Tag Archives: money

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.

Want Happiness? Buy Memories, Not Objects

In one of my very first posts, I wrote about an article that noted how “money will make you happier, up to a point. After that, it makes no difference. That point is the wonderfully quantitative ‘point of comfort‘.

That is, once we have enough money to feed, clothe and house ourselves, extra money makes little impact to our happiness. Or does it?

Recent research looking at this phenomenon is starting to suggest that more money can indeed buy happiness, but we’re just not very good at doing so.

[Researchers] are beginning to offer an intriguing explanation for the poor wealth-to-happiness exchange rate: The problem isn’t money, it’s us. For deep-seated psychological reasons, when it comes to spending money, we tend to value goods over experiences, ourselves over others, things over people. When it comes to happiness, none of these decisions are right: The spending that make us happy, it turns out, is often spending where the money vanishes and leaves something ineffable in its place.

As Jonah Lehrer puts it, “Instead of buying things, we should buy memories”. But why? Lehrer continues:

Why don’t things make us happy? The answer, I think, has to do with a fundamental feature of neurons: habituation. When sensory cells are exposed to the same stimulus over and over again, they quickly get bored and stop firing.

This memories-over-objects theory seems to tie-in quite nicely with these previous findings.

When Money Buys Happiness (or Not)

After discussing consumer signalling and Geoffrey Miller’s Spent in his Findings column (mentioned previously), readers of John Tierney’s Lab were asked,

List the ten most expensive things (products, services or experiences) that you have ever paid for (including houses, cars, university degrees, marriage ceremonies, divorce settlements and taxes). Then, list the ten items that you have ever bought that gave you the most happiness. Count how many items appear on both lists.

Dismissing for a moment the self-selection of the participants and the small sample size, the responses to the question are quite intriguing, showing you what consumer items are worth their cost in terms of ‘happiness’, and what items aren’t.

  • Expensive items that don’t significantly contribute to happiness: marriage ceremonies, most cars, boats.
  • Inexpensive items that do significantly contribute to happiness: meals with friends, alcohol, books, music, quality beds, pets, bicycles.
  • Items that are both (expensive and contributory to overall happiness): education, housing, foreign travel, electronics and sports cars.

Dr Miller’s analysis of the experiment’s trends is worth reading, as is this previous post on the link between money and happiness.

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.