In a recent CLM CCO Discussions webinar, "The $20 Billion Opportunity: Transforming Subrogation With Advanced Analytics," industry veteran Chris Tidball, senior vice president of global sales, SubroIQ, shared insights on transforming insurance subrogation through advanced analytics. Tidball, with over 35 years of experience at companies like Progressive and AIG, discussed what he calls the "$20 billion opportunity," the estimated annual cost of missed subrogation in the insurance industry.
"It's estimated that about 15% of all claims are closed with a missed subrogation opportunity. To the industry, that's about a $20 billion hit annually," Tidball explained. These missed opportunities affect policyholders through unreturned deductibles, higher premiums, and reduced satisfaction, he added.
Reasons Behind Missed Subrogation
Tidball identified several key reasons for missed subrogation, including overwhelming adjuster workloads, competing priorities, and insufficient training. He referenced his son, who works as an adjuster with a large global carrier and receives 12 to 14 files in a day. “There are just not enough hours in a day to do everything you need to do on a file,” said Tidball. “So, you have the competing priorities, you have the disproportion goals that you have to go after.”
He noted that particularly in auto claims, instead of fighting someone over [paying] 20% to 40%, adjusters will simply pay 100% just to get the claim off their desks. “So, [a] kind of path of least resistance approach [is taken], bad habits get created, [and] bad outcomes are the result. And at the end of the day…everybody gets adversely impacted.”
Tidball then presented multiple case studies showing how analytics-driven approaches recovered millions in previously closed claims, including $3 million for a global retailer and $2 million from 100,000 closed New York PIP (Payment Infrastructure Platform) files.
Utilizing Analytics and Technology
While highlighting various analytical approaches—from basic regression analysis to sophisticated AI—Tidball emphasized that technology alone isn't the solution. “Success is built upon three things: your people, your processes, and your technology."
The most basic analytics are regression analysis, which are “essentially variables that have relationships,” explained Tidball. “So, for example, at the simplest level, a rear-end accident has a high probability of subrogation and you can go back and look at a history of claims and it’s going to bear that out…then you stack on the predictive analytics and that looks at relationships to outcomes. So, we know that you’re going to have situations where, let’s say you’ve got a left turn intersection accident. We know there’s potentially subrogation. And if we apply the regression analytics, we’ll know that, for example, in Arizona, you’re going to get better outcomes than in California, both pure comparative states. But for whatever reason, Arizona performs better than California when it comes to subrogation. Then you tack on the predictive analytics and it’s going to…tell you what the likely outcomes are….Then, you layer on machine learning which…in its simplest form, allows the analytics to learn.”
Tidball noted that the machine and AI begin acting like the adjuster, rather than completely taking the adjuster’s role—and it allows the adjuster to perform better based upon all underlying analytics.
“So, to really get a benefit from advanced analytics, there is a methodology…they’re going to have a foundation that’s similar. And it typically starts with you want to improve results. And to do that, you’ve got to find your missed opportunities, [which] allows you to understand the gaps in your process.”
Addressing Misconceptions About AI
“When it comes to analytics and AI, there really is no standard solution,” Tidball explained. “There’s no one-size-fits-all. You have to really look at your line of business [and] understand what your goals are.”
A significant portion of the discussion addressed the cultural resistance to technological change. “The biggest obstacle, I think…is you’re going to have trouble getting adoption a lot of the time…But the reality is…other than death and taxes, change is the only certainty in our lives. But…people in general [are] creatures of habit. We’re resistant to change. And the change really needs to start at the top. It’s not an overnight process by any means…It’s a shift in paradigm from a traditional risk averse industry to one that embraces change.”
Tidball drew parallels to historical innovations by Henry Ford and Steve Jobs, noting that each faced significant opposition before revolutionizing their industries. In the case of Jobs, who saw that ordinary people could benefit from technology, Tidball noted that “if you think back to the…early ‘80s, this was a massive cultural shift…computers were for the government, for IBM, for big corporations. There was really no such thing as a personal computer or laptop or anything like that. People were actually fearful” that computers were going to take everyone’s jobs, much like people fear AI will take over jobs and the world. Despite the pushback, however, Jobs persevered and succeeded and ended up changing the world. “As we’ve learned from him, those who embrace change will become the leaders of tomorrow.”
Tidball dispelled common fears about AI, stating, "The biggest misconception is that AI is going to result in jobs being eliminated. It's possible that it might eliminate some jobs, but it's also going to create a lot of jobs, probably more jobs than [what will] be eliminated." He added that in today’s day and age, an organization cannot be solely run by people—there has to be a technology presence, and the people and technology must work together in line with a company’s goals to get optimal results.
Tidball concluded the webinar with practical advice for companies looking to implement analytics solutions, emphasizing the importance of data-driven decision-making, process mapping, and organizational calibration to transform subrogation outcomes.