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gechengling

China Insurance Actuarial Pricing Expert

by gechengling · GitHub ↗ · v1.1.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install insurance-actuarial-cn
Description
提供基于第四套生命表(2025)和CASS标准的中国保险产品精算定价、准备金计算及偿付能力资本分析。
README (SKILL.md)

\r \r

China Insurance Actuarial Pricing Expert (4th Life Table 2025) / 中国精算定价师(第四套生命表2025)|\r

\r

English: AI-powered China insurance actuarial pricing expert — the definitive skill for Chinese actuaries and product pricing teams. Built on the 4th Life Table (2025, effective 2026-01-01) and C-ROSS Phase II framework. Covers pure premium calculation, reserve calculation, solvency capital assessment, and insurance product pricing. Delivers production-ready Python pricing code.\r \r 中文: 中国精算定价师——基于第四套生命表(2025年发布,2026年1月1日实施)和偿二代二期工程(C-ROSS Rules II)的垂直精算Skill。覆盖纯保费计算、准备金计算、偿二代资本占用与定价协同、团险定价专项。内置完整精算定价Python代码。适用:精算师、产品定价岗、保险产品开发、保险精算实习生、精算咨询。\r \r ---\r \r

Trigger Keywords / 触发关键词\r

\r English Triggers: actuarial, pricing, insurance pricing, life table, mortality table, critical illness rate, experience rate, reserve calculation, solvency capital, C-ROSS, China actuarial, product design, insurance product, 4th life table\r \r 中文触发词(优先): 精算、精算定价、费率厘定、生命表、发生率表、死亡率表、重疾发生率、经验发生率、准备金计算、偿付能力资本、保险产品定价、CASS、中国精算师协会、第四套生命表、重疾恶化因子、产品设计\r \r ---\r \r

Core Capabilities / 核心能力|\r

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1. 4th Life Table (2025) Full Analysis / 第四套生命表(2025)全解析|\r

\r 发布背景:\r

  • 2025年10月29日,中国精算师协会正式发布\r
  • 金融监管总局同步发布实施通知\r
  • 自2026年1月1日起实施(现已适用期)\r
  • 第四套生命表替代第三套(2010年版本),反映中国人均寿命延长、老龄化趋势|\r \r

三张核心表|\r

\r | 表名 | 英文 | 用途 |\r |------|------|------|\r | 非养老类业务一表 | CL1 | 定期寿险、终身寿险等产品定价 |\r | 非养老类业务二表 | CL2 | 两全险、年金险等产品定价 |\r | 养老类业务表 | CL3 | 专属养老保险、养老年金等产品定价 |\r \r

与第三套生命表主要变化|\r

\r | 维度 | 第三套(2010版) | 第四套(2025版) | 影响 |\r |------|---------------|----------------|------|\r | 数据期间 | 2005-2008年 | 2018-2023年 | 更反映当前寿命水平 |\r | 男性预期寿命(60岁) | +20.7岁 | +22.5岁 | 年金险赔付期延长 |\r | 女性预期寿命(60岁) | +24.2岁 | +26.1岁 | 女性养老险成本上升 |\r | 表三(养老类) | 无专项 | 新增专项表 | 养老险定价更精准 |\r \r

