Using Data Analytics to Predict an Individual Lawyer’s Legal Malpractice Risk Profile (Becoming an LPL 'Precog')

6 U. PA. J. L. & PUB. AFF. 267 (2020)

41 Pages Posted: 27 Jan 2021

See all articles by Nancy B. Rapoport

Nancy B. Rapoport

University of Nevada, Las Vegas, William S. Boyd School of Law

Joseph R. Tiano, Jr.

Legal Decoder Inc.

Date Written: 2020

Abstract

The power of data analytics is revolutionizing the way that business is conducted in nearly every industry. The medical industry, the consumer/retail space, and the banking and financial industries are taking their business operations to the next level by leveraging the power of big data. Despite radical transformations in nearly every other aspect of the legal industry, though, the approach to preventing, predicting, assessing, and resolving malpractice claims hasn't really changed. Malpractice insurers and their law firm clients continue to take an old-fashioned approach when it comes to legal professional liability. Unlike the insurers pricing automobile policies, the vast opportunity that LPL insurers could use hasn't been used well--at least not yet. LPL industry experts have confirmed that most legal malpractice insurers aren't leveraging advancements in technology and legal analytics in order to predict risk areas. Instead, LPL carriers primarily are reacting to actual events or using the broad brush of simple demographics to set rates. Consequently, the "all-in" malpractice costs for insurers and law firms continue to escalate, even though risk and costs should both be decreasing. This is the wrong result for everyone directly or peripherally involved in the legal industry and, more specifically, the wrong result for the LPL industry as a whole. Our paper posits that a progressive, data-driven approach to legal professional liability will reduce the overall cost of malpractice claims, thus helping law firms to recognize potential pressure points before those intimations of problems become full-blown blisters. Part I analyzes the underpinnings of malpractice claims. Part II discusses how malpractice insurers and their law firm clients have historically assessed, underwritten, and resolved malpractice claims. Part III explains why historical malpractice metrics fall short. And Part IV proposes a new data-driven analytic schema by which malpractice claims might be predicted, managed, assessed, and resolved.

Keywords: legal malpractice, professional ethics, legal ethics, malpractice insurance, data analytics, BigLaw, big data

JEL Classification: K0, K1, K2, K3, K4, K19, K29, K30, K39, K40, K49

Suggested Citation

Rapoport, Nancy B. and Tiano, Jr., Joseph R., Using Data Analytics to Predict an Individual Lawyer’s Legal Malpractice Risk Profile (Becoming an LPL 'Precog') (2020). 6 U. PA. J. L. & PUB. AFF. 267 (2020), Available at SSRN: https://ssrn.com/abstract=3760626

Nancy B. Rapoport (Contact Author)

University of Nevada, Las Vegas, William S. Boyd School of Law ( email )

4505 S. Maryland Parkway
Box 451003
Las Vegas, NV 89154-1003
United States
713-202-1881 (Phone)
702-895-2482 (Fax)

HOME PAGE: http://www.law.unlv.edu

Joseph R. Tiano, Jr.

Legal Decoder Inc. ( email )

105 North Virginia Avenue
Suite 204
Falls Church, VA 22046

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