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Credit Card Data: Sifting, Sorting, & Selecting

By Brian Riley
March 27, 2019
in Analysts Coverage, Credit, Fraud & Security, Personal Data, Security
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Credit Card Data: Sifting, Sorting, & Selecting

Credit Card Data: Sifting, Sorting, & Selecting

Remember the Rime of the Ancient a Mariner? Water, water everywhere, nor any drop to drink” said Samuel Taylor Coleridge as a sailor on a troubled ship was surrounded by undrinkable salt water.  It is a lot like a challenge facing the credit card industry today.  So much data, so many customers, so little time.  How do you make sense of all this?

Enter the Data Decisions Cloud, a strategic partnership between FICO and Equifax.  According to FICO, the Data Decisions Cloud integrates with the Equifax Ignite platform and FICO’s Decision Management Suite, a digital decisioning platform.  The joint effort enables credit managers to use best-in-class data and analytic tools to drive better decisioning.

It is interesting to note that Stanford University’s John McCarthy coined the word “artificial intelligence” in 1955, as he was describing a proposal for a research conference. FICO entered the world of predictive analytics in 1956, through the vision of Mr. Fair and Mr. Isaac, the “F” and “I” of FICO.  The two started “Fair Isaac” with a startup fund of $400 each, and the firm has been focused on the science of decision management and analytics.

When asked about the venture between FICO and Equifax, my perspective was:

“We know there is an overwhelming amount of data in the world and we know consumer expectations are on the rise as they demand highly-personalized engagement, in real-time. To compete in this dynamic market, financial institutions need to leverage artificial intelligence, machine learning and predictive analytics to find the key insights that will help them deliver differentiated and profitable customer experiences” and “The Equifax and FICO partnership underscores these trends and should help address the industry’s most challenging problems like streamlining the customer experience, improving data analytic capabilities, and reducing operating costs.”

“There is an overwhelming amount of data in the world, and we know consumer expectations are on the rise as they demand highly-personalized engagement, in real-time. To compete in this dynamic market, financial institutions need to leverage artificial intelligence, machine learning, and predictive analytics to find the key insights that will help them deliver differentiated and profitable customer experiences.”

Today’s WSJ points out:

  • For decades, most U.S. lenders have reviewed loan applicants’ reports and scores to determine whether to approve them and what interest rate to charge. After years of relatively cautious lending, banks and other lenders are seeking additional consumer data to help them make loans to more borrowers, including people with little credit history or with blemishes. Credit-reporting and -scoring firms have been pivoting to address the demand.
  • Deciding which loan applicants to approve is complex. Making risky borrowers look more creditworthy can expose lenders to default risk. Denying someone who additional data might prove creditworthy—like someone who pays their cellphone and other non-loan bills on time—would hurt revenue. Approving a consumer whose credit reports and scores look good but have other hidden red flags, such as minimal savings, would elevate the lender’s risk.

This move is important for Equifax, who is rebuilding itself in many ways since the 2017 data breach, and a relationship with pristine FICO will certainly add to their credibility.  For FICO, it represents another channel to market to the cloud, a technology interaction point that has long been a feature of the company, before the word “cloud” was defined.

Says the WSJ:

  • Equifax and FICO argue that buying their services together will help lenders make underwriting decisions more quickly, especially for borrowers with thin credit histories. They also say that lenders will be able to make more precise decisions about what products to pitch to a customer, such as a plain vanilla credit card versus a premium rewards card, and will be able to better screen people who apply for bank accounts.

Perhaps a tool to plod through all this data.  Big data is never as easy as you might think!

Overview by Brian Riley, Director, Credit Advisory Service at Mercator Advisory Group

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Tags: Big DataCreditEquifaxFICO

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