KEY TAKEAWAY:
--We are seeing a transition away from a reliance on underwriters to data science and algorithmic rating-driven approaches. For many insurers assessing less complex and more commonplace risks, the underwriter now has less discretion to change the price and less leverage to adjust the policy. In many cases, insurers have implemented “no touch” pricing and underwriting, eliminating underwriter involvement completely.
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When fewer cars were on the road during the pandemic, U.S. commercial auto insurers enjoyed a respite from years of struggling with profitability. However, it turned out to be only a one-time shot in the arm for underwriters who maintained premium levels while enjoying a transitory reduction in loss exposure. As soon as driving patterns normalized post-pandemic, auto insurers began losing money again. After almost breaking even in 2021, the sector recorded underwriting losses of $3.3 billion in 2022.
Commercial insurance is lagging the personal lines insurance market in its digital transformation, which could help combat headwinds of “social” and economic inflation while dealing with the residual effects of supply chain disruptions that occurred in 2021 and 2022.
The commercial auto insurance segment has posted a combined ratio above 100% in 11 of the last 12 years — and some insurers are exiting the industry altogether. Yet, the market leaders that have pursued pricing automation and upgraded their segmentation capabilities continue to be profitable.
We need to broaden the conversation about modernizing rate plans to ensure the whole segment can move into profitability and benefit from these gains.
In an uncertain global economic environment, adopting next-generation data tools necessary to incorporate disciplined pricing, to achieve rate adequacy and to perform targeted underwriting can help future-proof policies and businesses against runaway loss trends.
See also: Could Auto Accidents Be Reduced by More Than Half?
Becoming Responsive, Not Reactive
The insurance industry, particularly commercial insurance, made up for lost time in 2023 and is rapidly automating. We are seeing a transition away from a reliance on underwriters to data science and algorithmic rating-driven approaches. For many insurers assessing less complex and more commonplace risks, the underwriter now has less discretion to change the price and less leverage to adjust the policy. In many cases, insurers have implemented “no touch” pricing and underwriting, eliminating underwriter involvement completely.
In the long run, using more data science-based versus manual pricing approaches will improve rating accuracy, make businesses more efficient and increase objectivity.
Traditionally, insurance actuaries and product line owners make an educated guess of where they think inflation and loss trends are headed, build those assumptions into their rating and tell regulators how much premium they need. However, the limitations of this approach were exposed when insurance was disproportionately affected by the spike in inflation in the last few years.
Inflation rose as high as 20% on a year-over-year basis for replacement equipment and parts, while the realized cost of replacing a totaled vehicle exceeded what insurers’ rate plans had built into their policies. This coincided with a supply chain choke point where new vehicles and replacement parts were not available due to a shortage of semiconductors, equipment and other parts. With obtaining approvals for rate increases from regulatory authorities taking 12 to 18 months, auto insurers couldn’t react fast enough — and so they accumulated losses faster, with commercial lines hit especially hard.
The Digitization of Insurance
Historically, the task of evaluating most risks fell to commercial insurance underwriters because of constraints in the availability of scalable data for use at rating, as well as limitations in legacy systems’ abilities to process complex data. But now, automating underwriting through robotic process automation (RPA), artificial intelligence and machine learning is helping insurers expand the breadth of available data, gain new insights from existing data and increase their level of rating sophistication.
To determine the pricing of a commercial auto policy, a data-driven approach assesses and weighs various exposures, allowing for a more granular evaluation of risk. This approach includes the assessment of drivers’ and vehicles’ records and behavior, as well as predictive factors such as proprietors’ and drivers’ financial management. Automated processes also play a crucial role in gathering all relevant data about businesses’ risk profiles.
Synthesizing these damage-coverage data points helps fuel the ability of insurers to automate reading and better select risk. However, when it comes to liability coverage, additional factors come into play, contributing to serious challenges in the industry. Yet, once again, a data-led approach can prove invaluable in mitigating the risks associated with social inflation.
Mitigating Social Inflation
Social inflation can be defined as the increase in liability costs as a result of paid and pending legal settlements above and beyond what can be expected due to normal inflation. U.S. commercial auto insurance liability claim payouts blew out by an estimated $30 billion between 2012 and 2021 due in part to social inflation. The Insurance Information Institute found that two of the biggest factors behind the dramatic rise were legal system abuse and third-party litigation where financiers such as hedge funds support injured parties to sue for much larger payouts.
Attributed in part to America’s litigious culture, this development marks a big departure from when the insurance company would offer the injured party a figure and they would generally accept. Further complicating risk, the commercial driver labor market has grown since the pandemic, and younger drivers have been increasingly responsible for a rise in moving violations and accident rates. That is why a full evaluation of drivers’ contribution to risk becomes critical to more accurately rate and underwrite policies. Modernizing the rate plan leverages data to better assess liability exposure.
See also: Telematics Updates Are Transforming Auto
Join the Data-Led Transformation
The headwinds facing the industry are admittedly highly problematic, but the market leaders are making money year in and year out because they have invested in the appropriate products and solutions. By contrast, many companies find themselves struggling to post combined ratios below 100% as a result of adverse selection from competitors. Yet, the automation tools to level the playing field by modernizing underwriting and pricing capabilities are available now. Small and medium-sized commercial auto insurers can future-proof their business by embracing this opportunity for digital transformation.
