Traditionally, the branch where a customer opens an account receives credit for that customer’s business. But what about scenarios where a customer opens an account, say near where she works, but primarily does transactional banking at a branch near where she lives? What if she changes jobs and now banks at a different branch near that new location? For long-standing customers, should business from a single person end up divided across multiple branches where accounts were opened? Should all business from that person be credited to the branch where the first account was opened?
Branch profitability calculations can get complicated quickly. Because single branch balance sheets are often deposit heavy or loan heavy, they are unbalanced. Some branch calculations include a correction by FTP-ing 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 general ledger expense calculations on an asset risk-weighted basis in an attempt to be more accurate. This works for bricks-and-mortar but misses the mark on channel, product and customer 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. However, operational systems typically lack the ability to easily include transactional data in branch profitability analysis. The set-up is flawed unless the customer does all transactional banking business with the same branch where the account was opened.
Modernizing the paradigm
The profitability measurement should be changed to assessing profitability by banking channel to take the focus away from bricks-and-mortar, location-based calculations. Given ATM, ITM, online and mobile banking and the like, bricks-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 banking 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 customer-centric view of data to glean a 360-degree picture of customer value, and then analyzing that value by banking channel to determine which are effectively growing customer relationships for added business. Customer behavior patterns lead to insights about best outreach modes and methods for superior customer experience. The bank can target its customer outreach to encourage banking by channels with higher profitability.
Yet mid-market financial institutions rely upon personalized customer service and relationships to thrive and grow business. The shift to digital banking channels has many cringing because the mid-market will never out tech the big banks. But this does not mean that mid-market banks need to be left behind as customers demand digital banking. Mid-market financial institutions do not need to lag in the push for digital transformation.
Because community banks 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 such as data scientists, data engineers, business analysts and other financial industry technical specialists is imperative. With access to experts as part of an analytics platform subscription, mid-market financial institutions can level the playing field against the big banks to gain business outcomes from AI enabled insights.
Digital transformation is needed to analyze true branch profitability in the modern banking channel landscape. Mid-market financial institution digital transformation is needed to be able to leverage the data gleaned by years of personal banking relationships (that the big banks will never have) to provide superior customer experiences.
There is a massive volume of transaction-level data that must be analyzed to understand the customers each as a market of one so that personal interactions can occur. This volume of data cannot be processed on traditional data warehouses. Banks 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 customer behavior, of mid-market financial institutions.
Katie Horvath is chief marketing officer of Aunalytics, a data platform company that works with community banks to accelerate their digital transformation.