A bank branch associate helps a customer
By Pete Daugherty | July 24, 2023

Five Ways You Can Use AI to Meet Your Banking Customers Where They Are (in Branches)

You need to know which associates, and how many associates, need to be available in branches, but outside variables are making workforce management hard for branch managers. That’s where adaptive AI comes in.

Forecasting is at the heart of efficient bank branch staffing and cost reduction. But in a post-pandemic banking world, branch labor planning and management will be more complex than ever. The largest driver of this complexity is the change in how customers consume banking services.

High volumes of transactions are migrating to self-service channels, such as online and mobile banking, and even in-branch “smart” ATMs. Over the past several years, even during “business as usual,” this shift made forecasting transaction levels more difficult. So, now that behavioral changes caused by the pandemic has accelerated these trends, increasing the challenges and burdens for your forecasting and scheduling teams, the question becomes, “What changes do you need to make to respond to, and even match, these behaviors in your branches?”

Change Management Done Smart

I know you were already forced to quickly make a wide variety of changes to your branch processes and operations these last three years. And naturally, you have had to rethink everything, from which branches can open due to available staff, to the need for plexiglass in front of all tellers, to specific cleaning processes, and so on. However, you’re going to need to re-tool your processes and adjust the workforce management tools you’re using if you want to keep your remaining branches profitable and efficient. Your people are the foundation of your business. No building can serve customers the way your associates can. So, you’ll need to be proactive in adopting new workforce scheduling models, in particular, if you want to keep your bank relevant to customers who have a growing number of financial services options available to them (including online banks with virtual 24/7 access to experts).

An accurate demand forecast can optimize network-level staff planning and enable you to respond faster and more effectively to changing market trends and consumer behaviors. For instance, if you’re seeing an uptick in customer wait times in certain branches and need to reduce those within the next month, a forecast generated in part by adaptive artificial intelligence (AI) can help you determine how best to staff branches the next few weeks to meet that objective. 

However, there are five things to keep in mind when adopting – and adapting – AI-powered forecasting practices to inform your decisions related to branch staffing in support of improved customer service:

1. Factor in All Banking Variables

Accurate banking forecasting typically requires a wide variety of variables, similar to those in other customer-facing industries, such as retail. They include government check days, holidays (including the lead up and days after), week of the month, day of the week, and month of the year, in addition to any bank marketing initiatives.

However, when these variables overlap on the same day, traditional forecasting models typically overstate what the actual transaction volume will be. What’s most important are the interactions among these variables, and how the variables will behave when two or more are present.

Deep learning and neural network forecasting models, by their very nature, take into account interactions between variables. It’s best to use a forecasting module that utilizes the latest and best-in-class forecasting algorithms designed specifically for the micro and macro trends that impact banking service levels. This will result in a more accurate result and help simplify the forecasting process.

2. Forecast with All Transaction Channels

Banks typically forecast all their transaction channels independently, which means you probably do too. However, it’s much easier to get an accurate forecast and understand customer patterns by looking at all transaction channels combined (in-person, ATM, mobile, and online). This is especially important now, as so much volume has continued to shift away from the branches. AI models allow for analysis and inclusion at a level that older models do not.

Changes at the branch level make much more sense in the context of overall transaction migration. Total transaction volume and the percentage makeup by channel are generally stabler and easier to forecast.

3. Build Potential Traffic Disruption into Your Models

Even before the pandemic, disruptions to branch traffic (including natural disasters, road closures and the like) were unfortunately common. That hasn’t changed. In fact, it seems that these disruptors are happening on a greater scale these days. We’re hearing of more frequent and sizeable storms, more extensive and prolonged road construction, and even changes in people’s daily schedules, which also impacts foot traffic patterns. Fortunately, with adaptive AI tools, you can explore the creation of new variables as part of the forecast to help your predictive model better understand the underlying customer patterns.

Examples include: “days since shelter in place lifted” or “percent of neighboring business open.” These variables are closely tied to customer sentiments and the likelihood of customers visiting a branch in-person.

4. Fix your Forecast Models on the Fly

Quickly tweaking your forecasting model is crucial. Using an accessible platform—where business users can directly control the variables and parameters, is paramount. But if you are dependent on your software vendor for assistance in variable selection or for creating new variables, you lose both flexibility and agility. Managers should be capable of doing such tasks without vendor or IT support.

5. Monitor Exceptions

Once a forecast is live, monitoring for exceptions becomes the core duty. To quickly and efficiently identify what areas to review, it’s crucial to have robust dashboarding and business intelligence capabilities. For example, reports that consistently highlight your top branch forecast variances accelerate corrective action. This visibility also simplifies communicating and sharing business exceptions with executives and field leadership. Ultimately, exception-based management can free your team’s time for more strategic initiatives.

Good forecasting, in conjunction with smart workforce planning and mobile-first scheduling, enables your associates to fully support customers’ changing needs. Instituting these five activities will help you ensure the highest possible forecast accuracy in this dynamic and variable environment.

To learn more about how Zebra Banking solutions can help you achieve optimized forecasting and scheduling, reach out today or check out our “Insights on Employee Retention” brochure.

Best Practices, Banking,

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