A pair of Plymouth State University professors are revolutionizing the practice of law by introducing the use of data analytics. Chantalle Forgues, J.D., MBA, associate professor of business law, and Daniel Lee, Ph.D., professor of economics, can identify variables that predict litigation risk and outcomes, potentially helping firms make better hires and operating decisions, avoid lawsuits, and take other cost-saving measures. Forgues and Lee’s approach, more advanced than the summary statistics available from several commercial legal analytics products, may also have other far-reaching potential, including identifying ways to reduce the excessive use of force by police departments and creating criteria for less arbitrary criminal sentencing decisions.
“Data analytics will eventually transform the legal industry. It will help companies improve their operations by preempting a certain number of lawsuits and making litigation more effective, streamlining the process with fewer motions and more informed negotiations,” Forgues said. “We anticipate many large companies and law firms will turn to analytics in the future, taking a more data-driven approach to practicing law.”
Since publishing initial research in Bloomberg Law and the New Hampshire Bar News, Forgues and Lee presented their research and methodology at The Future of Law symposium earlier this fall, teaching other legal practitioners to take an analytical approach to law. The Future of Law is an international symposium hosted annually by LexTech, an Irish firm specializing in digital solutions for multiple legal practice areas. The 2020 conference was held virtually and brought together more than 400 leading legal authorities from around the world, including Ireland’s Minister of Justice, Helen McEntee. Forgues and Lee were among the few invited presenters.
Their session, “Litigation Data Analytics – The Advent of Predictive Outcomes in Litigation,” discussed the two branches of litigation analytics they identified: legal risk analytics, which uses data to identify risk factors that make a business more or less likely to be sued or to sue another, and litigation management analytics, which helps lawyers gain a litigation advantage. Legal risk analytics is an unchartered area of law of which few lawyers are even aware. Litigation management analytics is also unique; using text analytics to predict litigation outcomes is a novel approach in the field with great promise.
Forgues and Lee’s research illustrates how legal risk analytics could be used at large firms to identify specific factors that put organizations at litigation risk, helping them anticipate and even preempt certain lawsuits. Just as preventative care medicine looks to reduce negative outcomes and costs down the line, legal risk analytics can reduce or eliminate the costs associated with being sued or suing another. Forgues and Lee used text analytics and statistical modeling for employee reviews on Indeed.com, a job hunting and employee review website, identifying words that predicted an increase or decrease in lawsuits against healthcare companies over 10 years. Some of their more intriguing and potentially valuable findings include:
- A healthcare company with an ostensibly bad manager is predicted to have nearly 100 more lawsuits over ten years than a company without a bad manager.
- Those with reviews mentioning “teammates” are predicted to have nearly 600 fewer lawsuits over ten years.
- Companies that offer employee stock options are predicted to have 135 fewer lawsuits over ten years, while 401k retirement contributions do not have any impact on the number of lawsuits.
“The data from our analysis of employee reviews could be used to advise healthcare companies as they make new hires and focus on internal culture and employee benefits,” Lee said. “This is just one of the many ways data can drive strategic business decisions, decreasing the future legal burden for the company.”
At the symposium, Forgues and Lee demonstrated how data analytics can help lawyers gain an advantage in litigation by informing their strategy. Using data, lawyers can calculate the probability of winning, losing, or settling a case. They can also use analytics to determine the amount of damages to request to maximize their gains, and which motions, including specific words to use in motions, are likely to be successful. Predictive analytics can even inform legal teams of the probable duration of specific litigation, enabling appropriate planning and budgeting.
“There are countless ways litigation management analytics could help a team develop a strategy, framework and timeline for a case,” Lee said. “We are just scratching the surface of what we can accomplish with this discipline.”
Forgues and Lee’s current research is focused on criminal justice reform. The first project will identify words and phrases in local police departments’ excessive use of force policies that may correlate to an increase or decrease in excessive use of force; the team may expand this research further, exploring any connections between excessive use of force and officers’/citizens’ demographics, civil rights lawsuits, contaminated drinking water, low-income or public housing, or accusations of voter suppression or fraud. The information could help guide police departments as they revise excessive use of force policies, and local governments as they look to address outside factors identified as leading to increases in excessive use of force.
The second project will identify variables in sentencing data to determine which defendants are least likely to commit another crime, making them prime candidates for a suspended sentence, and which defendants are more likely to commit another crime, indicating a committed sentence is warranted.
“A data-driven approach can help remove bias from the criminal justice system, making it less arbitrary,” Forgues said. “By analyzing suspended sentences, we hope to determine which characteristics and variables make someone less likely to commit a second crime – whether that be a job, a close-knit family, specific education level, or strong connections in the community. Armed with that information, judges can make sound decisions, knowing which defendants are most likely to benefit from a suspended sentence and which defendants merit a committed sentence, making the process fairer.”
Forgues and Lee plan to publish their criminal justice reform research in 2021.