As AI spreads in insurance, a new battlefield emerges

As more and more insurers leverage AI for underwriting, claims, and risk management, they face a new kind of pressure: the need for explainability, defensibility, and accountability.

In the world of insurance, explainability isn’t about opening the black box completely.

It’s about being able to clearly articulate what data influenced a decision, how heavily it was weighted, and whether that aligns with underwriting intent and regulatory expectations.

In practice, it means you can sit across from a regulator, broker, or insured and walk through why a risk was priced or declined in plain English without data science jargon.

“Today, the bar for explainability is moving from ‘we trust the model’ to ‘we can defend the outcome.’ That’s a fundamental shift in accountability,” said Jeff Lang, senior vice president, California at Trucordia.

Why AI accuracy isn’t enough

An accurate AI model isn’t automatically a usable one.

“If you can’t justify its decisions, especially in a regulated industry where every outcome may need to be defended, it’s no good,” Lang explained.

While accuracy optimizes performance, explainability protects the business. In insurance, you need both. Otherwise, you’re taking on invisible risk.

The moment a claim is denied or a premium spikes, accuracy becomes irrelevant if you can’t explain the ‘why’ in a way that holds up legally and reputationally.

That said, it’s essential to be able to defend AI-driven decisions in regulatory or legal settings.

“A defensible decision is one where you can demonstrate consistency, absence of prohibited bias, and alignment with underwriting rules that were approved, not invented by the model,” Lang said.

Although regulators don’t expect perfection, they do expect governance. If you can document how a decision was made, monitored, and corrected, you’ll be in a much stronger position.

Explainability is a competitive advantage

Explainability will absolutely become a differentiator because trust is now becoming as important as price. Clients and brokers will gravitate toward carriers who can justify their decisions.

In a commoditized market, the ability to explain ‘why’ will separate transactional carriers from advisory partners.

“The winners won’t just have better models.  They’ll have models they can stand behind in front of regulators, brokers, and clients,” explained Lang.

The leading insurers will be the ones who pair advanced analytics with strong governance. Those who embed explainability, auditability, and compliance into their AI models from day one will win.

It won’t be about who has AI.  It will be about who operationalizes it responsibly at scale.

“Carriers that align underwriting expertise with data science, rather than replacing it, will outperform those that treat AI as a black-box shortcut,” added Lang.

How current insurance advisors can prepare

The ultimate goal is for advisors to become translators.

If you’re an insurance professional, it’s your job to bridge the gap between what the model is doing and what the client needs to understand.

The brokers who will succeed will be the ones who can challenge and interpret carrier decisions, not just relay them.

There’s no better time to start implementing this shift than now.

“Don’t wait to build fluency in how AI-driven underwriting works, because clients are going to start asking tougher questions, and ‘that’s just the model’ won’t be an acceptable answer,” explained Lang.

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Anna Baluch is a finance reporter and writer with more than a decade of experience. Contact her at [email protected]