AI and alternative data transform credit risk, letting you focus precisely on the people you need to grow your business.
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
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.
Tags: Machine Learning, lending, Artificial Intelligence, linear model, interpretable results
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