A Bigger Commercial Auto Picture

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The December 2025 edition of Best’s Review contained an article entitled, “Stuck in Reverse: Commercial Auto Losses Keep Mounting”, that contended:

The U.S. commercial auto insurance line continues to burden the overall property/casualty industry, accounting for more than $10 billion in net underwriting losses over the past two years … the sector has now generated an underwriting loss for the 14th consecutive year … the difference between auto liability and physical damage results has been stark and diverging … The difference in the net loss ratio … has typically been significant and far higher for liability coverage … due to increasing loss severity and adverse loss development.

The article went on to acknowledge:

Technology has played a big part in … being able to better attack things from an underwriting expense perspective … [Insurers] need to focus more on the loss ratio and refining their risk appetite from the standpoint of understanding their portfolios, and how their portfolio might have changed over time, where the risk exposures are, where the susceptibilities are, and make better decisions from a risk appetite/risk selection standpoint on an account-by-account basis.

That’s fair … to a point. But there’s more to consider.

IBNR Estimating

Technology, AI, and advanced actuarial analyses can address challenges related to IBNR claims and aspects of cash flow underwriting in commercial auto, even as underwriting losses persist due to rising loss severity and adverse loss development outpacing premium increases. Here’s how:

  • Poor IBNR estimates foster adverse development in long-tail lines like commercial auto liability. But advanced actuarial models and machine learning improve IBNR forecasting by moving beyond traditional methods — chain ladder or Bornhuetter-Ferguson — to incorporate individual-level claim data, non-linear patterns, and broader variables; to predict individual claim development, to detect emerging trends earlier, to reduce reserve errors, and to better account for volatility from things like social inflation and litigation delays.
  • AI-driven tools provide more timely views of claims trends, allowing forward-looking reserve adjustments and scenario or stress testing for extreme outcomes like nuclear verdicts.
  • Using AI for claims triage, severity prediction, and development forecasting helps identify high-risk claims early and refine IBNR estimates.

These improvements lead to more accurate overall reserves, reducing the risk of large adverse development surprises that drain underwriting results.

Cash Flow Underwriting

In a high-severity, adverse-development environment like commercial auto, cash flow underwriting can be risky because of delayed but escalating payouts. But:

  • AI and technology in underwriting enable better risk selection and pricing precision that lets insurers target more profitable segments, reducing reliance on pure cash flow by improving loss ratios over time.
  • Using AI to enhance claims management can accelerate and control cash outflows, shortening claim cycles and improving liquidity.
  • More accurate IBNR and reserving with AI yields better capital management and less need for defensive over-reserving.

Will any of that yield miracles? Maybe not. Will AI and advanced analytics sharpening actuarial precision on long-tail uncertainties like IBNR and enable more disciplined underwriting? Yes.

Either way, it’s worth considering in the interest of having a more complete and less bleak picture.