Technologists Tackle Major Problem in Credit Industry

Data type

For their final projects, or Capstones, students in Georgetown University’s graduate professional degree programs typically work individually, with the assistance of their advisors. This spring, however, a group of technology students from the School of Continuing Studies pursued something more dynamic: they worked directly with business stakeholders on a real-world problem in the asset recovery management and collections industry. 

The semester-long project concerned the lack of common data standards, which results in a complex and expensive problem that affects many facets of the industry. On May 5, they presented industry leaders with their final report, which distilled the problem to its essence and made recommendations using a rigorous, systems engineering approach.

“Here we have an honest-to-goodness industry stakeholder who acts like an industry stakeholder,” said Adam Firestone, a Chief Technology Officer in industry and a faculty member in Georgetown University’s graduate program in Technology Management. That stakeholder has a problem, or a “gap” in its system, so the question becomes: “How do we turn this into a system—a reality—that not only fills the gap from a technical perspective, but also from a sustainable, viable, long-term business perspective?” 

The importance of reaching a business decision, not simply a technological one, was underscored by Stephanie Eidelman, CEO of the Information Architecture (IA) Institute—a media company serving the credit and collections industry—and a partner in the Georgetown project.

“This was a fantastic experience for everyone involved, because things don’t exist in a vacuum,” Eidelman said. “If you are going into management or leadership, there is no problem that is only technical, or only financial. Everything involves getting buy-in, understanding the marketing, and knowing who your customers are going to be. It also means getting the price right and making sure the business model works.”

An Expensive Problem

The problem was explained in a preliminary study last year. After 17 hours of interviews with IA institute members, the institute identified several problems that contribute to “friction, cost, and delay in debt collection process.” One of the top four issues is the high cost of on-boarding new clients because of a lack of interoperability among data systems.

“Here’s an oversimplified illustration of the problem,” the study explains. “One organization formats a phone number (123) 444-6789, while another organization formats it like 123-444-6789.”

That might sound trivial. But when those kinds of inconsistencies are multiplied many times, the time devoted to fixing them becomes a major expense.

And because big organizations that extend credit—for example, large retailers, hospitals, and telephone companies—generally outsource overdue accounts, that task falls to smaller debt services companies.

“So, every time there’s a new client,” Eidelman said, “you have to sit down and look at the spreadsheet and say, ‘OK, what does this field mean, and what does that field mean, and how can we standardize it?’”

Solving this problem was beyond the capacity of a group of relatively small debt services companies. But, as it turns out, it was perfect for the skill set of students and faculty in the Technology Management program. To grossly simplify the students’ 274-page final report, the solution involved designing an automated tool for ingesting, analyzing, and converting the data format, one that would maximize the use of machine learning and artificial intelligence and reserve human brainpower for those tasks that were beyond the computer’s ability. 

A Critical Exercise 

It all began with a process that might seem simple on its face, but is almost always a lot harder than it looks, said Firestone, the Georgetown faculty member. An engineer, or anyone, really, can often confuse a solution with the problem itself. When a problem is complex, like those that the students took on and that technologists and engineers typically face, defining it correctly is absolutely essential.

“Anytime I see a system failure, the first question that runs through my mind is: What problem did they think they were solving?” Firestone said. “And if you haven’t really gotten to the point where you understand what problem you’re actually solving, the odds of you getting the right solution are greatly reduced.”

If that explanation isn’t convincing enough, Firestone offers this quote from Albert Einstein: 

“If I had an hour to solve a problem, I’d spend 55 minutes thinking about the problem and five minutes thinking about the solutions.” 

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