21 Conn. Ins. L.J. 339 (2014-2015)
Risk Classification's Big Data (R)Evolution

handle is hein.journals/conilj21 and id is 349 raw text is: 






          RISK CLASSIFICATION'S BIG DATA (R)EVOLUTION

                                                       RICK SWEDLOFFt


        Insurers can no longer ignore the promise that the algorithms
driving big data will offer greater predictive accuracy than traditional
statistical analysis alone. Big data represents a natural evolutionary
advancement of insurers trying to price their products to increase their
profits, mitigate additional moral hazard, and better combat adverse
selection. But these big data promises are not free. Using big data could
lead to inefficient social and private investments, undermine important
risk-spreading goals of insurance, and invade policyholder privacy. These
dangers are present in any change to risk classification. Using algorithms
to classify risk by parsing new and complex data sets raises two additional,
unique problems.
        First, this machine-driven classification may yield unexpected
correlations with risk that unintentionally burden suspect or vulnerable
groups with higher prices. The higher rates may not reinforce negative
stereotypes and cause dignitary harms, because the algorithms obscure
who is being charged more for coverage and for what reason.
Nonetheless, there may be reasons to be concerned about which groups are
burdened by having to pay more for coverage.
        Second, big data raises novel privacy concerns.      Insurers
classifying risk with big data will harvest and use personal information
indirectly, without asking the policyholders for permission. This may cause
certain privacy invasions unanticipated by current regulatory regimes.
Further, the predictive power of big data may allow insurers to determine
personally identifiable information about policyholders without asking
them directly.



    I Associate Professor and Co-Director of the Rutgers Center for Risk and
Responsibility, Rutgers University School of Law, Camden, New Jersey. Thanks
first and foremost to Greg Lastowka who helped nurture the seed of this
idea. Many thanks also to Jay Feinman and Peter Siegelman for helpful
conversations and comments. This Article was prepared for the Big Data and
Insurance Conference at the University of Connecticut School of Law and
supported by the Insurance Law Center. It was also presented to my junior
colleagues at Rutgers University Law School in both Camden and Newark and at
the Predictive Analytics Colloquium at Virginia Tech. It benefited greatly from
conversations in those venues as well.

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