No time to lose. In 2019, lenders simply have to be smarter.

Posted on August 21, 2019

Inadequate credit scores. Tightening Demand. Changing Demographics. Can businesses grow without raising risks?

 

Despite rising FICO scores, credit card charge-offs are increasing.

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.

 

FICO scores no longer reflect consumers’ ability to pay their debts.

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.

 

Credit card debt is worsening for young Americans 

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? 

 

Demand for credit is declining even as credit risk increases.

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. 

 

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: lending, Financial Services, AI Lift, Accelitas, Artificial Intelligence

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.

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

Loan Rejection Rates Digging into Revenues? Dig Deep into Data

Posted on May 4, 2018

We're pleased to present this guest blog by Paul Greenwood, CEO and Co-founder of our strategic partner, GDS Link, a provider of technology solutions, analytical and consulting services for the modern lender.

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Tags: lending, AI Lift, credit risk management, Artificial Intelligence, GDS Link

Improve Lending Decisions with Machine Learning

Posted on November 27, 2017

Machine Learning enables computers to recognize patterns and take actions without first being programmed with built-in directives to do so. Machine Learning software “learns” through exposure to data, automatically refining its own ability to recognize and respond to patterns.

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Tags: Machine Learning, lending, white paper

Lend360 Recap: Helping Lenders Stop Rejecting Valuable Accounts

Posted on October 19, 2017

We’re back from the Lend360 Conference, a conference dedicated to exploring every angle of the online lending market.

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Tags: lending, AI Lift, Lend360

Why Lenders Should Focus on Profitability, Not First Payment Default (FPD)

Posted on September 4, 2017

Many non-bank lenders base their lending decisions on predictions about which applicants are likely to incur a First Payment Default (FPD)—that is, which applicants are likely to be late making their first payment on a loan. Lenders assume that by predicting FPD they will be able to predict which applicants are likely to default on the loans entirely, resulting in losses for the lender.

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Tags: lending, First Payment Default (FPD), data waterfall

Lessons from Finovate 2017

Posted on May 22, 2017

At this year’s Spring Finovate conference in San Jose, California, the consensus among attendees seemed to be that this year’s event was interesting, but hardly groundbreaking. The presentations from roughly 60 companies were mostly developments and refinements of ideas that had been presented in earlier conferences.

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Tags: mobile banking, Machine Learning, lending, Financial Services, Finovate, API, Employee Efficiency

LendIt 2017: The Time for Machine Learning Is Now

Posted on March 10, 2017

Photo Credit: LendIt USA

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Tags: Machine Learning, lending, Big Data, LendIt USA

 

 

 

 

 

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