Tag Archives: credit-cards

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.

The Irrational Use of Credit Cards

Our irrationality toward money and inability to fully visualise the impact of distant events is how credit card companies thrive and many bank balances suffer.

That’s the conclusion one draws after reading this article from Time that looks at a number of studies showing that we fail miserably in making logical decisions about money when we use credit cards rather than cash.

As a species we’re just really bad at understanding costs that come later on. Instead, we assign a disproportionate amount of importance to what’s immediate and tangible. […]

Once we’ve got our card in hand, our behavior becomes riddled with irrationalities. In one experiment, Drazen Prelec and Duncan Simester of the Massachusetts Institute of Technology found that people were willing to pay twice as much for basketball tickets when they were using a credit card as opposed to paying cash. Credit-card spending just doesn’t feel like real money. In another study, Nicholas Souleles of the University of Pennsylvania and David Gross of the consultancy Compass Lexecon calculated that the typical consumer unnecessarily spends $200 a year in interest payments by keeping a sizable stash of cash in savings or checking while at the same time carrying a credit-card balance. In our heads, the two don’t line up.

Consumer Profiling and Credit Card Data Mining

I’ve always loved reading and learning about data mining and its applications in various fields. Because of this, Charles Duhigg’s comprehensive look at the consumer profiling practices of credit card companies was my favourite read over the weekend.

[Researchers] emphasized that the biggest profits didn’t come from people who always paid off their bills but rather from less-responsible clients who never paid their entire balance. […]

But giving credit cards to riskier customers posed a problem: How do you know which cardholders will pay something each month, providing fat profits, and which will simply run up a huge tab and then disappear?

[One] solution was learning to predict how different types of customers would behave. Card companies began running tens of thousands of experiments each year, testing the emotions elicited by various card colors and the appeal of different envelope sizes, for instance, or whether new immigrants were more responsible than cardholders born in this country. By understanding customers’ psyches, the companies hoped, they could tell who was a bad risk and either deny their application or, for those who were already cardholders, start shrinking their available credit and increasing minimum payments to squeeze out as much cash as possible before they defaulted.

There are some fascinating insights in the article, and throughout I was reminded of this Marissa Mayer quote (from her Charlie Rose appearance), taken from Super Crunchers—a book on number analysis and data mining:

Credit-card companies can tell whether a couple is going to get divorced two years beforehand, with 98% likelihood.

The validity of that statement seems slightly dubious, but I love it nonetheless.

Psychology of Credit Card Minimum Instalments

New research finds that the ‘recommended minimum instalment’ suggestions on credit card statements are more influential than previously thought:

Mr. Stewart presented 413 people with mock credit-card bills of £435.76 (about $650) that were identical — except that only half mentioned a minimum payment of £5.42. Participants were asked how much they would pay.

Among those inclined to pay the bill in full, the presence of the minimum payment hardly made any difference. However, those who wanted to pay just part of it handed over 43 percent less on average when presented with a minimum payment. In the real world, this would roughly double interest charges.

I can’t help feeling that The Economist and the author of the original paper are taking a rather naïve view in believing that these recommended instalments are there for the benefit of the consumer.

Surely a more realistic view would be that they are a ‘compromise’ between keeping a card-holder perpetually in debt (maximum profit) and preventing them from defaulting on the entire amount of credit (minimum profit)?

via Freakonomics

(I digress, but it’s worth noting that outside the UK many countries don’t have laws stipulating that these ‘minimum payments’ must cover the interest to be charged in addition to a percentage of the outstanding credit—in other words they are typically designed to keep the card-holder perpetually in debt!)