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Dynamic Auto Insurance Pricing and UBI Actuarial Model

1) Charging insurance companies model licensing fees and a commission on premiums per policy; 2) Providing white-label p

MODEL

Key Fields

FIELD STAMPS

IndustryFintech

RegionChina

ScaleMid-size

ChannelOnline

📌 Background

In 2026, InsurTech entered an AI-native reconstruction phase, with the combined ratio of auto insurance remaining under pressure and traditional uniform pricing struggling to match individual risk profiles. According to the iResearch '2025 Insurance Industry AI Application Panorama Insight Report,' technology investment in China's insurance industry is expected to exceed 67 billion RMB in 2025, with the AI sector becoming the fastest-growing segment at a CAGR of 22.5%. In the first half of 2026, ZhongAn Online saw a 105.7% year-on-year surge in new energy vehicle (NEV) insurance premiums (based on consulting firm data).

👤 Target Customers

Small and medium-sized property and casualty insurance companies and insurance brokerage platforms, which procure actuarial models and pricing engines.

💰 Revenue Streams

1) Charging insurance companies model licensing fees and a commission on premiums per policy; 2) Providing white-label pricing SaaS to brokerage platforms with fees based on API call volume; 3) Usage-based add-ons: Pricing API calls exceeding the package limit and dedicated capacity are billed by tier, with excess calls and instance scaling settled separately.

🧮 Cost Structure

Data acquisition hardware costs, computing power for model training, and expenses for actuarial teams and compliance auditing personnel.

🛡️ Moat

First-mover data barrier formed by accumulated multi-dimensional driving behavior data correlated with claims; actuarial compliance licenses and model explainability capabilities.

🔑 Keys to Success

Establish stable data pipelines with mainstream vehicle telematics manufacturers
Validate model fairness and explainability within regulatory sandboxes
Create a closed-loop data feedback system from pricing and underwriting to claims

⚠️ Risks

Regulatory requirements to gradually disclose pricing factors may weaken model advantages
Supply disruption if data sources are exclusively locked by car manufacturers or platforms
Price wars leading to budget cuts among small and medium-sized insurers

🏢 Cases

China Pacific Insurance (CPIC) launched an AI dynamic pricing pilot for new energy vehicles
ZhongAn Online's auto insurance division is exploring linking driving behavior scores to premiums

📊 SWOT Analysis

Strengths

Dynamic pricing accurately identifies low-risk drivers and reduces loss ratios
AI actuarial engine supports minute-level batch pricing and strategy backtesting
Integration with IoT data sources from vehicles provides rich data dimensions

Weaknesses

High initial data cold-start costs and lack of closed-loop claims verification
Increasingly stringent regulatory scrutiny on the transparency and fairness of auto insurance pricing
Lack of sensor data support for some older vehicle models

Opportunities

Rapid growth in NEV ownership introduces new risk factors
Small and medium-sized insurers urgently seek differentiated pricing to compete for high-quality customers
Regulators encourage technology-driven improvements in underwriting precision

Threats

Large insurers building in-house actuarial platforms squeeze third-party market share
Data privacy and personal information protection regulations may tighten data collection scopes
Extreme weather and 'black swan' accident events may cause short-term model distortion

Sources