Navigating AI Safety: Opportunities and Challenges for Life Insurance Technology
American Entrepreneur Jim Rohn once wrote: “Your life does not get better by chance, it gets better by change.”
AI has changed not only the conversation around how we use data, but also the landscape of every industry that leverages it. AI is everywhere, and life insurance isn’t immune to the both the hype and the hope. But here’s the thing: while everyone’s rushing to implement the latest AI tools, we should all take a moment to breathe, reflect, and think about what this actually means for an industry built on decades-long promises and deeply personal data.
If you’re a Brokerage General Agency, Carrier, or Insurance Marketing Organization, you’ve probably had countless vendors pitch you on how AI will revolutionize your business. Some of that excitement is justified. AI really can streamline operations and improve customer experiences. But it also introduces risks that could bite you years down the road—and in life insurance, “years down the road” is literally the business model.
Why Life Insurance and AI Make Sense (Mostly)
AI has the power to completely transform our industry. The applications are genuinely compelling when you think about it:
Underwriting that doesn’t take forever. Remember when getting a life insurance quote meant waiting weeks while someone manually reviewed medical records? AI can crunch through health data, fitness tracker metrics, and lifestyle information in minutes. Your customers get faster answers, and your underwriters can focus on the complex cases that truly actually need human judgment.
Nationwide, for example, recently partnered with DigitalOwl to help process vast amounts of medical data to help create a more holistic view of medical history for a client. This type of implementation shows that we are past the precipice and beginning to see legitimate AI adoption at the highest levels.
Customer service that actually helps. We’ve all dealt with terrible chatbots, but the good ones are getting really good. An AI assistant that can handle routine policy questions, process beneficiary changes, or help agents find the right product for a client’s situation? That’s not science fiction anymore—it’s Tuesday.
Claims processing without the headaches. Nobody wants to deal with paperwork when they’re grieving. AI can verify death certificates, cross-reference policy details, and flag potential issues without making families wait months for payouts. When it works well, it’s genuinely compassionate technology.
Products that make sense for real life. Here’s where it gets interesting. AI can spot trends in customer behavior and life events that humans might miss. Maybe people are having kids later, or buying homes at different ages than they used to. AI can help design products that actually fit how people live now, not how they lived in 1995.
But Here’s Where Things Get Tricky
Every conversation about AI benefits needs a reality check, especially in life insurance. We’re not selling subscription software or social media ads—we’re making promises that might not get tested for decades. That changes everything.
When algorithms get it wrong, people get hurt. If your AI model has biases baked into the training data (and they often do), you might end up systematically underpricing policies for some groups or overcharging others. That’s not just bad business; —it’s potentially discriminatory. And unlike other industries where you can quickly fix a mistake, insurance decisions stick around for a long time.
Data breaches hit different in our world. A retail company loses customer emails, that’s bad. An insurance company loses health records and financial information? That’s catastrophic. We’re talking about HIPAA violations, state regulatory issues, and the kind of customer trust that takes decades to rebuild.
Regulators are paying attention. The NAIC isn’t asleep at the wheel here. They’re developing guidelines specifically for AI in insurance, and state regulators are asking tough questions. If your AI system can’t explain why it denied someone’s application, you might find yourself in hot water during your next examination.
The accountability puzzle is real. When your AI makes a mistake—and it will—who’s responsible? Is it the vendor who built the model? The data scientist who trained it? The company that deployed it? In life insurance, where decisions affect families for generations, “I don’t know, the algorithm did it” isn’t going to cut it.
How to Do This Right
While we can never completely remove risk from any new implementation, we can at least strive to mitigate potential pitfalls from the get-go.
Start small and be strategic about it. Don’t try to AI-ify your entire operation overnight. Pick one area where the risk is manageable and the benefit is clear. Maybe that’s automating routine customer inquiries or helping agents with product recommendations. Get that right first.
Treat data governance like your business depends on it. B—because it does. If you’re going to feed sensitive information to AI systems, you better have bulletproof policies around access, encryption, and retention. This isn’t just about compliance; it’s about not becoming the next cautionary tale in the trade press.
Keep humans in the loop for anything that matters. AI can help your underwriters and agents work faster, but it shouldn’t replace their judgment on complex cases. It can help process claims, but a human should review anything unusual. The goal is augmentation, not replacement.
Test for bias regularly and fix it when you find it. Your AI models will drift over time, and what seemed fair six months ago might not be fair today. Build in regular audits and be prepared to retrain models when needed or to implement stricter system prompts to prevent gettingdrifting into trouble.
Be ready to explain your decisions. If an AI system denies someone’s application, you need to be able to tell them why in terms they can understand. “The neural network said no” isn’t an explanation, —it’s an excuse. I anticipate more compliance standards will be developed for cases such as this. Even in agent portal bots, we should be cognizant of AI drift and consistently test the AI’s ability to accurately explain answers.
The Bottom Line
AI in life insurance isn’t going away, nor should it. The technology really can make things better for everyone involved. But we’re not selling widgets here; —we’re making promises that stretch across lifetimes. That means we need to be more careful, more thoughtful, and more responsible than industries where the stakes are lower.
The companies that get this balance right—leveraging AI’s power while respecting the industry’s unique responsibilities—will have a real competitive advantage. The ones that don’t? Well, they’ll probably provide some valuable lessons for the rest of us.
The choice is ours. Let’s make it count.
