Artificial Intelligence Yields Real Results For This Cooperative’s Short-Term Loan Product

First Financial of Maryland FCU ($1.3B, Sparks, MD) is using staff smarts to create and fine-tune a short-term loan product that relies on artificial intelligence and machine learning.

The credit union’s AnyTime Express Loans offers $1,000, $1,500, or $2,000 with a fixed rate of 5.99% for a 12-month note. Borrowers can apply for loans through online banking or the First Financial mobile app. In turn, the credit union instantly funds loans upon approval.

AnyTime Express is an end-to-end loan application, underwriting, and fulfillment product that’s part of the credit union’s lineup of unsecured Anytime Loans. Members complete a mostly pre-filled application either online or through the credit union’s app.

“The AI/ML decision engine then decides to approve the application and fund the loan or pass it off for a second look to one of our human underwriters,” says Michael Powers, the Maryland cooperative’s chief innovation and strategy officer. “Approved members are presented with loan documents, and upon digital signing, the loan records are created on the core and the members have their funds available immediately.”

The product provides an alternative to payday loans and other short-term products, and the instantaneous nature of the process appeals to any borrower interested in quick access to money.

“Our vision for AnyTime Express is to deliver a low-dollar consumer loan that could be started at the time a member gets out of their car at a retail location and be finished with proceeds in a checking account in less time than it takes to get to the front door of the store,” Powers says.

Seizing The Opportunity

Powers says the credit union knew, fundamentally, it had an effective rules-based automatic approval engine in its LOS; however, many applications and especially subprime credit applications were not automatically approved. Instead, they were frequently approved in manual review.

“We saw the opportunity for machine learning to replicate this behavior and deliver more instant loan decisions and instant funding across the credit spectrum, from prime credit to subprime credit borrowers,” Powers says.

Since its deployment on Oct. 15, 2020, the system has processed approximately 3,000 applications; roughly 40% of those were received outside of business hours.

Powers says the approval rate moves between 60% and 80% because of fluctuations in the credit quality of the applicants. It sends those who don’t make that cut to manual review. The abandonment rate for instantly approved applications is only about 3%, one-fourth that of the abandonment rate for manually approved applications.

A Lift In Sales And Member Satisfaction

According to the innovation officer, management wasn’t necessarily seeking a meaningful reduction of approved application abandonment, but the difference is striking and persistent. He calls it the clearest quantitative example of member satisfaction around getting instant answers and instant funding.

“We all know the value of money; our instant-approval results show the value of timing,” Powers says. “Delaying a response for manual review by even a short interval can lose someone’s attention and discount the value of your offer.”

The credit union also has noted a lift in sales and internal efficiencies from the work.

“Before AnyTime Express, we had seasonal personal loan promotions in the summer and winter, but participation in those pre-approved programs had been waning for years,” he says. “We replaced those pre-approvals with an invitation to apply for an AnyTime Express loan at a rate discount, and volumes have recovered to healthy levels last seen years before. Additionally, AnyTime Express is lower cost and requires less processing labor than the previous pre-approval program.”

The Creation

Creating the AnyTime Express project was a three-stage process that began with a prototype of an AI/ML underwriting decision engine run in a controlled test environment. From there, First Financial built elements for real-time integration into the cooperative’s core and digital banking platforms.

The credit union then invited employees to submit loan applications in a real-time, controlled setting.

“We worked out a number of issues with this beta test and concluded we were prepared to release an improved version to production,” Powers says.

The final result is AnyTime Express is fully integrated with one part of the credit union’s core banking system, one part in its digital banking system, and a third part, the decision engine, running as a web service on-premises. The three parts communicate with one another and their respective back-ends to make it work, Powers says.

Further Refinements And Rollouts

AI/ML technology is all about continuous improvement, and First Financial’s system is no exception.

“There has been additional data collected on approvals, manual reviews, and charge-offs that we can incorporate into re-training the AI/ML system,” Powers says. “We found some notable improvements came from adding rules to the engine to handle some cases.”

Those refinements will help further reduce the number of applications that can meet credit union approval without being sent for time-consuming manual review and better prepare the system to handle the differences as the instant fulfillment process is expanded to other loans at the credit union.

Teamwork Makes This Dream Work

Powers himself brings a distinct skill set to the job. He has a Ph.D. in electrical engineering from the University of Maryland and joined the credit union’s staff in 2017 after 11 years as a member of its board of directors while he held robotics and imaging research posts with General Dynamics and then the U.S. Army.

But Powers is quick to point out that enterprisewide involvement was critical to the instant approval system’s creation and continued evolution.

His innovation strategy team built the core AI/ML decision engine; the IT department constructed the core extensions, online banking widgets, and server resources; and the lending department configured the loan product while determining credit risk and underwriting performance parameters.

“We also had consistent support from our compliance department to ensure fair lending standards were observed from the start and followed throughout,” he says. “Marketing, meanwhile, created the graphics that gave it a visual appeal consistent with our branding and banking app appearance.”

Management Buy-In And Hiring Insight

Powers adds that management buy-in was crucial across every department and functional area, including, he says, those closely involved in the project and others not involved but interested in the outcome.

“Our step-by-step approach to development let everyone see and build confidence in the results of each increment as it built toward an ever-more practical system in an increasingly realistic environment,” Powers says.

He gave a particular shout-out to the lending team for taking a big step into the unknown with impressive optimism, courage, and care.

“Despite the technical demands and considerable unknowns of the work, the team came together with strong collaboration and common purpose,” Powers says.

In true AI/machine learning fashion, the learnings here include how to hire for an enterprise that wants to innovate like this now and going forward. First Financial looks at a variety of characteristics when it considers a candidate. Powers says his favorite is the ability to tolerate ambiguity.

“The people who will be the most productive and the most valuable to a technology development in your organization will be those who can make progress toward the overarching intent of the work despite and sometimes because of limited information, conflicting priorities, unclear feasibility, and undefined methods,” he says. “These conditions are always present to some degree when something original and significant is to be done.”

— This article originally appeared on CreditUnions.com on July 11, 2022.