Big Data is New Tool Used in Consumer Lending

By LowCards.com

Two new start-ups have recently opened their doors to help consumers with limited or no credit files obtain a loan. These consumers find it difficult, if not impossible, to borrow from traditional lenders because the credit bureaus do not have enough information to determine their level of risk.

The two companies, Earnest and Affirm, have created computer programs that run a detailed analysis on other factors in a consumer’s life to determine how much of a risk they may be.

Have you had the same cell phone number a long time? That probably indicates you are a good credit risk. Use all capital letters to fill out a form? That indicates you are less likely to repay a loan.

Earnest, Affirm and numerous other startups use computer programs to create a tapestry of a consumer’s online and offline behavior. The data points can include things as varied as social network connections and education history to the amount of money in a consumer’s retirement account. This allows the companies to go beyond what normal credit bureaus analyze and seek out consumers who are good credit risks but might be undiscovered by traditional methods.

Many experts expect this analysis of “Big Data” to be the future of analyzing credit risk. They may eventually replace the use of the standard credit bureaus and FICO scores. While the lenders have mostly restricted their efforts to writing small personal loans, some have recently announced they will enter the student loan and mortgage markets.

LowCards.com simplifies the confusion of shopping for credit cards. It is a free, independent website that helps consumers easily compare credit cards in a variety of categories such as lowest rates, rewards, rebates, balance transfers and lowest introductory rates. It also gives an unbiased ranking and review for each card. Created by Hampton & Associates, the company has been analyzing the credit card industry and supplying objective websites on various consumer expenses for 15 years.

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