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.