I teach a data mining course here to our MBA students. It is a popular course with about 70% of the students taking it at some point during their two years with us. Since I am an operations research guy, I concentrate on the algorithms, but we spend a lot of time talk on the use of data mining, and possible pitfalls. The New York Times today has a wonderful story illustrating the pitfalls. American Express has been using shopping patterns to reduce customer’s credit limits. This, in itself, is not surprising, but a letter the company sent out implies that it was basing the evaluation on the stores and companies the customer used, rather than a more direct measure of consumer ability to repay a debt.
“Other customers who have used their card at establishments where you recently shopped,” one of those letters said, “have a poor repayment history with American Express.”
Wow! Shop at the Dollar Mart, you are not a careful shopper reacting to an uncertain financial world, but rather a poor credit risk who should be jettisoned before defaulting (I don’t know if Dollar Mart is one of the “bad” establishments: American Express has not released a list of companies that are signs of imminent financial doom). That is, of course, what data mining results come down to, but it is rare for a company to admit it. Not surprisingly, customer’s who received such a letter became a little irate. Check out newcreditrules.com for one person’s story.
American Express says it is no longer using store shopping information, but it will continue to use the results of data mining in its credit decisions.
In one presentation to analysts, it noted that people with multiple residences and multiple mortgages used to be a good bet. Now, the reverse is true.
In a good economy, lots of data mining was used to “help” customers by identifying new products or offers that might appeal to them. Now, it seems that more data mining uses the customer’s data against their own interests. I suspect we will see more stories of this type.