It’s only fair. Predictive analytics is a win/win for both borrowers and lenders.

Posted on September 18, 2019

New CFPB study shows AI and machine learning can approve significantly more applications, while yielding lower average APRs; AI Lift proves itself twice as predictive as the competition

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Tags: Machine Learning, lending, AI Lift, Accelitas, Artificial Intelligence, linear model, interpretable results, Credit Risk Web Service, Credit Risk, Adverse Actions, Explainable AI, predictive analytics, CFPB, near prime, FCRA

How to weather the latest lending forecast: Let a Micro-Climate™ credit score guide you.

Posted on September 12, 2019

AI and alternative data transform credit risk, letting you focus precisely on the people you need to grow your business. 

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Tags: Machine Learning, lending, data waterfall, Artificial Intelligence, linear model, interpretable results, Alternative Data, Credit Risk Web Service, Credit Risk, Credit Scores, credit screening, predictive analytics, Alternative Lending, micro-climate score

What does traditional credit screening miss? Start with 70 million potential customers.

Posted on August 29, 2019

A new world of creditworthy customers are getting lost in the "invisible marketplace." Here's how our Credit Risk solution can help you find them.

They are the future of your business, the people who can help lenders reach aggressive sales goals in an increasingly tight credit market. They are 70 million strong and loaded with purchasing power. But according to traditional credit screening, they simply don’t exist. 

The fact is, as many of 30% of adults in today’s credit market are virtually invisible to traditional screening methods. 

Those traditional scores were designed to assess traditional middle-class and upper-class consumers who purchased houses and cars and used credit cards frequently, building up extensive credit histories over time. It turns out Millennials and Generation Z consumers just don’t fit that pattern. The oldest Millennials are now nearly 40 years old, but only 15% of Millennials have purchased a house.[1] Many will take Uber rather than buy a car, and prefer Venmo over Visa, but millions of these thin-file, no-file digital natives are genuinely creditworthy and just waiting to be your good customer. 

It’s a big problem. And a massive opportunity. 

But finding new growth will require greater risk. As risk grows, lenders who rely on traditional scores will be forced to limit their lending, increase their risk of losses, or miss out on the growing population of younger borrowers.

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Tags: Machine Learning, lending, Artificial Intelligence, linear model, interpretable results, Alternative Data, Credit Risk, Credit Scores, credit screening, Millennials, Gen Z, Alternative Lending, Credit Invisible

 

 

 

 

 

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