With recent annual returns surpassing 20%, it’s not difficult to see why investors are making third-party litigation funding (TPLF) one of the fastest-growing alternative asset classes. Litigation can be expensive, but a stake in a winnable lawsuit can be a valuable asset. So, investors, looking to diversify their financial portfolios, front the costs. However, as economists often remind us, there is no free lunch. Evidence indicates that for the end results of this capital infusion into the litigation industry, insurers and policyholders ultimately bear the brunt of the tab.

This paper discusses the combined impact of social and economic inflation because the study’s actuarial methodology cannot differentiate between underlying drivers. Social inflation is defined as “excessive inflation in claims” for the purposes of this paper and is a term primarily used by insurance industry professionals. It is closely related to “legal system abuse,” which the Insurance Information Institute (Triple-I) defines as policyholder or plaintiff attorney practices that increase the costs and time required to settle insurance claims to the detriment of consumers.

The phenomenon of social inflation has garnered a great deal of attention in the property and casualty (P&C) insurance industry. The term defies strict definition, though it is widely acknowledged to involve excessive growth in insurance settlements. We examine evidence for its existence in standard industrywide claims triangles through 2019. The focus is on commercial automobile liability insurance, though other annual statement lines of business are examined as well. We find development patterns in commercial auto liability are consistent with most descriptions of social inflation.

The study, Getting Granular to Find Lower-Risk Properties Amid Wildfire Perils, comes at a critical time as California continues to grapple with regulatory obstacles related to actuarially sound underwriting and pricing. Using Guidewire HazardHub predictive Wildfire Score methodology, Triple-I’s research demonstrated how granular geospatial data analysis can identify lower-risk properties, even in areas traditionally considered high-risk.