Tags: Machine Learning, lending, AI Lift, Accelitas, credit risk management, Artificial Intelligence, linear model, interpretable results, Alternative Data, Credit Risk Web Service, Credit Risk, Adverse Actions, credit screening, Explainable AI, predictive analytics, CFPB, near prime, FCRA Compliant, Credit Reporting, Credit Reporting Agency, credit decisions, compliance, AI-Powered Analytics
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-powered analytics "bend the curve" to reveal the creditworthy customers you've been missing.
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
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
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
AI and alternative data transform credit risk, letting you focus precisely on the people you need to grow your business.
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
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.
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
Inadequate credit scores. Tightening Demand. Changing Demographics. Can businesses grow without raising risks?
In the first quarter of 2019, charge-offs among card-issuers increased to the highest level in seven years, even while FICO scores rose overall. When credit scores rise along with charge-offs, it’s time for lenders to re-evaluate the scores they’re relying on for lending decisions.
Credit scores may have risen, but that doesn’t mean high-scoring borrowers are on firm financial footing. 40% of U.S. households would have trouble raising $400 to cover an emergency. Additionally, Goldman Sachs and Moody’s Analytics recently claimed certain FICO credit scores have been artificially inflated over the past decade.
Specifically, 8.05% of outstanding credit card debt among 18 - 29 borrowers was delinquent by at least 90 days. If young consumers, whose scores weren’t affected by the Recession, are struggling to make payments now, how will they fare when interest rates rise or the economy falters?
It gets worse. At the same time that credit risks are increasing, demand for credit is falling. According to the New York Fed, credit inquiries in the last six months have fallen to historical lows.
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.
Tags: lending, Financial Services, AI Lift, Accelitas, Artificial Intelligence
Artificial Intelligence (AI) is getting a lot of attention in the financial services market - and rightfully so. AI promises to deliver more accurate predictions of creditworthiness for lenders. It also promises to be useful in detecting fraud. In this blog, learn how lenders need more than AI platforms and new analytical models, to realize its full potential.
Tags: Identity Intelligence, Artificial Intelligence, Alternative Data