AI Rewrites How Trucking Fleets Buy Insurance
Commercial motor carriers face mounting operational pressures driven by escalating liability costs. Traditional fleet risk assessment relies heavily on manual audits, leaving critical gaps in network visibility. When systems fail to process historical data efficiently, fleets often overpay for coverage.
To mitigate these expenses, operators are turning to automated data models that evaluate risk in real time.
The pressure Corgi is targeting is well documented. The median nuclear verdict in trucking cases climbed to $51 million in 2024, up from $21 million in 2020, and verdicts above $1 million have risen 235 percent since 2012, according to industry litigation data. Commercial auto premiums rose 9.4 percent in 2025, and combined net profit at the ten largest carriers fell from $4.2 billion in 2021 to $2.2 billion in 2025 as insurance costs outpaced revenue, Insurance Business reported.
Identifying market gaps
Rather than relying on static annual reviews, emerging software platforms use machine learning to continuously monitor fleet data. This shift changes how underwriters evaluate commercial transport networks.
Erika Lee, a spokesperson for Corgi, a commercial insurance provider focused on the trucking industry, explained how continuous data ingestion allows the company to target underserved market segments.
"We approach segmentation differently than the market traditionally has. Rather than devoting our focus to a single segment, we look for small pockets of opportunity across many. Our goal is to be in the market at every level, from preferred to lightly distressed and from small to large. One area we find especially appealing is the gap between the fleet and non-fleet segments, since nearly every other carrier struggles to be successful there," Lee said in written responses to The Supply Chainer.
Constant underwriting and speed
Data bottlenecks that inflate fleet insurance also cripple underwriting speed. When evaluating risk, manual planning teams lose critical hours attempting to audit accounts.
Lee noted that failure to automate decisions leaves underwriters at a disadvantage.
"AI strengthens our underwriting in three ways: Constant underwriting: Most carriers lack the time to audit their books regularly. Our technology continuously monitors changes across our book, so we can act on them quickly," Lee noted.
