Giving traditional credit scores a serious turbo charge

Posted on November 12, 2019
Read More

Tags: Machine Learning, lending, AI Lift, Accelitas, Artificial Intelligence, linear model, interpretable results, Credit Risk Web Service, Credit Risk, Adverse Actions, Credit Scores, credit screening, Explainable AI, predictive analytics, CFPB, near prime, FCRA, thin-file, un-scored, FCRA Compliant

AI Lift is giving credit risk a new angle.

Posted on October 8, 2019

AI-powered analytics "bend the curve" to reveal the creditworthy customers you've been missing.

Read More

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

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

Read More

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. 

Read More

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.

Read More

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

The Benefits of Crossing Disciplines in Artificial Intelligence

Posted on May 8, 2018

Steve Krawczyk, Director of Research & Development, Accelitas

At Accelitas, we’re dedicated to providing businesses with predictive insights that grow profitable accounts while reducing risks. And when it comes to the data analytics that deliver these insights, we believe in using the best tool for a job. To determine the best tool, you need to have well-rounded knowledge spanning multiple disciplines. It’s not sufficient simply to rely on one’s own area of expertise, even if that expertise includes PhD work. Work at the PhD level almost always requires specialization in a narrow topic within a single discipline. That tight focus is great for making incremental advances in a field of study. But it’s all too easy in post-graduate work to fall into the trap of keeping that tight focus when trying to solve the broad, highly varied range of problems that arise in the real world.

Read More

Tags: Machine Learning, lending, Artificial Intelligence, linear model, interpretable results

 

 

 

 

 

Accelitas Insights Blog

AI-powered insights for fast, fair, and frictionless access to more good customers.

Subscribe to The Insights Blog

Recent Posts