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Is big data right for the underbanked?
WASHINGTON and BOSTON (3/7/14)--A National Consumer Law Center (NCLC) study released Thursday warns that while big data promises it can make better products available to the unbanked and underbanked through predictive systems, the information based on those algorithms can be riddled with inaccuracies.
Big data brokers claim to be able to help some of the approximately 64 million consumers in the U.S. who have no credit history or lack sufficient credit history to generate a credit score--a state that in the modern world can cut off access to traditional banking services--by providing informed predictions of their histories.
The NCLC said it got 15 of its employees to request information about themselves from some of the largest data brokers in the country. The report's executive summary said that errors in the information ranged "from the mundane--a wrong e-mail address or incorrect phone number--to seriously flawed."
It went on to say that seven of 15 reports of a data broker that touts an ability to estimate income and education based on its advanced models contained errors in estimated income, nearly doubling the salary of one participant and cutting that of another in half, and 11 of the 15 reports incorrectly stated the volunteer's education level.
"Big data makes big promises. It promises to make better predictive algorithms that in turn can make better products available to the unbanked and underbanked. But can big data live up to this big promise?" the NCLC summary asks.
It suggests that when analyzing this use of big data, consumers and policy makers should be concerned with these questions:
  • Are the decisions based upon accurate data?
  • Can the algorithms, when fed with good data, actually predict the creditworthiness of low-income consumers?
  • Does the use of big data in reports used for credit, employment, insurance, and other purposes comply with consumer protection laws?
  • Is there the potential for a discriminatory impact on racial, geographic, or other minority groups?
  • Does the use of big data actually improve the choices for consumers?


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