In Part One, we introduced you to the Monte Carlo analysis, a technique by which an auto collision is evaluated multiple times using a mathematical model. After outlining the basics, we explored an example involving path intrusion from a side street. In the final section of this two-part series, we apply the method to determine liability in a scenario involving a left turn with approaching traffic.
Let’s suppose an insurer’s claims organization is investigating the following incident:
Failure To Yield?
The policyholder enters the southbound left turn lane and is stopped waiting until there is a safe gap in northbound traffic which would allow him to turn in front of opposing traffic and into the parking lot. He’s been stopped about 10 seconds and observes the claimant approaching in the right lane. The insured feels that there is a safe gap and proceeds from a stop, turning across traffic. The claimant states that he was approaching in the outside lane between 40 and 45 miles per hour and never saw the insured vehicle before striking it in the right quarter panel.
The claimant thought the speed limit was 45 miles per hour; however, the police report stated that it was 35 miles per hour. The insured was cited for failure to yield the right of way while turning left. The claimant adjuster demanded 100 percent of his damages. Your claims adjuster offered 80 percent, alleging inattention on the part of the claimant driver.
The Monte Carlo analysis shows that 36 percent of drivers traveling at or around 35 miles per hour would have avoided this collision. Because the claimant was speeding, the probability of a collision increased by 6 percent. It also shows that the claimant’s response time was slower than 99 percent of all drivers. Had the claimant been traveling at the speed limit, only 40 percent of the population would have attempted a left turn. Because he was traveling 10 mph faster than the normal flow of traffic, however, 62 percent would have attempted a left turn.
Because of the claimant’s unlawful speed, the not only did the likelihood of a collision increase, but also the probability that a vehicle would attempt to turn. The simulation shows that a significant portion of the population could have successfully coped with this hazard. The claims adjuster now has tools that not only support a 20-percent offset, but perhaps also something larger.
While this isn’t a substitute for an adjuster’s judgment, it does represent a way to give that individual an objective and scientific means to examine a driver’s actions. In order to make an argument about a driver’s negligence, the insurer must know what, if anything, the policyholder did wrong.
Until now, you were stuck with opinions and uncertainty. The truth is that Isaac Newton is often your best witness. He is impartial, objective, and consistent. He also forces the argument over liability into a forum that fairly compares the driver’s performance to everyone else.
Uncertainty is the largest obstacle facing the adjuster when it comes to liability. How fast was the car really traveling? Did the car accelerate normally or rapidly from a stop? Did that car even stop? It’s easy to just raise the white flag of surrender under the assumption that there will never be enough information to figure this out. Monte Carlo takes care of that. Does it matter if we know exactly how fast the car was going? No, because that variable is modeled with a range of values. We have the advantage of taking into account almost every possible solution, mathematically. A more complete and scientific understanding of the collision is available without collecting any more information than you would find in a police report, satellite photo, statement or vehicle damage photo. If you have this minimal level of information, you have enough to get an answer.
Traditionally reconstructions were reserved for high exposure losses like fatalities. The hourly costs of a technical analysis made it cost prohibitive for anything less. But, times have changed. The technology has improved to a point where these standard models can be adapted to an individual case and provide a meaningful analysis very economically. Costs to replace or fix vehicles are not declining. This, in combination with the increasing probability of disputes, makes non-injury, physical damage claims a significant source of potential savings. An insurer wouldn’t think twice about flat-rating a damage estimate in a subro demand from a competing carrier. Now that an economical tool is available, why not give liability disputes the same attention? Take a look at how much money and time you’re losing on unsuccessful negotiations, or arbitrations. A new approach may reveal opportunities you never knew existed