Fighting fraud with data? Maybe…
One possibility comes to mind: perhaps shopping patterns are so statistically consistent, routine and as personal as DNA, that information about a person’s previous purchases — or even non-shopping activities — enables an algorithm to know if the customer is truly the person he or she says.
That is interesting. But, alas, the report looks at more prosaic things, ie:
PayPal, Amazon, and Google have all developed sophisticated analytical tools and infrastructure to identify patterns of fraudulent activity. Paypal, for example, has a series of Fraud Management Filters that screen payments and sort out transactions that warrant review because of their amount, their origin, or other factors that can be set by a merchant. […] PayPal and Amazon have developed fraud detection tools that depend on massive datasets containing not only financial details for transactions, but IP addresses, browser information, and other technical data that will help these companies refine models to predict, identify, and prevent fraudulent activity. PayPal and Amazon have had years to amass databases of the transaction details for hundreds of millions of customers across thousands of merchants.
The sort of filtering and checking described above (bold emphasis mine) involves no conceptual shift in how to use data. All that is being described is doing the same intutive techniques that one would have long done in a world of “small data.” The only thing “big” about it is that there’s a lot more data to sift through. But the firms are not using the size and depth of data to do anything novel per se.
This is a pity. The revolution that is taking place in other dimensions of the Internet industry is that companies can do entirely new things with a big data set that they cannot do with a small one. A former top Google executive once told me that Google Checkout was created in part because the firm realized that learning about a customer’s shopping pattern could better detect fraud, which is the key e-commerce stumbling block.
Likewise, at the O’Reilly Strata conference in February, hallway chit chat was about how a financial services firm might be able to more accurately predict whether someone will repay a loan using Facebook’s social graph than a FICO score, since best predictor if person will repay is if their friends repay their loans. (Actually, the example was told to me as if it were already being done, though not with Facebook’s data). Yet I think I’m safer considering it apocryphal until I hear it first hand.
Does anyone know of incredible stories of how “big data” is being used in new ways to reduce financial fraud? If so, comment here or email me directly.