Companies, and claims organizations in particular, are continuously searching for ways to rein in the ever-increasing costs of litigation. Most of these efforts, however, miss one of the most effective ways to do so — resolve disputes faster. Imagine how much could be saved by resolving claims prior to all-out discovery, expert studies and motion practice. So why isn’t this being done?
One of the major reasons why cases drag on, and costs pile up, is because claims professionals and counsel find it too difficult to value cases in their early stages. Not until massive document production has been fought over and completed, witnesses prepped and deposed, expert reports generated and rebutted, and numerous motions drafted and ruled on, does the company or its counsel feel comfortable enough to assess its chances of success at trial and the likely verdict range should plaintiff win. And after all that work…then...almost all cases settle.
It’s obvious that enormous sums could be saved if settlements could be brought forward in time. But to do so, claims professionals and attorneys need to be able to value claims early on, when uncertainty is everywhere: What surprises might surface in discovery? How will the judge rule on important pretrial motions? Which witnesses will be most credible? What will the jury conclude on each of the underlying liability issues? How much might they award for each element of allowable damages? In the face of all this, very few claims professionals or attorneys are comfortable deciding on the appropriate settlement value. If they were somehow to come up with a number, they would find it difficult to defend it persuasively to others — whether that be their management/client, a mediator or opposing counsel at the settlement table.
Disciplined Approach
A disciplined and thoughtful approach to addressing these uncertainties is of paramount importance to any insurance carrier or self-insured. While individuals may hold differing views on any particular decision or uncertainty, the process by which decisions are made across an organization should be applied as consistently and objectively as possible. Consistency of process in making claim decisions across a claim portfolio will inevitably lead to greater predictability of outcomes and improved loss costs overall.
The principal tool across all disciplines for making good decisions in the face of uncertainty is decision tree analysis. It has been employed for decades by corporations to make major business decisions, government agencies to make major public policy decisions, and even medical doctors to make better life-and-death decisions. With increasing frequency it finds a place in the legal arena as well, in both claims and corporate litigation departments.
Two basic principles of decision tree analysis help explain its universal appeal. The first principle is that better decisions will be made if a complex problem is broken down into its major uncertainties, each is evaluated, and these issue-by-issue evaluations are intelligently recombined.
In litigation, these major uncertainties equate to the key issues that the judge or jury will specifically consider. A decision tree provides the tool for capturing the break down of the issues, and keeping track of how the claims professional or counsel wants the issues recombined to produce an end value. In a personal injury case, for example, in which plaintiff is suing for millions in economic and non-economic damages, but might have been comparatively at fault, the decision tree might look like Figure 1.
Other steps in the process involve capturing the existing and potential evidence, testimony and other factors the trier will weigh on each issue, and then formulating an opinion about the relative likelihood of good versus bad results on each. These opinions should be expressed numerically because there is no sound way to combine a “fighting chance” and an “outside shot.” Centuries-old probability arithmetic provides the means to arrive at the overall chance of a defense verdict when there is a 25 percent chance of the jury finding “not negligent” and a 20 percent chance of the jury finding “not the cause” even if it does find negligence. This same arithmetic also produces the “probability-weighted average value” — i.e., the value obtained by weighting each of the possible total payments by its estimated probability of occurring — which is the dollar value above which a risk-neutral plaintiff, and below which a risk-neutral defendant, should be willing to settle. (See Figure 2.) Technically, this average value is referred to as the “expected value” of the range, even though it does not mean the single result that is most expected.
Of course, even though decision tree analysis provides the means to value cases early on, companies will still be nervous making settlement decisions too far ahead of the trial date. But delay usually means incurring major costs. The answer to this dilemma lies in a second principle of decision tree analysis: it is possible for a claims professional/counsel to be very uncertain about one (or more) of his/her assessments without necessarily being uncertain about the best course of action.
Using the earlier case (in Figures 1 and 2) to illustrate, assume counsel has reason to believe the plaintiff will settle for $750,000. But with none of the expert work yet done on causation, counsel is concerned about his/her .80/.20 “early” assessment of this issue, wondering how different it might be at the close of discovery and the exchange of expert reports. Is the possibility of better odds enough for the company to make the decision to turn down a $750,000 settlement and keep fighting? Remember, at an .80/.20 chance of the jury finding cause/not-the-cause, the Expected Value is $922,500. Assume counsel could imagine the causation probability — after the case is fully-developed — turning out to be as low as .67/.33. Solving the tree with those odds leads to a revised Expected Value of $770,000 (rounded). So, a $750,000 settlement would still be a good deal. Thus, even though counsel might be uncertain whether the odds of the jury finding causation would end up closer to 2-to-1 (.67/.33) rather than the initially-assessed 4-to-1 (.80/.20), the company could still be very comfortable settling at $750,000.
In fact, even at a probability of .60/.40 — which would lead to an Expected Value of $690,000 (rounded) — a $750,000 settlement may still be a good one if, by settling early, the company would be able to save more than $60,000 in continuing pretrial and trial costs and fees.
These examples serve to illustrate that even a high degree of uncertainty about an assessment might have little impact on the wisdom of a particular settlement. Yet most claims professionals or attorneys facing such uncertainty would shy away from attempting to value their case, and thus potentially miss out on the opportunity to dispose of it early before incurring substantial costs.
Building a Tree
The time and expertise required to build out a good decision tree — one for which the right legal issues and factual uncertainties are identified and properly sequenced, with the right consequence specified for each potential scenario — has discouraged insurance companies and their attorneys from applying the technique to the routine types of claims they encountered daily. In addition, the software programs designed to support building trees and performing the necessary “expected value” arithmetic remained outside the comfort zone of the typical claims professional and attorney.
Now decision tree template technology can streamline the decision tree analysis process to bring the benefits of this discipline to more of the claims faced daily by insurers. Claims professionals and counsel would use a library of pre-defined decision tree templates for routine types of claims, thus removing the historical difficulties of having to start from scratch each time trying to construct a sound tree. Personal injury, property damage, product liability, professional liability, employment practices liability and construction defect are good examples of claims that lend themselves to the use of templates, but any type of claim a company sees on a regular basis is a candidate for capture in a template.
When it is time to evaluate a new case, the claims professional or attorney simply selects the appropriatetemplate from the library for the type of claim at hand. The template then drives the user through an assessment of each critical liability and damage issue in the case. As each issue is presented, the user is first asked to think through and record the helpful and harmful evidence and other factors (e.g., witness credibility) that will influence the results on the issue. The user then inputs their probability assessment of winning/losing that issue or being hit with higher/lower damages. Once all issues in the tree have been evaluated by the user, the technology takes care of the math, enabling accurate, instantaneous calculation of the expected case value, and allowing claims professionals and their attorneys to quickly explore the impact of alternate probability assessments or alternate pretrial or trial strategies.
Decision tree template technology can integrate with case and litigation management systems, which brings the tool into a claims professional’s or counsel’s daily workflow. In addition, the ability to capture completed decision tree templates in a case or litigation management system that also houses claim data may create opportunities for data mining and predictive analytics. For example, when a company gets a new claim, it can query against prior claims with a similar profile to get insights about how these cases can be won or lost, how variations in the evidence might influence expected values, and how much outside counsel spend might run at successive stages from complaint through trial.
Companies looking to improve litigation loss costs should consider applying decision tree analysis as an early case assessment — and thus early case disposition — tool. And decision tree templates offer a smart technology to enable implementation of this approach on a broad scale.