the big banks will never have) to provide superior member experiences.
There is a massive volume of transaction level data that must be analyzed to better understand members and generate higher levels of personalization. This volume of data cannot be processed using traditional data warehouses. Credit unions need access to a data platform designed to ingest, process, calculate and deliver insights at scale, every day.
An end-to-end solution including the right technology and access to the right experts is needed to organize data into a form ready to answer the most important business questions, such as branch profitability based upon product saturation and member behavior.
Katie Horvath is chief marketing officer for Aunalytics, a data platform company delivering insights as a service. Prior to Aunalytics, she was CEO of Naveego, where she was the only woman CEO of a big data company in North America until 2021 when the business was acquired.