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Litigation drives “social inflation,” increased insurance awards, and settlements in casualty losses. Juries that are more plaintiff-friendly than in the past, expansion of coverages by judges, and litigation funded by investors have led to higher losses paid by insurers. “Nuclear verdicts,” those more than $1 million, are common and can stretch into the tens of millions of dollars today. Between 2012 and 2019, the average award rose by 50 percent to $1.7 million.
Insurance carriers need more tools to combat social inflation. I believe one answer to this problem lies in carriers pooling their data to create insights about the early warning signals that precede litigation and, once a claim becomes attorney represented, those that help minimize costs and settlements.
My company has built artificial intelligence (AI) tools that leverage cross-carrier pooled data for many years. Our clients who have contributed their anonymized data see the benefits every day. Building effective AI depends heavily on sample size. And our pooled data allows our models to be built on a broad cross section of carrier data. Small carriers benefit from the added number of data points; large carriers benefit from that and the addition of exposures for which they may not have much data. On average, the benefits are shown in lower litigation rates and smaller settlements and awards.
An Industry Solution Is Needed
We are helping combat social inflation for our clients, but what can be done to continue the fight across the industry? I advocate for establishing a clearinghouse of litigation-related data contributed by insurance carriers and self-insureds. Contributed data would be stripped of identifiers of plaintiffs and defendants (the contributing party). Still, it would include key data elements about claims either decided in court or litigated and settled out of court. The entire contributory database would be available for analysis by contributing parties and their designees.
What types of data would be in the database? It may include details of each claim that describe the circumstances from which it arose (cause of injury, extent of injury, etc.); a longitudinal accounting of attorney involvement (names of plaintiff attorney(s) and firm, dates and details of demand letters, other critical correspondence, and more); ultimate determination of liability and settlement/award amounts and the timing of events leading up to claim closure. We have found these data elements to be crucial to predicting the future course of a claim. Correct “time stamping” of when each data element is added to a claim is also critical so that predictions can be made as soon as new information becomes available.
Data sharing is not pervasive in the insurance industry as past data provides critical information used to set rates, segment customers, and settle claims. Companies, primarily huge ones, are hesitant to give up what they perceive to be a competitive edge. Antitrust issues are a potential concern too. However, in the case of the fight against social inflation, there is a compelling reason for companies to participate and regulators to have a favorable point of view: It’s in the insurance consumers’ interest.
A 2022 study by Jim Lynch and Dave Moore of the Casualty Actuarial Society, “Social Inflation and Loss Development,” estimated that social inflation increased commercial auto liability claims by more than $20 billion between 2010 and 2019. It also showed what they calculated as the unexpected paid losses (i.e., those not built into rating plans) each year. In their words:
We call the amount that actual losses exceed expectations “unexpected” because they were not anticipated by standard loss development techniques. This is evidence that social inflation in the 2010s caused paid losses to be more than $4 billion higher than might have been predicted with standard loss development techniques.
Insurance consumers pay these costs as carriers create their rates to achieve a target return on equity using loss and loss development data. A cross-carrier contributory database would enable all contributing companies to help reduce rates by the amount that predictive intelligence built from the data would save them in litigation and settlement costs. And based on my experience, I believe a substantial portion of the $20 billion cost of social inflation could be saved as a result.
A Way Forward
The clearinghouse idea I mentioned earlier does not exist and is not on the horizon. So, what can carriers and self-insureds do to move forward with a shared-data model today? One thing they can do is employ the services of third-party vendors who build contributory databases from their customers’ data. Firms such as mine have had significant success building tools that fight back at social inflation by aggregating data from a wide range of carriers and self-insureds, anonymizing and standardizing it so that all participants benefit from the shared data. The more companies that participate, the more that the industry benefits.
Social inflation is one of the most vexing issues facing the insurance industry today. Plaintiff attorneys promote what they believe is their fight for “justice” for injured parties. Unfortunately, the costs attorneys extract from the system are carried on the backs of all consumers through higher rates. It’s unfair for one special-interest group to benefit at the expense of all of us. On behalf of the consumer, insurance companies need to fight back, and working with a vendor that leverages a shared-data model is one way they can.