management of those claims. The goal of any good workers’ compensation program is to control the components
that you can to the best outcome for the employer and the
employee, keeping costs down.
Litigation Management Applications
A growing challenge for claim management is litigation.
Claim managers struggle to strike the right balance between
efficiency and effectiveness of the litigation management
process. It is not beneficial if reduction in legal expenses
leads to larger indemnity payouts. Predictive modeling can
help litigation managers allocate the right case to the right
law firm at the right price. Applying modeling to litigation
management can help reduce the total legal cost while maintaining or even improving the quality of litigation results.
If defense counsel is consulted or obtained early where there is a
prediction of significant severity, counsel can direct the investigation and begin the discovery process. In addition, the process
of formulating a defense strategy can begin at the earliest stages
of the claim so as to mitigate the loss and to ensure corporate
goals and philosophies in dealing with injured workers are properly integrated into the process. This allows the legal team to be
used for strategy and not just facilitation through a legal process.
Litigation of a claim requires numerous decisions to be made
throughout the litigation process. These decisions are often made
based upon gut instinct, anecdotal evidence or simple prejudice
of thought. Predictive analytics in litigation can allow a greater
chance of success than the anecdotal approach currently utilized.
For instance, in selecting a medical expert one should consider the evidentiary weight that the judge assigned has historically given to the selected expert’s opinion. Other variables
to consider are the injured worker’s attorney’s success rate at
trial with the particular judge assigned, opposing counsels
propensity to settle versus try cases, and if settlement is historically preferred, at what stage of the litigation process does
it occur. Today’s technology can offer you the opportunity to
create a more “experienced team” through predictive modeling. Now with the proper modeling you can put that experience at the front of line. Leaving that very limited time that is
available to the people working the claims, from adjusters to
attorneys, to design strategies to mitigate costs.
Future of Predictive Analytics
The claims and litigation management industries have been
building and improving predictive modeling significantly
over the last decade. Insurance companies and third party
administrators have built vast workflows to capture data
for both management and underwriting. While there is an
opportunity to develop models to help benchmark, predict
and better manage litigation. One of the key issues is antici-
pated to be the availability, collection and sharing of data.
The legal community has long captured data in a much different way than the adjusting community. Attorneys are
working through very elaborate legal systems and complex
processes. They communicate this information in great detail
often through emails and letters. Due to this fact, the level of
technology for gathering and trending data has not been a
concern for law firms. Their concern is the workflow of the
legal process, not data mining. The time is coming for the
legal community to understand and partner with administrators, insurers, claims and litigation managers. It is also time
for the insurance community to seek out forward-thinking
law firms that do not have to be convinced of the value of analytics for increased efficiency and improved outcomes.
As insurers and employers are embracing predictive analytics, so should the legal community. The mining of relevant
information from a statistically significant data base is crucial to increasing the likelihood that litigation decisions can
truly be in the best interests of the employer.
Predictive modeling provides the needed information at the
beginning of the process to do better. Forming teams and partnerships using the right information to create customized models can create a competitive advantage in this rapidly changing
world of technology and data. Having the right information
for decision-making at the right time can address today’s problems. Capturing that information over time, modifying and
then building on it will solve tomorrow’s problems.
Cheng-sheng Peter Wu, FCAS, ASA, MAAA, is the Director
of Advanced Analytics & Modeling in the Actuarial and
Insurance Consulting Group for Deloitte Consulting LLP.
David Duden is the Director of Risk Management in the
Actuarial and Insurance Consulting Group for Deloitte
Consulting LLP. Misty Price is the Director of Analytics and
the Innovation and Analytics Leader for the Law Offices of
Adelson, Testan, Brundo and Jimenez. Steven C. Testan, Esq.,
is the Founder of the Law Offices of Adelson, Testan, Brundo
& Jimenez. Karen Stankevitz is the Director of Consulting at
Adelson, Testan, Brundo & Jimenez.
As used in this document, “Deloitte” means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see www.deloitte.com/us/about for a detailed description of the legal structure of
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