Machine Learning: The end for America’s Most Hated Company?
What do low cost airlines, The Weinstein Company, and for-profit colleges have in common? Unfortunately for Equifax, this is not the beginning of an off-color joke. In 2018, Equifax beat each of these companies out for the dubious honor as America’s most hated company .
Years of consolidation in the credit monitoring space has left consumers with little choice but to accept the status quo when it comes accessing credit. Three companies, (Equifax, Transunion, and Experian) maintain credit histories on approximately 200 million consumers and process nearly two billion items of information each month. Traditionally, a proprietary linear regression is applied to the data in this file to come up with a FICO score, which is then used in conjunction with the file, as the basis for lending decisions. This consolidation of power has been good for business, while perhaps bad for consumers. As of this writing, Equifax ($12.1B), TransUnion ($11.93B), and FICO ($5.5B) have a combined market capitalization over $29 Billion.
How can a company, and an industry with such low satisfaction in the eyes of consumers be so profitable? My assertion is that the cost of gathering and maintaining the data files provides an effective moat, protecting the entrenched players from competition from innovative challengers. To this point, in 2017, Equifax had $1.48 Billion Cost of Goods Sold on $3.36 Billion Revenue.
As data has proliferated, and computer processing power has become dramatically cheaper, Machine learning techniques pose an existential threat to Equifax. With alternative scoring methods, the cost of entering the market as a low-cost competitor has decreased to the point of eliminating the moat around a tired and stodgy business.
Again, unfortunately for Equifax, they seem to fail to understand the magnitude of the threat. Instead of revamping the entirety of their operation, they are making small and incremental steps to improve the process that they already use, without regard to the customer experience, cost, or satisfaction levels. Conventional wisdom holds that 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.” In this vein, “using machine-learning techniques has led to a performance lift of as much as 16.6 percent for bank card originations and 12.5 percent on auto originations of consumers with dormant credit files.”
Machine learning drives these gains by considering many variables simultaneously, looking for nonlinear interactions instead of considering one variable at a time. These increases are without a doubt a benefit to Equifax, and demonstrate the power of using machine learning in assessing credit risk. However, celebrating these gains without restraint, would be much like Blockbuster celebrating a commensurate increase in DVD rental income as Netflix and Hulu built streaming businesses. A quick review of CB insights reveals that funding for AI startups reached record highs in 2016 and include businesses across the spectrum of the financial services industry. Of the top 100 startups ranked by CB insights, 14 were in the credit scoring or direct lending space.
Rebuilding the moat
Equifax should recognize the barbarians at the gate and look inward. As cost to collect data decreases, they will have to compete on consumer satisfaction. Where do they have a sustainable competitive advantage and what has driven consumers to their abysmal approval rating of their performance? Before a startup can steal market share, I would refocus on the customer experience. For existing customers, I would use the informational advantage from the existing data set to provide the most accurate scoring possible, while using AI to find and correct errors in users files. I would grow by addressing the underbanked population in the United States, while establishing goodwill by displacing predatory payday lending services.
- What areas is a startup most likely to pry into Equifax business? (I.E. are auto, payday, mortgage, consumer, or education loans most susceptible?)
- What is Equifax’s biggest competitive advantage in the face of consumer sentiment and decreasing costs of accessing data?
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 VantageScore White Paper Highlights VantageScore 4.0’s Use of Machine Learning Manufacturing Close – Up; Jacksonville (Dec 16, 2017).