Transformation Through Automation at Financial Brands

“I don't think that the right thing to do is think about, ‘Oh, look at the cost savings that we're going to have out of this,’ because that's shortsighted.” -Glenn Hopper

Terms like machine learning, artificial intelligence, and automation tend to make us envision a bleak future where humans are fighting robots. In reality, automation is helping people live better lives.

At the world’s most innovative financial brands, automation goes hand in hand with digital transformation. However, most financial executives still need plenty of education about what it means to excel in the digital landscape.

Sandline Global’s CFO Glenn Hopper is a financial guru who gets it. He believes that with proper oversight, automation separates the exceptional from the ordinary in the financial industry. 

From Science Fiction to Daily Reality

If Glenn Hopper’s name sounds familiar, maybe it’s because you read his groundbreaking book, Deep Finance: Corporate Finance in the Information Age. But Glenn’s really a science fiction writer at heart. Someday, you might be reading his still-in-progress space opera.

Meanwhile, Glenn has become an expert at helping people in the business world understand the difference between science fiction and science fact. Concepts like machine learning and artificial intelligence are all too real.

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Banking executives are trying to make data-driven decisions, but many of these decisions aren’t based on reality. Hunches and hypotheses strain to deliver positive outcomes.

The digital marketplace is now too complex and ever-changing to operate on instincts alone. What worked a year ago no longer works. Glenn has interacted with plenty of financial brands where hunches were limiting the capacity to accomplish true digital transformation.

Fraud, Loans, and Bad Assumptions

Banks and credit unions have always used data to drive certain parts of the business. Even the most old-fashioned bank has fraud protection in place that provides alerts when someone’s account is used in a potentially fraudulent way.

For example, if you live in California, a sudden grocery store charge in Texas can create a red flag. That’s a good thing. However, sometimes data-based assumptions lead to negative consequences for customers. 

AI and machine learning can help with this problem, but only when they’re carefully managed by human beings who apply common sense. Glenn shares an example from outside the banking world that illustrates the risks involved here. 

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Amazon used AI technology to identify its best possible new hires. They crunched the numbers on their current employees and told the AI, “Find more people just like these.” Unfortunately, this method introduced bias into the process that would prevent hiring minorities and maintaining diversity in the workforce.

In the financial world, we have an obligation to maintain fair lending. We can’t just look at the ZIP codes where people more frequently default on loans and say, “Sorry, we don’t lend to those ZIP codes.”

Lookalike audience data doesn’t always work to promote the type of equity that’s ethical and required by law. This point is essential to keep in mind as you explore adding automation to your processes.

Using Customer Maps to Illuminate the Voyage

Glenn views customer mapping as a smart method of creating wins for the customer on top of wins for the financial organization. To start creating customer maps, go back to the first touchpoints of any new/potential customers. 

From there, follow them through their entire customer journey. What type of lead prospecting did they encounter? What was the first message from your company that they acted upon? 

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Continue moving along their voyage into the next phase, where they become more engaged and do something like asking about getting a loan or setting up an educational savings plan. Look at what data you gathered about this touchpoint, where the data was stored, how it was analyzed, and how you used it to improve the customer experience.

Digital transformation isn’t a one-and-done approach. It’s an evolution. When you take the time to do this kind of deep diagnostic discovery work, it’s an enlightening process that ultimately benefits the customer and encourages them to stay loyal to your brand.

Why Technology is a Multiplier

Dan Sullivan at Strategic Coach is fond of saying that “technology is a multiplier.” Technology will multiply whatever you have, so make sure you’re multiplying good stuff.

When financial brands start working with their sales, marketing, and administrative teams to build more automation and AI into company processes, there’s always the potential to inadvertently multiply something bad. Suddenly you’re amplifying the wrong things and introducing additional chaos into an already chaotic system.

Glenn says the best way to avoid this problem is to stop viewing automation as a cost-cutting measure. Step back and take your eyes off the dollars and cents. As Glenn says, “It's not just turning over everything to the robots and saying, ‘You run the ship.’”

This includes how you view your people and their productivity. Don’t assume you can pressure people to deliver more than ever before simply because you gave them some new technology. 

Instead of glorifying automation as a way to cut jobs and drive people out of the process, keep people at the heart of it all. Free them from mindless tasks while empowering them with the opportunity to build more value.

What’s Your Automation Priority?

Automation is a complex issue that can feel overwhelming for the average bank employee. If you’re a C-suite executive, you likely have far more power to tackle this issue than a lower-level manager or front-line worker.

Glenn suggests that the best way for any financial worker to take a small but important step forward is to assess the data available in your daily work. Determine whether more could be done with it. Audit the data, analyze it, understand it, and open your mind to new possibilities. 

If you’re a customer service worker who can see how many customers you sign up for new accounts per month, analyze who these new customers are. Why are they your customers? Where are they coming from? What causes some of them to abandon the process? What can you and your company do to improve this?

Look at things like churn rates, overdraft fees, loan default rates, or anything else that’s in your sphere. Add as much historical data as possible and try to build a bigger picture. At every turn, ask, “Why?”

The next step is layering in predictive modeling and contingency planning. What could happen in the future? How would you react to it? .

Automation and machine learning can be enormously powerful when they’re used properly. In a world that’s increasingly depersonalized and commoditized, automation offers a way to learn more about our customers’ needs and lend a helping hand.

At every opportunity, use automation like a human, not a robot. Provide the personal touches that make the financial world feel more human than ever.