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A consumer's credit score used to be a commonly understood number - the time-honored FICO score - that banks all used in their underwriting. But banks increasingly are relying on dozens of scores that reflect a variety of data sources, analytics and use of artificial intelligence technology.
The use of AI offers lenders the ability to get a precise look into someone's creditworthiness and score those previously deemed unscorable.
But such scoring techniques also bring uncertainty: What it will take to convince regulators that AI-based credit scores are not a black box? How do you get a system trained to look at the interactions of many variables, to produce one clear reason for declining credit? Data scientists at credit bureaus and banks are working to find answers to questions like these.
The benefits of AI credit scores
There are two main reasons to use artificial intelligence to derive a credit score. One is to assess creditworthiness more precisely. The other is to be able to consider people who might not have been able to get a credit score in the past, or who may have been too hastily rejected by a traditional logistic regression-based score. In other words, a method that looks at certain data points from consumers' credit history to calculate the odds that they will repay.
Machine learning can take a more nuanced look at consumer behavior.
"A neural network more closely mimics the way humans think and reason, whereas linear models are more dogmatic - you're imposing structure on data as opposed to letting the data talk to you," said Eric VonDohlen, chief analytics officer at the online lender Elevate. The more complex reasoning of artificial intelligence can find things in the data that wouldn't be apparent otherwise.
And instead of considering one variable at a time, an artificial intelligence engine can look at interactions between multiple variables.
"It's harder for the workhorse, logistic regression, to do that," said Dr. Stephen Coggeshall, chief analytics...