How Experian is turning big data into big dollars
At Experian DataLabs in San Diego, a team of scientists is thwarting bad guys with math.
A top-five U.S. credit card issuer recently dumped about 6 billion transaction records on Experian DataLabs to see if its machine-learning mathematical formulas could do a better job of rooting out credit card fraud than the bank’s existing system.
Experian scientists used neuro-embedding/natural language processing techniques to understand the “syntax” of the credit card data, computer scientist Honghao Shan said.
Transactions with odd syntax — words out of place, if you will — popped up as potential fraud.
“We thought we had figured it out and went back to them,” said Eric Haller, head of Experian DataLabs. “They said, ‘How did you do that?’ … It turns out we reduced their false positives by half.”
Making sense of the tsunami of data from connected devices and other sources is a fast-growing field. Predictive analytics — the mathematical formulas that find patterns and draw conclusions from the data — are being applied to topics as diverse as smart cities, medicine and cybersecurity.
“We are going now from the monitoring era to really anticipating what is about to happen at that moment, or what will happen in the next hour or two hours so we can plan for it,” said Ilkay Altintas, chief data science officer at the San Diego Supercomputer Center.
San Diego has a small cluster of data analytics firms, particularly in the sub-specialty of searching for credit card fraud.
ID Analytics, Fair Isaac Corp. (FICO), Opera Solutions, Global Analytics and Experian DataLabs are among the companies in the San Diego area applying math to big databases to uncover useful information.
Many of these analytics firms have their roots in HNC Software, which was bought by FICO in 2002 for $810 million. Several HNC executives and scientists left after the FICO acquisition to start their own firms.
“There is this network from HNC that is still pervasive,” said Haller, who formerly worked as chief marketing officer at HNC. “Almost everybody keeps in contact with each other.”
Experian, a 17,000-employee global business services firm, is best known as one of the big three consumer credit reporting companies. Haller was managing products for its credit services group in Costa Mesa about six years ago when he pitched the idea of creating an internal research lab focused on analytics.
“We needed to invest in R&D,” Haller said. “I knew the people. I worked with them all down in San Diego.”
Experian gave Haller’s DataLabs experiment three years. It started with a small team and got off to a hot start. A major credit card firm hired the lab to build an analytics-based cross-sell marketing platform.
That led to other projects. The company built Extended View, which ranked the risk of lending to people with little or no credit history by looking at data such as gym memberships, rental history and magazine subscriptions.
It developed an analytics “sandbox” where large banks, retailers, mobile network operators and others merge their data — with the identity information removed — with Experian’s massive data sets.
At the three-year mark, Experian DataLabs was in the black, Haller said. It opened additional labs in Brazil and London, and it now employs 40 data scientists and engineers in San Diego. Revenue is in eight figures.
But that still is small for Experian, which generates $5 billion in annual revenue. Haller said the company wants to invest in making it grow, with a goal of pushing the envelope around data and analytics.
For example, nobody has figured out how to offer instant credit on mobile devices, Haller said. Experian DataLabs is working on that problem now, particularly convenient identity confirmation “so I don’t have to ask you 20 questions to confirm your authenticity,” he said.
Experian has not been immune to the rash of recent data breaches. In September 2015, the company revealed that hackers had broken into an Experian computer server that contained personal information of about 15 million T-Mobile customers.
The server was part of a business unit different from Experian DataLabs, a company spokesman said.
The server contained names, addresses and encrypted Social Security numbers of T-Mobile customers who had their creditworthiness checked before signing up for wireless subscriptions or device financing. Experian and T-Mobile provided free credit monitoring to affected customers.
Although there is more data available than ever, finding ways to make money off it remains a challenge. Most business models focus on better marketing, controlling risk and improving day-to-day operations.
But there are new areas where data analytics is being applied. San Diego’s CyberFlow Analytics, which was acquired last week by Webroot, taps predictive algorithms for cybersecurity.
CyberFlow uses machine learning to determine what normal traffic looks like inside a corporate network. It then flags anomalies as potential cyber threats.
According to industry research firm Gartner, 25% of large global companies will use big data analytics for security or fraud detection applications this year, up from 8% a couple of years ago.
New data sources also have fueled demand for analytics, said Bruce Hansen, a former top executive at both HNC and ID Analytics.
“There is all that social data that didn’t exist before. There is all this mobile cellphone data that didn’t exist before,” he said. “And now because of computing power, everything is just getting faster and faster.”
mike.freeman@sduniontribune.com
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