The Value of AI in Insurance

DeepSeek has been making waves lately — its impact rivals ChatGPT’s debut at the end of 2023.

Today I chatted with my friend @Yuzi, who works at an insurance brokerage, about AI and insurance. Here are some highlights from our conversation, along with a few thoughts of my own — mostly for my own record.

I’ve recently been trying out Keyou Health’s “personalized health checkup” service (¥299 per session). I started wondering if I could build something similar myself, because the intake questionnaire is entirely structured: do you currently have any of the following conditions? Does your family? If yes, when was it diagnosed, what treatment did you receive, how did it go? You also need to upload lab reports, medical records, and so on. I’m curious to see whether something interesting emerges from the handoff to a real human advisor.

Keyou also offers a health report interpretation service. Once my checkup is done, I might give it a try — though honestly, I’m skeptical. The conclusions are right there in the report, and you could just ask an AI. So what justifies the ¥500/session price tag? I genuinely don’t know. Staying curious.

This naturally led us to insurance. I’d started learning about it last year. As I understand it, insurance is fundamentally a contract — and the terms of insurance products are fixed; they can’t be customized. That part is highly structured and standardized. The complexity lives on the customer side: most people buying insurance don’t know what they actually need, what a specific policy covers or excludes, or what to watch out for. And most people (I’d guess) aren’t fully aware of their own health status — they breeze through the underwriting process without a second thought. That gap between policy complexity and buyer awareness is exactly why insurance agents and brokers exist.

A good agent or broker starts by understanding the customer: why do you want insurance? What’s your budget? What’s your household’s financial situation? From there, they match products and build a suitable plan. They also need to understand the customer’s health history — reviewing insurance records, medical records, lab reports, physical exams, medication history — and use all of that to navigate the underwriting process. Underwriting is probably the hardest part, especially for customers with pre-existing conditions (so-called “non-standard” policyholders), where it needs to be done case by case and can be exhausting.

I also learned that insurance products are heavily commoditized — the differences between competing policies are often minor. This is where AI shines, I pointed out. It can sift through a product library and surface the relevant differences for each customer. Whether it’s gathering customer information, organizing health records, or matching someone to the right plan — AI can do all of this accurately and efficiently. Things that used to require an agent’s time and expertise are now within reach of a well-designed product.

First takeaway: the efficiency gains on the sales side will be dramatic. Today, putting together a customized plan for one customer might take 1–2 hours. With AI, you could do it for dozens — or hundreds — of customers simultaneously. And if you watch how DeepSeek reasons through a problem, you’ll notice it outpaces human analysis and catches errors faster. Greater per-agent capacity means more customers served, which means higher conversion rates overall.

So what happens to the salespeople displaced by AI? They move to the back end: service and relationship management. Right now, brokerages mostly hire more salespeople to handle acquisition and conversion. Once an AI system can do that work, the front-end value of a human salesperson declines — and the backend, where human judgment and relationship-building actually matter, becomes more important.

At its core, buying insurance is a trust transaction, not a tactics game. Whatever sales scripts, product knowledge, and industry expertise are currently being trained into salespeople will be absorbed by AI. Trust cannot be absorbed. What does trust look like? It’s eating hot pot together, playing basketball together, sitting in a sauna together. What does a tactic look like? “You’re one step away from claiming ¥300 cash,” “You’ve outperformed 99.3% of users.”

Because insurance is a long-term, recurring product, customers also need to trust that their agent will still be around when they need to file a claim. I’ve noticed that agents with genuine personal relationships have a real edge. A friend’s agent — 19 years in the industry — plays basketball and has meals with my friend regularly. My friend doesn’t even need insurance right now, but he’d recommend this agent to anyone. Because there’s real rapport there, because this person is the first who comes to mind. Online-only brokers face the opposite problem: if you’re not present in someone’s life, they won’t think of you when they need help — or recommend you to their friends.

My friend made a sharp point: if you’re not going to invest in service, you might as well sell simple, transactional products — travel insurance, accident insurance. I’d argue that’s bad for everyone involved: the policyholder gets no real support, the insurance company gets no loyalty and low ticket sizes, and the brokerage makes thin margins.

Second takeaway: in an AI-enabled market, choosing insurance is really choosing service — service that’s tangible, visible, and trusted. Low-level conversion is tricks; Pinduoduo has mastered that. High-level conversion is trust: when you buy an iPhone, you go to the Apple Store or JD.com, because you trust the brand. I’m not saying every agent needs to send gifts on every holiday — just that a deeper, more genuine connection is what ultimately matters.

More broadly: a business that only sells standardized products is hard to sustain. What keeps you alive is something others don’t have — something the customer genuinely needs. That’s your moat. That’s your core advantage.

How should the insurance industry build that kind of service? How can AI be used to genuinely help policyholders, rather than just cut costs? I don’t have the answer. Happy to discuss if you do.

P.S. DeepSeek is everywhere right now — free, open-source, accessible without a VPN, and genuinely capable. I’ve been watching people in my circle monetize it left and right, and I’ll admit I’m a little envious. I keep asking myself: which of these applications are actually solving real problems, and which ones are just riding the hype? Staying tuned. Continuing to learn.