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    Chicago Tribune: Lenders’ data mining goes deep

    The Chicago Tribune reports on all the data collected and used by banks, lenders, and risk-management companies to determine whether to give loans to people:

    That pizza you had delivered the other night could mean the difference between whether you are approved for a mortgage or rejected.

    There’s a big stretch between making a house payment and paying for a pizza. But it’s not what you pay for carryout that matters, at least not in the eyes of lenders. It’s where the food was delivered.

    Ordering takeout proves that you live where you say you do, and that helps lenders uncover the crook who claims to live in the property he is trying to refinance when he really lives hundreds of miles away. […]

    When you order food online, you become part of a vast database that lenders might tap to help them determine whether you are a good risk. Moreover, all sorts of these data reservoirs exist, and none of them is off-limits to lenders who are coming off the worst financial debacle since the Great Depression. […]

    Digital Risk is just one of numerous risk-management companies that are continuously probing for ways to help clients quantify their risk, prevent fraud and otherwise ensure the quality of their loans. And they’re going to extraordinary lengths to do so.

    For example, they might peek into your online-buying habits. After all, the reasoning goes, someone who buys his shirts from a Brooks Brothers catalog may have more disposable income than someone who shops at JCPenney. […]

    Now, too, the most cautious lenders are digging into noncredit proprietary databases such as those maintained by Papa John’s or Victoria’s Secret. And nothing is out of the realm of possibility. The “only boundary,” says [a Digital Risk employee], is whether information can be accessed legally.

    As long as it does not distinguish between race, religion, age and other “protected” classes, anything is fair game.

    One Response to “Chicago Tribune: Lenders’ data mining goes deep”

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