China's "Leading Life Insurance" Breakthrough, Embracing "AI Agent"

As the global financial industry accelerates its embrace of artificial intelligence, a new trend is emerging.

AI is no longer just a backend tool but is now directly involved in customer service, decision support, and even business execution in the form of “Agents.”

Banks are experimenting, brokerages are testing, and now it’s the insurance companies’ turn.

For a long time, buying a life insurance policy meant that when you needed to check your policy or inquire about a surrender, you often had to wait for manual responses, making the process slow and time-consuming.

Especially for questions like “How much have I paid in premiums?” or “How much can I get back if I surrender now?” which seem simple but involve personal data and rule-based calculations, customer service agents are hesitant to answer freely, while customers are eager to know.

This gap between “long-term commitment” and “instant service” has always been a challenge for the life insurance industry.

Research by Zishi Tang found that China’s largest life insurance company—China Life—has been trying out a new type of AI (called AI Agent) through its overseas platform to handle these high-frequency, sensitive questions.

China Life Takes Initiative

Zishi Tang learned that China Life (overseas) recently partnered with NetEase Zhiyi’s cloud commerce division to deploy AI Agent applications in after-sales service.

According to official disclosures, the system achieves over 90% accuracy in answering questions about “policy maturity and surrender.”

For example, when a customer asks “How much have I paid in premiums?” or “How much can I get back if I surrender now?” previously, staff had to log into systems and manually retrieve information, which was lengthy. Now, the AI Agent can automatically fetch data, perform calculations, and generate compliant responses, significantly reducing response times.

What Are the Pain Points for Life Insurance Giants?

For customers purchasing life insurance, “after-sales service” involves all interactions with the insurance company after buying the policy, such as checking the policy, asking about payments, making changes, or understanding “How much will I get back at policy maturity?” or “How much will I lose if I surrender now?”

These questions seem simple but often require manual system checks, leading to long wait times during peak periods.

Because each policy’s payment records and attached benefits are different, even a small oversight during manual inquiry can cause inconsistent responses, leaving customers confused.

For insurance companies, these inquiries hide complex “calculations.”

Examples include: Has the policy been paid for 5, 10, or 20 years? Were there any missed payments? Are dividends or universal accounts attached? How is the cash value calculated upon surrender?

These cannot be guessed or estimated; they require logging into multiple systems, retrieving historical data, and calculating step-by-step according to actuarial rules.

In the past, all manual, time-consuming, and prone to errors—mistakes in figures could lead to complaints or disputes.

What Is an AI Agent?

An AI Agent, in Chinese, means an artificial intelligence entity. It differs from traditional chatbots.

Ordinary AI can only respond passively to questions, but AI Agents can proactively understand goals, plan steps, call tools, and complete tasks.

For example, when a customer says “I want to understand surrender,” the Agent will automatically recognize the intent, retrieve policy information, calculate cash value, and explain the process—all without human intervention.

This is especially challenging in financial scenarios.

Because services like insurance and banking involve large amounts of personal sensitive data and strict compliance requirements, AI cannot operate freely. Each step must follow preset rules. For example, it cannot casually disclose someone else’s policy information or make decisions on behalf of the customer; it can only provide fact-based calculations and guidance.

Therefore, AI Agents in finance are more like “professionally trained digital employees”: they know what they can do, what must be reported, and what requires human judgment.

Currently, in practice, when sensitive operations or complex judgments are involved, systems still transfer to human customer service to ensure safety and a good experience.

Not the First Attempt

In fact, this is not China Life (overseas)’s first disclosure of AI transformation efforts.

Zishi Tang learned that as early as February this year, this insurance giant launched two AI applications in Hong Kong and Macau:

One is a 24/7 AI assistant for customers, available on the OneService app, handling common inquiries about policy status, value, maturity documents, and renewal methods.

The other is an AI knowledge retrieval tool for financial advisors, helping them quickly access product and policy information.

Both tools are based on the DeepSeek V3 large model, emphasizing natural language understanding and complying with regional regulatory requirements in Hong Kong and Macau.

The system design principle is: simple questions are responded to instantly by AI, while complex questions are transferred to human agents during working hours, enabling human-AI collaboration.

How Large Is the “Policy Capacity”?

According to publicly available data, China Life’s overseas operations achieved impressive results in 2025, setting new profit records.

Specifically, last year, total premium income was HKD 49 billion, with new business premiums (converted) of HKD 10.5 billion, a 63% year-on-year increase.

The company also achieved a comprehensive investment return of HKD 21.15 billion, with an annualized return of about 6%, up 0.91 percentage points from the previous year.

This operational scale indicates that as the number of policies increases, customer inquiries naturally rise. Relying on manual responses is slow, costly, and prone to errors.

Naturally, this “life insurance leader” has developed strategies to address this.

From another perspective, its steady operational performance provides space for exploring technological applications.

Disclaimer: The content and data in this article are for reference only and do not constitute investment advice. Please verify before use. Operate at your own risk.