alphaspirit - Fotolia

How PayPal fights fraud with predictive data analysis

PayPal is using advanced predictive data analysis to help protect its users from fraud and preserve the service's brand. Constant evolution is the key to PayPal's success.

Scam artists continually find new ways to defraud unsuspecting people, and PayPal is fighting back with predictive data analysis.

By now most people are familiar with some variant of a common scam: An email arrives in your inbox from someone you don't know promising you a share of a large pool of money. All you have to do is send an upfront investment, often through a payment processing service like PayPal.

Constant attacks from fraudsters make life difficult for Hui Wang, PayPal's senior director of global risk sciences. She is tasked with stopping fraudulent transactions before they are processed. There's a lot at stake here for PayPal. When the service is used for fraud, it erodes the public's trust in the payment platform and damages the brand. Over time, this could threaten to diminish the user base.

To stop fraud and scams, Wang and her team have turned to predictive data analysis to identify potentially fraudulent transactions. Given the evolving nature of threats, Wang's work never slows down. "For PayPal, we are unique in the sense that our problems are very dynamic," Wang said.

Predicting fraud attempts with data analysis

To identify potential cases of fraud, Wang's team analyzes historical payment data to identify features that may indicate an attempted scam. Things like what type of device the requester is using, what country the request originates from and details from the user's PayPal profile all can be correlated with fraud. The team uses this data to build machine learning algorithms that assess each transaction for potential signs of fraud. Over time, the algorithm learns and sharpens its predictions.   

We have to continue to evolve our technologies to stay one step ahead.
Hui Wangsenior director of global risk sciences at PayPal

Given the evolving nature of threats to PayPal users, this learning approach to predictive data analysis has always been the goal of Wang's risk modeling team. But she said it's only been possible to implement in the past five years or so. Before that time, the computing power simply wasn't available to run complex algorithms on such large volumes of historical data.

But over the past five years a number of new technologies have emerged, particularly from the open source community, that have enabled Wang's team to move beyond traditional risk modeling. The team uses products from Teradata and Oracle for data management and from SAS Institute for analytics, but is becoming a bigger user of open source tools like Hadoop and Spark. Wang said the open source resources give them a great amount of flexibility in the number of tools they support, which enables data scientists to work with whatever they feel most comfortable.

"Many times commercial software doesn't meet our needs completely, so, in this case, open source really comes in handy," she said. "We are able to take them and do all kinds of adjustments ourselves. That really unleashed the power of our data scientists."

Knowing where to look for data scientists

Another part of Wang's success has been finding the right data scientists and engineers. When it comes to hiring new team members, she looks more for things like domain knowledge and curiosity rather than specific technical skills. Wang said technology changes, and good engineers should be able to pick up new programming languages.

She admits this approach is more difficult than putting together a spec list of skills. There's no place on a resume to list curiosity. But she said knowing where to look has helped make the hiring strategy workable.

"We've had a lot of success recruiting fresh-out-of-school students," she said. "These people have the latest and greatest on the technology side, and these people, we notice, are more open minded."

It's this open mindedness that Wang hopes will help PayPal compete with the fraudsters. Even as PayPal's technology stack has matured, supporting more advanced predictive data analysis and machine learning, so too have the technical tools available to scammers. Which is why for Wang, continued improvement is a must.

"We have to continue to evolve our technologies to stay one step ahead," she said.

Ed Burns is site editor of SearchBusinessAnalytics. Email him at eburns@techtarget.com and follow him on Twitter: @EdBurnsTT.

Next Steps

Test your predictive analytics models often to avoid common pitfalls

Predictive analytics helps produce better business decisions

How the New York Times sharpens its business approach with predictive analytics

This was last published in January 2016

Dig Deeper on Predictive analytics

PRO+

Content

Find more PRO+ content and other member only offers, here.

Join the conversation

2 comments

Send me notifications when other members comment.

By submitting you agree to receive email from TechTarget and its partners. If you reside outside of the United States, you consent to having your personal data transferred to and processed in the United States. Privacy

Please create a username to comment.

What business areas do you think are most ripe for the application of predictive data analysis?
Cancel
Predicting customer attrition before it happens, especially in the financial services market would be an application of predictive analytics that would generate a good ROI.
Cancel

-ADS BY GOOGLE

SearchDataManagement

SearchAWS

SearchContentManagement

SearchCRM

SearchOracle

SearchSAP

SearchSQLServer

SearchSalesforce

Close