Datasets:

---

Recag

/

Rp_CommonC_151

like 0

ERROR: type should be string, got "https://blogs.wsj.com/cio/2015/08/25/paypal-fights-fraud-with-machine-learning-and-human-detectives/\nCIO Journal.\nPayPal Fights Fraud with Machine Learning and ‘Human Detectives’\nKim S. Nash\nAug 25, 2015 5:03 pm ET\nHui Wang, senior director of global risk sciences at PayPal Inc.\nPayPal Holdings Inc.,\nStopping fraud is key to PayPal Holding Inc.’s financial health, according to Hui Wang, senior director of global risk sciences. “We have little wiggle room for error. Any one wrong decision will cost the company a lot of money,” she said.\nThe digital payment processor, which split from eBay Inc. in July, uses a combination of technology and human expertise to identify and stop potential fraud in real-time, during online transactions.\nMerchants are expected to lose 1.32% of revenue to fraud this year, up from 0.68% in 2014, according to Aaron Press, director of e-commerce and payments at LexisNexis Risk Solutions, which plans to release its annual “True Cost of Fraud” study Sept. 10.\nThe 94% increase is due in part to the increasing number of data breaches that “put payment credentials in the hands of fraudsters,” Mr. Press said in an e-mail. Plus, “card not present” transactions in e-commerce can be more difficult to police, he said.\nPayPal, Google Checkout and other digital payments methods account for 15% to 20% of fraud at merchants that accept such alternatives to credit and debit cards, he said.\nPayPal works to spot various kinds of fraud conducted by or against consumers as well as by and against merchants. For example, a data thief might create a new identity based on stolen credit card and personal information. A merchant might set up shop and not deliver products as promised.\nIn 2008 and 2009, PayPal tested several fraud detection packages, finding that none could provide correct analysis fast enough, Dr. Wang said. She declined to name the packages but said that the sheer amount of data PayPal must analyze slowed those systems down.\nPayPal processes more than 1.1 petabytes of data for 169 million active customer accounts, according to James Barrese, PayPal’s chief technology officer. During customer transactions, subsets of the data are analyzed in real-time.\nSince 2009, PayPal has been building and modifying its fraud analytics systems, incorporating new open-source technologies as they have evolved. For example, the company uses Hadoop to store data, and related analytics tools, such as the Kraken. A data warehouse from Teradata Corp. stores structured data. The fraud analysis systems run on both grid and cloud computing infrastructures.\nSeveral kinds of algorithms analyze thousands of data points in real-time, such as IP address, buying history, recent activity at the merchant’s site or at PayPal’s site and information stored in cookies. Results are compared with external data from identity authentication providers. Each transaction is scored for likely fraud, with suspicious activity flagged for further automated and human scrutiny, Mr. Barrese said.\nFor example, “a very bad sign” is when one account shows IP addresses from 10 parts of the world, Dr. Wang said, because it suggests the account might have been hacked.\nThe system tags the account for review by human experts, she said. “They might discover that the IP addresses are at airports and this guy is a pilot,” she said. Once verified, that intelligence is fed back into PayPal’s systems. Humans don’t make the system faster, but they make real-time decisions as a check against, and supplement to, the algorithms, she said.\nThe combination of open-source technology, online caching, algorithms and “human detectives,” she said, “gives us the best analytical advantage.”\nHui Wang\nJames Barrese\nPrevious The Morning Download: Court Upholds FTC's Power to Police Corporate Cybersecurity Next ATF CIO Rick Holgate to Step Down"