As defense and indemnity expenses continue to rise, proven strategies to help insurers improve claim outcomes and control costs are needed. This article explores why insurers’ legal spend is escalating and discusses how insurers can leverage AI-powered analytics to identify law firms that deliver the greatest value—and potentially reduce their legal spend by as much as 15%.

The civil litigation process for insurers

Insurance litigation typically involves disputes between insurers’ policyholders and third parties. The adjudication process for litigated claims is complex with several phases, which typically move at a slow and costly pace.

A civil lawsuit begins with the pleadings when the plaintiff files a complaint with the court, which describes the plaintiff’s injuries or damages and alleges how they were caused by the defendant. The defendant must file an answer to the complaint. Typically, there are several conferences during the process to give parties the opportunity to consider settlement options. If the parties are unable to reach a settlement, the parties move into the formal discovery phase where depositions are taken and the parties exchange information about the evidence and the witnesses they will present during the trial. If a settlement is still not reached, the case moves into trial preparation and ultimately a trial. Once the case is decided by the jury or judge, a judgement is entered. There is an opportunity for one or both parties to appeal the decision. The appellate process itself involves potentially multiple levels of review. If the appeal is not successful, the judgement is enforced.

Litigation process delays are increasing costs for insurers

Because the legal burden of proving a claim is on the plaintiff, the plaintiff’s deposition is one of the key litigation events in the civil litigation process. During the deposition, the insurer’s attorney questions the plaintiff under oath to gather information about the claim and establish the factual basis for it.

Working with AI-powered analytics, we looked at plaintiff depositions and determined that, beginning in 2021 there have been significant delays, as shown in Figure 2. Referencing a large dataset of litigated insurance claims throughout the United States, we used Milliman’s Datalytics-Defense to determine the average number of days between the date when the plaintiff filed the lawsuit and the date of the plaintiff's deposition. In 2018, 2019, and 2020, this number was very consistent at about 450 days, and by 2023, the time lag jumped by about 40% to nearly 650 days, mainly due to the lingering impact of pandemic-related shutdowns in the court systems.

Figure 3 depicts how much the number of days from plaintiff lawsuit to deposition has been impacted by state. In some states, the delay is now more than one year longer than the 450-day benchmark.

Depositions are the costliest part of litigation, and the plaintiff deposition delays depicted in Figures 2 and 3 directly correlate to the increase in cost of defense litigation for insurers. Whether the discovery process takes six months longer or a year longer than the 450-day benchmark, the insurer’s attorneys will continue to monitor and work on the case—and continue to bill their clients for their time. In short, the longer the litigation process, typically the more costly the litigation becomes.

Trial activity is back and defense costs are escalating

Figure 4 shows the results of our analysis of trial activity, using AI-powered technology, of 28,000 open claims from 2019 through 2023, depicting the number of hours that defense attorneys either traveled to or attended trials:

• Beginning in March 2020, there was a dramatic reduction in trial activity due to pandemic-driven court shutdowns, from 4,000 hours per month in 2019 to less than 1,000 hours per month in 2020.
• Across the claims analyzed, there were 3,000 less hours of trial activity per month in 2020 and 1,000 hours less per month in 2021.
• During 2022 and into the first few months of 2023, the industry has compensated for about 1,000 hours per month of the lost trial activity as court activity has increased beyond its pre-pandemic levels.

With trial activity occurring at levels greater than pre-pandemic levels insurers should be on alert for a potential increase in overall costs. Specifically, increased trial activity may increase the average cost of claims as the larger claims typically require an upcoming trial date to motivate a settlement. For pending large claims that go to trial over the next 6 to 12 months, insurers might face verdicts that significantly increase their total costs.

Best practices to maximize the allocation of legal spend

Insurers can leverage AI-powered analytics to detect patterns that help them build better defense strategies and make more informed, data-driven decisions. This includes benchmarking and evaluating their attorneys to find outliers who are not cost-effective. The objective is to maximize the allocation of an insurers’ legal spend to the most effective attorneys. By implementing this best practice, some insurers have saved as much as 15% of their legal spend, all the while getting better claim outcomes.

The results of our trial cost analysis are shown in Figure 5. Green dots indicate that the insurance company won the case and grey dots indicate that the plaintiff won the case. The analysis shows that:

• The average trial lasted five days with in-trial costs of $43,000.
• Some trials lasted more than 30 days.
• The average in-trial cost per day, depicted by the red line on the first chart, is approximately $8,800.
• Trials shown above the red line were more expensive per day than average.
• The green dot trials below the red line were less expensive per day than average, and the insurer won the case.

Given that not all attorneys are as effective as their peers, insurers can use this information to determine which ones are more likely to win their cases. And, given that spending more on a particular attorney does not necessarily deliver a better result, insurers can also use AI-powered analytics to determine which attorneys offer the best outcome at the most cost-effective prices, and eliminate the outliers who are not as effective and/or are more expensive than their peers.

It is important to note that these analyses are not intended to minimize insurers’ defense costs by finding the least expensive attorney. Rather, the intent is to minimize the total cost to insurers—the amount spent on defense and indemnity costs. This concept is consistent with the 2023 CLM Litigation Management Study, which surveyed more than 90 claims and litigation management executives. More than 80% of respondents said that they believed that “Higher compensation to attorneys does not translate to better attorneys or better outcomes.”¹

Conclusion

As legal spend continues to climb every year, the management of defense and indemnity costs is becoming a priority for many insurers. Some are turning to legal claims analytics solutions to increase visibility into their legal expenses and empower them to make smarter, data-driven decisions. When evaluating these solutions, consider the following:

• Does the technology use data mining to interpret line data?
  For visibility into your legal spend, avoid technology that uses the UTBMS codes (Uniform Task Based Management System) since those codes are inconsistent and inaccurate for delivering analytics. Instead, it’s now possible to use AI and machine learning to create usable data from unstructured data. For example, through data mining, you can differentiate the types of depositions taken, identify the number of depositions and motions taken by each law firm and lawyer, and help identify best practices by determining the highest-cost activities. Other examples include travel spending by firm, the success rate of motions filed by each lawyer, and the number of trial days by case. You gain valuable insights into cost drivers to help you make more informed decisions when allocating legal resources.
• Can the technology predict expected results?
  AI-powered analytics can be used to develop risk-adjusted predictive scores for performance that empower you to compare your actual spend for each claim with the predicted defense and indemnity costs. This enables you to determine each firm’s performance, so you can make data-driven decisions to move cases away from underperforming firms and assign them to top-performing firms. Some insurers have realized millions of dollars in savings by employing this approach.
• Does the software use machine learning to improve over time?
  Through machine learning, the data algorithms will continuously improve over time as more legal invoices are processed, to deliver even better predictability and reporting, which will further improve your team’s decision-making.

Leveraging AI-powered analytics and machine learning, can help insurers understand, manage, and allocate legal spend. As trial activity resumes and the litigation process elongates, working with risk experts – paired with the right technology – can help minimize costs while maximizing efficiency.

¹ CLM. 2023 CLM Management Study Report of Findings. Retrieved 6/1/23.