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With social inflation driving automobile liability judgments to new heights, businesses are increasingly looking for ways to avoid litigation altogether.
Indeed, U.S. commercial auto insurers saw their liability losses nearly triple from 2010 to 2019, a period during which the median jury verdict’s monetary award almost doubled.
Thus, avoiding litigation means thoroughly understanding past claims to inform how to approach current ones.
But there’s a catch. Over the years, the commercial auto insurance industry has struggled with inconsistencies in the way claims are documented and a general lack of data to use in documentation.
Today, innovative technology is providing an answer. Leveraging artificial intelligence (AI) and natural language processing can extract valuable information from even a minimal, unstructured data set that can provide insights into a claim, potentially avoiding the risk of litigation.
Looking Back to Reduce Costs Going Forward
The value AI delivers in analyzing commercial auto claims essentially lies in providing a clear view of past claims. AI models are trained to look at claims in a way similar to an actuary, examining historical risk factors, characteristics of claims that have led to litigation, and any common themes that might emerge.
The rules AI develops in analyzing those past claims then get applied to new losses, with natural language processing picking up details from claims documents and weighing them based on the company’s claims history.
AI can then bring together all the identified and weighted attributes to determine the risk of litigation. Armed with an understanding of that risk, the business can move quickly to disrupt the possibility of litigation, perhaps by offering favorable terms to achieve an early settlement that avoids an open case that lingers for years in costly litigation. A recent analysis of claims in Texas, for example, found that when an attorney is involved in a commercial auto claim, the average time to resolve a claim grows to 516 days from 165 days without an attorney. The analysis found that in 2019, the average total loss on a commercial auto claim in Texas was 17.1 times higher with attorney involvement than without, while the average cost for adjudicating a claim was 52.8 times higher.
Capturing Greater Detail in Initial Claims Reports
In today’s nuclear verdict environment, following best practices means relying on unstructured data more than ever and relying on claims-management teams to embrace this new process. For AI and natural language processing to generate the information needed to reduce the risk of litigation, those claims administrators need to document claims in greater detail than they have historically.
What’s needed to help assess litigation risks are claims notes that provide more information about the driver or passengers in the affected vehicle — if a passenger was pregnant, for example — that might prove compelling to a jury if the case were to go to trial. Running such claim details through natural language processing can allow AI to identify factors that might lead to heightened litigation risk in a commercial auto claim.
Other Benefits of AI and Natural Language Processing
AI can also be used to analyze data collected by telematics, such as onboard cameras and speed sensors, that are increasingly employed by commercial fleet operators to improve risk management by recognizing good drivers and identifying those engaged in risky behaviors.
Following a catastrophic loss, reviewing with legal counsel the insights gleaned from telematics by AI can help ensure that the investigation is efficient and detailed and that the organization has done all it can to protect itself from costly litigation.
In addition, AI can help identify trends that could prove useful in loss-mitigation or loss-control efforts, therefore reducing the frequency of commercial auto claims themselves. AI insights can also provide feedback to claims administrators about their own decision making, possibly helping them to avoid future litigation.
And, in cases in which litigation can’t be avoided, AI analysis can help the company understand the performance of specific attorneys and law firms in past cases, enabling the business to choose attorneys who might be best suited to represent them in a particular case.
Preparing the Industry for Innovation
The application of AI in commercial auto claims is in its infancy but has huge potential. To take full advantage, the industry must prepare for the technology.
Third-party administrators (TPAs) and insurers should be encouraged to document cases quickly and thoroughly as AI is rolled out in the claims process, and uptake of these applications should be swift. As fleet operators wrestle with the increase of commercial auto premiums by 200 percent or more for multiple years running, they’ll likely embrace the benefits of AI and natural language processing.
Of course, there will be challenges, and claims administrators will have to determine how much capacity they want to dedicate to more detailed documentation. As they understand and learn to work with AI, however, they can make informed decisions about the changes necessary to support the use of the new technology. Furthermore, the models used in the analysis will be refined over time, making them even more effective in analyzing commercial auto claims litigation risks.
As the models become smarter, the underlying data will become more valuable. And as more value is drawn from the data, businesses’ ability to reduce commercial auto litigation risk will increase — ideally leading to a decrease in organizations’ cost of risk.
 “Triple-I: Rising Litigation Expenses Are Driving Up Cost of Insurance,” Insurance Information Institute
 “New Report Highlights Harmful Impact of Commercial Automobile Litigation on Businesses and Consumers,” American Property Casualty Insurance Association