Credit Card Customer Profiling and the Luhn Algorithm

From a Q&A with a VISA fraud prevention agent on reddit:

Some years ago, someone wrote a paper claiming he could get the age, gender and race only from the credit card purchase history. It worked very well. Today, with your full purchase information, we can even “guess” your income range, number of dependants and even weight. We have a statistical profile of every customer. We can even calculate the odds you eat at McDonald’s today, considering you ate there once every X days. 98% of the time, this model is very accurate.

One drawback is that it requires a lot of information. That is why it takes a few years and then, we are fully able to track you. In many cases, we compare the profile calculated from your purchase history to who you really are (and you thought they asked your income for credit validation) to further improve our models, and track fraud, most of all. It’s so sophisticated that if you order products a person in your group never ordered, your card will get automatically locked.

As I’ve mentioned previously, “I’ve always loved reading and learning about data mining and its applications”—this is no exception.

From this Q&A I also discovered the Luhn algorithm.



2 responses to “Credit Card Customer Profiling and the Luhn Algorithm”

  1. Paul

    I know for a fact that the Nectar card people know more about UK citizens than the government. Remember the nectar programme is not just shopping but insurance, loans, utilities etc.

    All those transactions leave one hell of a big identity footprint so unsuprisingly when you ask for a loan from many participating lenders in the Nectar programme, they ask for your Nectar card to authenticate you, not your bank card or passport.

  2. Wow–I had no idea of the extent of the Nectar programme. A quick browse of the list of participating companies in the scheme is quick exposing: The AA, Ford, the majority of the Home Retail Group… the list goes on. Very impressive and, yes, the extent of their consumer database must be very impressive.

    I was going to draw the obvious comparison to the Tesco Clubcard scheme. The services the Clubcard tracks are equally as diverse.

    Interestingly, the data for both schemes is actually maintained by separate private companies: Loyalty Management Group and dunhumby respectively.