How Bridgeforce Data Solutions Automates Credit Reporting and Disputes on AWS

If you’re an individual or a small business owner in the United States with a credit card or any type of loan, you know your credit score matters—a lot. Even a minor error in your credit report can drop your score 50 to 100 points, affecting your ability to buy a vehicle, take out a mortgage, or get approved for a loan. What you may not know is that these errors are more common than you think. In fact, 80 percent of the complaints that the Consumer Financial Protection Bureau receives each year are not related to debt collection or fraud but credit reporting errors. And these errors can be time consuming for people to fix themselves.

Such errors haven’t gone unnoticed. For years, then-management consultants Matt Scarborough, Brian Reiss, and Michelle Macartney helped lenders deal with issues across the end-to-end lending lifecycle. That is where they identified data quality issues in credit reporting and dispute processes. The problem they kept seeing was that these lenders relied on manual sampling, which meant that a human analyst would pull and examine small samples out of a portfolio that contained millions of accounts. In other words, due to human limitations, it was impossible for the lenders to obtain a truly comprehensive picture of their data quality.

A blind spot in fintech

Noting a blind spot in the financial services market, Scarborough, Reiss, and Macartney co-founded Bridgeforce Data Solutions in 2016. It’s a small financial technology firm dedicated to providing automated credit reporting, compliance disputes, and benchmarking solutions. Their core product, the Data Quality Scanner® (DQS), enables companies to find credit reporting discrepancies in their data, before they snowball into disputes, complaints, or regulatory risks. “At its core, our main product helps with the quality of the data in credit bureau reporting ecosystem,” says Scarborough. DQS looks at every single account at the time of furnishing to the credit bureaus as well as month-over-month, subjecting the data to hundreds of rules that a human wouldn’t have the time to implement, all while searching for and identifying potential discrepancies.

In 2018, Bridgeforce transitioned DQS to AWS Cloud from its original on-premises solution. After the height of the pandemic, it became more widely adopted and demand grew by a factor of more than ten. The company found they needed to greatly expand the amount of data they were processing—quickly. “Because we were already in an AWS environment, that sort of scaling was something we could rapidly do,” says Scarborough. “That would have been obviously much harder if we were doing things out of our own datacenters.”

From concept to prototype

More recently, AWS helped Bridgeforce when they conceived a new product—one that focuses on the quality of the data being disputed. “Because it’s a large, complicated ecosystem with so many different players—lenders furnishing the data, credit bureaus receiving and handling it, and in some cases, system of record and servicing organizations getting involved in between—it’s easy for everybody to just point fingers at each other,” says Scarborough. “The challenge is that no single party will always be in the best position to address data quality, which is why everyone needs to work together on this.”

Bridgeforce’s new product, Data Quality Scanner®(DQS) Disputes Module creates first-of-their-kind credit bureau disputes insights and benchmarking that significantly improve outcomes and reduce operational costs. “By layering in our data quality rules at every step, we can see the end-to-end data journey and evaluate data quality at each step, we can also look and see which changes made things better, and which ones didn’t,” says Scarborough. “This allows all the stakeholders involved to identify the specific data quality issues occurring and understand who is in the best position to fix them.”

With support from AWS, Bridgeforce Data Solutions’s entire product development cycle only took two months—from first conceiving of the idea to creating a solid prototype. “That rapid product development cycle is something we’re really proud of—and a testament to the capabilities that come by being in an AWS environment,” says Scarborough. “AWS’ implementation of PCI 4.0 requirements and their extremely high uptime numbers (99.999%) has also led to us to not having to directly manage servers and worry about addressing issues and compliance needs, freeing our team to focus on these types of new product development opportunities.”

Conclusion

Looking forward, the Bridgeforce team will start to use more advanced cloud services, specifically Amazon SageMaker Canvas and Amazon SageMaker Data Wrangler, to explore how they might be able to use AI in new ways. While these projects are still nascent, the team is excited about how this technology will allow them to prevent credit reporting errors and disputes before they arise, making it easier for lenders to improve the outcomes for their customers and reduce the risks and costs for all stakeholders in the credit reporting ecosystem.

If you’re a small business interested in automating its processes, you can learn more about our AI solutions, find an AWS expert who can help, or contact us to get started.