By tracking client matters and comparing them to this data, Seyfarth helps clients understand and manage their risks.
For example, if a client has an EEOC charge, Seyfarth can pull data on other complainants, the particular EEOC investigator, and the type of claim. That data may indicate a higher risk with this investigator and category of claim. Often, clients treat EEOC charge work as "the lowest type of commodity work — it's essentially just filing an answer," she says. But now Seyfarth can warn a client to be extra careful in a particular case. It can tell the client: "If you don't manage that charge right ... it could [turn] into a multi-million-dollar lawsuit."
Extra Credit: Daniel Katz slide deck on quantitative legal prediction, presented at LegalTech New York 2012: slidesha.re/LTN1261.
Tam Harbert is a freelance reporter based in Washington, D.C. Email: tam@tamharbert.com.
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Gerard Britton
Great article expanding the applications of predictive technologies beyond "coding". DOAR posted on this type of application in the securities area just last week http://bit.ly/Ldnlpb.
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