Cheche Group, the Chinese auto insurance technology platform, has launched ABAO Agent, an AI-powered underwriting and renewal agent now deploying at scale across auto insurance renewal scenarios. The system automates customer outreach, needs identification, policy follow-up, and policy conversion, giving insurers a way to shift repetitive workflows to machines.
The company built the agent on years of proprietary insurance data and scenario-based algorithms. Rather than a narrow chatbot, ABAO Agent functions as what Cheche calls the core engine of its broader intelligent transformation strategy, connecting multiple points in the policy lifecycle.
What the agent actually does
ABAO Agent handles four sequential tasks that typically require human agents: reaching out to customers, identifying what coverage they need, following up on pending policies, and converting those touchpoints into completed policies. Cheche said insurers can automate these workflows end-to-end, not just assist human staff.
The system targets renewal scenarios first - a high-volume, repeatable use case where automation can cut costs quickly. Auto insurance renewals involve predictable data points and customer interaction patterns, making them a practical starting point for production deployment rather than a pilot.
Leadership's bet on structural cost reduction
"ABAO Agent is the core engine of Cheche's intelligent transformation," said Lei Zhang, Founder, CEO and Chairman of Cheche Group. "We have spent years building proprietary insurance data, scenario-based algorithms, and deep industry relationships - ABAO Agent is where that foundation becomes a competitive moat."
Zhang said the company will continue integrating AI across the full insurance lifecycle, "from underwriting and pricing to claims, with the goal of driving structural cost reductions for our carrier partners and cementing Cheche's leadership in intelligent risk management for NEV insurance." NEV refers to new energy vehicles, a growing segment in China's auto market that carries distinct underwriting challenges.
The rise of AI for Insurance has pushed carriers to examine where automated decision-making can replace manual processes without increasing risk. Cheche's approach ties the technology directly to carrier economics - lower costs per policy, not just faster workflows.
The automation angle
What separates an agent like ABAO from older rules-based systems is its ability to handle the full sequence: outreach, identification, follow-up, and conversion. Most insurance automation tools address one or two of those steps. Systems that connect them all change the unit economics of renewal books, especially in high-volume lines like auto.
For insurers evaluating where AI Agents & Automation fit into their operations, renewal cycles offer a contained proving ground. The customer data already exists, the coverage parameters are established, and the primary task is execution - getting the right offer to the right person at the right time and closing.
Why this matters for insurance professionals
Cheche's launch signals that AI agents are moving past the experimental phase in insurance and into scaled production, starting with the highest-volume use case: renewals. For underwriters, product managers, and operations leads, the immediate question is whether their own renewal workflows can be similarly automated - and what that does to staffing models built around manual outreach. The vendors that build proprietary data moats around specific insurance verticals, as Cheche has done with auto and NEV in China, will be harder for generalist AI platforms to displace.
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