The Customer Effect

Explainer: How neural networks are changing credit scores

  • Credit bureaus are increasingly using machine-learning technology to calculate credit scores.
  • This new method is said to provide a more balanced assessment of an individual's creditworthiness.
close

Email a Friend

Explainer: How neural networks are changing credit scores

A credit score has a major impact on a person’s life. It’s the key to getting a car loan, a house or an apartment. The traditional way scores are calculated is a method called logistic regression, which means assigning a value to a number of factors in your financial life (for example, payment history, number of credit accounts, length of credit history) and weighing them.

But credit bureaus are now looking into other ways to determine an individual’s credit history beyond the result of a static formula. They’ve integrated machine-learning into credit scoring methods to get a more balanced picture of someone’s likelihood of defaulting on a debt. But it’s more complex that that — we break down the method. 

The basics
This technology essentially integrates machine-learning into the credit-scoring process. There are multiple tools: NeuroDecision technology is a tool developed by credit bureau Equifax. Other credit bureaus, including Experian, are also reportedly exploring similar ideas. The technology is modeled on neural networks in the human brain, so it can assess the interrelationships between the different factors rather just spit out a number based on a static formula.

“Each attribute can have multiple weights,” said Peter Maynard, svp of enterprise analytics for Equifax.  It allows you to better or more accurately predict the person’s likelihood to default.”

The process
Artificial neural networks mimic the way the human brain works, so a non-human has the ability to think through the data and assess patterns.

So the machine has the ability to process the data like a human. Perhaps an individual with a historically poor payback record made improvements over time — a machine-learning tool may be able to take those details into consideration.

“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, in an interview with American Banker.

The traditional method has been popular because of the ability to provide specific explanations to customers, Maynard said. By contrast, neural networks have been seen as a “black box” due to the inability to understand how the decisions were determined. What’s new with Equifax’s tool is the ability to find specific reasons to explain why someone is declined credit. “The algorithm Equifax created allows full transparency into how each consumer is scored,” he said.

The implications
There can be biased outcomes depending on the human that inputs the data. Some industry watchers are concerned that the use of machine learning to analyze financial data can generate biased results. How the data is fed into the system and the instructions that are given to the machine are important factors contributing to biased outcomes, said Kevin Petrasic, partner at international law firm White & Case.

“The program can have the very powerful capability to model itself over time and turn into something it wasn’t originally programmed to do,” he said. “It may come to the conclusion that loans to people in a particular zip code aren’t good credit decisions, or people who hang out in a certain social network are not good credit risks because of whatever associations they have with people in that network.”

Equifax doesn’t share these concerns — for now. Maynard said its product was vetted to meet regulators’ standards, including those of the through regulators including the Office of the Comptroller of the Currency, the Federal Reserve and the Consumer Financial Protection Bureau.

“Currently, we do not have concerns, said Maynard. “First, the sample of data used to build the model is algorithm agnostic, meaning we use the same sample of data for both logistic regression and neural nets. Second, we follow our rigorous compliance and model governance processes to review and approve the model for use.”

0 comments on “Explainer: How neural networks are changing credit scores”

The Customer Effect

The pandemic may be receding, but its effects on consumer banking aren’t

  • Covid has changed the consumer’s relationship with cash, savings, and digital banking.
  • It’s becoming increasingly likely that these changes will far outlive the pandemic.
Ismail Umar | August 18, 2021
Member Exclusive, The Customer Effect

Inside ‘climate fintech’: The fintech firms using carbon offsets to address climate change

  • Financial institutions are increasingly rallying around environmentally friendly initiatives.
  • Startups are turning towards carbon offsets to build a carbon-neutral future.
Rimal Farrukh | June 07, 2021
The Customer Effect

‘It’s the unifying login layer for commerce’: Bolt’s new SSO product eliminates the need for guest checkout in online shopping

  • SSO Commerce by Bolt enables smooth checkout for shoppers and higher conversion for retailers.
  • Consumers can open a store account and save their payment credentials in a single click at checkout.
Ismail Umar | May 20, 2021
Member Exclusive, The Customer Effect

‘What gets measured gets done’: The steps B2B fintechs are taking to improve customer success

  • It looks like B2B fintech is booming this year.
  • To stay in the game, B2B fintechs need to keep their customers happy. Here’s how they’re doing that.
Rivka Abramson | April 15, 2021
Member Exclusive, The Customer Effect

‘Like sneaker culture’: Are gimmicky debit cards overplayed or a smart business decision?

  • Revolut’s glow-in-the-dark debit card is the latest in a series of flashy debit cards to hit the market.
  • Experts say it’s a smart, cost-effective strategy that builds customers, brand equity and culture.
Shehzil Zahid | April 13, 2021
More Articles