Unleashing Data to Improve Lending Opportunities


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By Roy Urrico


Are credit unions missing out on lending possibilities hidden in data silos? TrackStar.ai, a Chandler, Ariz. startup believes its new, predictive application programming interface (API) could help credit unions offer more loan opportunities to members – all based on information already existing in its databases.


Fifty-three percent of Americans receive rejections for a credit card, loan, or car due to poor credit, according to a survey conducted by YouGov on behalf of ScoreSense. The study also found credit ignorance — not knowing their credit score; and having too many credit cards or none at all — as the main causes of the declines.


TrackStar.ai believes traditional financial institutions currently lose out on credit and lending opportunities. Another wrinkle is that many direct lenders have less restrictive lending requirements than traditional financial institutions. TrackStar.ai helps optimize the borrower acquisition and retention process by adding its API, built on a proprietary credit dataset, to the credit union’s existing infrastructure (e.g., lending platforms, consumer finance applications, risk models and POS financing).

Clint Lotz, TrackStar.ai

TrackStar.ai’s predictive artificial intelligence (AI) layer determines the member's future lending qualifications, the result of millions of new data points originating from over 15 years of credit data from over 30,000 lenders in the U.S.; and which negative items are subject to dispute/removal in the future thus raising a consumer’s credit score. This could allow credit union lenders to extend credit offers to members previously considered as risky loan applicants.


“Credit unions can really take advantage of all the innovations that have happened over the past 24 months and plug (TrackStar.ai) right into their business and join the fintech revolution as opposed to trying to work against it,” said Clint Lotz, president and founder of TrackStar.ai.


Alternative Data Releases Members’ Creditworthiness


Alternative data denotes types of information that augment traditional financial info. “In the realms of fintech or lending, alternative data is anything that you're not going to get off the shelf,” said Lotz. He explained data from the three major bureaus (Equifax, Experian and TransUnion), considered structured data, is pretty standard and has been for decades. “Alternative data is when you bring in any type of data that's not normally part of the decision-making process and use it as part of the decision-making process.”


Lotz maintained alternative data can come in various forms such as spending habits, the use of revolving credit lines, and even web surfing tendencies. “But the real thing about alternative data is sorting through it and finding out what actually adds value to the process.”


The use of alternative data at credit unions could help capitalize on its community orientation advantage. Lotz said, “When you're a member of the credit union, it's different than just being a customer of a big bank. You feel like you have some ownership. It always made sense to me that credit unions have this kind of underlying card that they can play, like an ace up its sleeve, if you will.”


The TrackStar.ai founder pointed out if you take the technology available today, like AI lending, and add that in to a credit union, it modernizes the entire member experience. “Now you are on par with the with the big banks that you're competing with, but more importantly, you are really on a more level playing field when you join the fintech community instead of trying to compete against it.”


Changes in the FICO World


For decades, a consumer’s FICO score has been the leading factor in determining credit risk and creditworthiness by lenders. But, Lotz anticipates a change brewing in the FICO-based lending universe. He talked about challenges to the relevance of FICO’s algorithm by consumers concerned about the use of their data and how it is impacting their lives; and lenders, who now also include different data points to assess a person's credit background.


“FICO is trying to prepare for an onslaught of negativity. But what I feel is going on is FICO is grasping. They are working with the bureaus, pulling all the data points they possibly can to come up with new and innovative ways to do credit scoring, like FICO 10 T trended data,” said Lotz.


FICO 10 and FICO 10T, jointly known as the FICO Score 10 Suite, are the latest credit scoring models from FICO, which designed it to be the most predictive and comprehensive credit score model it has developed to date. A key feature of FICO 10T is the use of trended credit bureau data on individual borrowers to calculate their credit scores, and providing a more complete picture of potential credit risk. Added Lotz, “I think FICO is grasping at straws. I think FICO is trying to figure out a way to stay relevant.”


As for TrackStar.ai, Lotz explained, “We just help lenders understand and digest what's on the credit report. You are making decisions on Americans' behalf, whether you know it or not. There's a lot of people that have been held back by that little three-digit number for all too long, who have realized that this is one of the things that's been holding them back for a long time.”


COVID’s Impact on Lending


Lotz also addressed the credit elephant in the room. “We've never had a situation like this to where there's a global pandemic that seriously disrupts our economy and our workforce.” This takes into account job displacement, programs like the CARES Act, loan deferments and other unfamiliar scenarios. “But it's coming at a cost of truth when it comes to reporting, because it's not quite clear if what's on someone's credit report is accurate.”


Lotz suggested lenders must ask “Are (credit applicants) in one of these (government) programs? Are they six months, 12 months, two years behind in their mortgage?” Lotz suggested that in many cases not every data point currently makes it onto a credit report. “So, from the lending standpoint, underwriters are looking at credit reports and trying to figure out “is this accurate?” And so, they are going a step further. Now we need statements for basically everything that's on a credit report to make sure that it is current.” That slowed the whole lending process down.


Lotz also noted there has always been problem errors and issues with data on credit reports. “There's a whole industry from all the errors that are in credit reports. And now we throw a pandemic and legislation on top of credit reporting. It is a complete mess and it is difficult for some people to work through it, unfortunately.”


Lotz believes this will lead to modifications going forward. “I think it’s going to change how consumers understand and comprehend what their options are (and) what their financial picture really looks like. They're not only looking for help monetarily, but they're looking for help with ideas and resources, and information that they can use to help better their situation.”


The TrackStar.ai API plugs into just about any digital lending platform, according to Lotz. “We're an Amazon Web Services (AWS) marketplace partner and have been for years. All of our solutions are cloud based, secure and made easy for deployment.”


Lotz acknowledged TrackStar.ai is currently working with some credit unions, but the company is seeking more financial institutions to implement its API.

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