2. China Actuarial Pricing Methodology / 中国精算定价方法论|\r

\r

保险产品定价基本公式|\r

\r

毛保费 = 纯保费 × (1 + 附加费用率)\r
纯保费 = 趸缴纯保费 / 年金现值系数\r
      = 保险金额 × 平均发生率 × 平均保险期间 × 折现系数\r
```\r
\r
#### 寿险产品定价要素|\r
\r
| 要素 | 说明 | 典型取值 |\r
|------|------|---------|\r
| **预定死亡率** | 来源:第四套生命表 | CL1/CL2/CL3 |\r
| **预定利率** | 保险公司投资收益率假设 | 2.5%-3.5%(2024年后限高3%) |\r
| **预定费用率** | 销售渠道/管理/理赔费用 | 10%-35%(渠道差异大) |\r
| **预定利润率** | 公司利润要求 | 5%-15% |\r
| **保费缴纳方式** | 趸交/期交/年交 | 期交更常见 |\r
\r
#### 重疾险定价方法(Python示例)|\r
\r
```python\r
# 重疾险纯保费计算框架\r
def critical_illness_premium(age, sum_assured, policy_term, payment_term):\r
    # 第四套生命表:非养老类业务二表(CL2)为基础\r
    i = 0.025  # 预定利率(3.0%上限后的保守假设)\r
    v = 1 / (1 + i)\r
    \r
    # 重疾给付现值\r
    A_crit = 0\r
    for t in range(policy_term):\r
        q_crit_t = lookup_critical_illness_rate(age + t, t)\r
        A_crit += v**(t+1) * q_crit_t * sum_assured\r
    \r
    # 死亡给付现值(身故/全残)\r
    A_death = 0\r
    for t in range(policy_term):\r
        q_death_t = lookup_mortality_rate_CL2(age + t, t)\r
        A_death += v**(t+1) * q_death_t * sum_assured\r
    \r
    # 生存年金现值(缴费期)\r
    äx_n = sum(v**t * survival_rate(age, t) for t in range(payment_term))\r
    \r
    # 纯保费 = (重疾给付现值 + 死亡给付现值) / 生存年金现值\r
    pure_premium = (A_crit + A_death) / äx_n\r
    return pure_premium\r
```\r
\r
### 3. Reserve Calculation / 准备金计算|\r
\r
| 类型 | 定义 | 计算基础 |\r
|------|------|---------|\r
| **未到期责任准备金(UEPR)** | 为未来赔付准备的钱 | 剩余保障期内的纯保费现值 |\r
| **已发生赔款准备金(IBNR)** | 已发生未报告赔款 | 精算估计(流量三角形等方法) |\r
| **保费准备金** | 保费充足性测试后补充 | 现金流测试 |\r
| **寿险责任准备金** | 人寿保险长期负债 | 精算评估法(平滑法) |\r
\r
### 4. Group Insurance Pricing / 团险定价专项|\r
\r
#### 团险定价要素|\r
\r
| 要素 | 说明 | 评估方法 |\r
|------|------|---------|\r
| **团体规模** | 参保人数 | 人数越多,波动越小 |\r
| **行业风险** | 企业所属行业 | 行业风险等级系数 |\r
| **年龄结构** | 员工平均年龄/分布 | 员工平均年龄越大,保费越高 |\r
| **历史赔付** | 过去1-3年赔付记录 | 经验费率调整 |\r
| **福利包设计** | 保障范围/保额/免赔 | 方案设计影响 |\r
\r
#### 团险经验费率计算|\r
\r
```python\r
def group_experience_rate(base_premium, experience_factor):\r
    if experience_factor \x3C 0.7:\r
        rate_adjustment = 0.85  # 优良经验,减费15%\r
    elif experience_factor \x3C 0.9:\r
        rate_adjustment = 0.95  # 较好经验,减费5%\r
    elif experience_factor \x3C 1.1:\r
        rate_adjustment = 1.00  # 标准经验\r
    elif experience_factor \x3C 1.3:\r
        rate_adjustment = 1.15  # 较差经验,加费15%\r
    else:\r
        rate_adjustment = 1.30  # 恶劣经验,加费30%\r
    return base_premium * rate_adjustment\r
```\r
\r
---\r
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## Reference Files / 参考文件|\r
\r
| File / 文件 | Content / 内容说明 |\r
|------|---------|\r
| `references/life_table_2025_usage.md` | 第四套生命表(2025)使用说明,含CL1/CL2/CL3三表差异说明 |\r
| `references/actuarial_pricing_formulas.md` | 各类产品精算定价公式汇总(含代码示例) |\r
| `references/critical_illness_pricing.md` | 重疾险定价专项,含恶化因子、重疾发生率表 |\r
| `references/reserve_calculation.md` | 准备金计算模板,含寿险/健康险/财险准备金 |\r
| `references/group_insurance_pricing.md` | 团险定价专项,含经验费率表、行业风险系数 |\r
Usage Guidance
This skill appears safe to install from an agentic-security perspective because it is instruction-only and requests no system access. Before using it for real insurance product pricing, verify the publisher/package, obtain the missing or official reference tables, and have a qualified actuary review the assumptions and generated code.
Capability Analysis
Type: OpenClaw Skill Name: insurance-actuarial-cn Version: 1.1.0 The skill bundle provides legitimate actuarial pricing tools and documentation based on the 2025 China Life Tables and C-ROSS framework. The Python code snippets in SKILL.md and the reference files are standard mathematical implementations for insurance premium and reserve calculations without any evidence of data exfiltration, malicious execution, or prompt injection.
Capability Assessment
Purpose & Capability
The content is coherent with China insurance actuarial pricing, reserves, and solvency analysis. However, it makes strong production-ready/complete claims while some referenced materials are absent and examples appear simplified, so real pricing decisions should be independently verified.
Instruction Scope
The instructions are domain-specific formulas, examples, and trigger keywords; no goal override, prompt-injection behavior, automatic tool use, or high-impact mutation workflow is evident.
Install Mechanism
No install spec or executable code files are present; the README contains only a user-directed standard install example.
Credentials
No required binaries, environment variables, credentials, OS restrictions, network endpoints, or local file access capabilities are declared.
Persistence & Privilege
No persistence, background execution, memory storage, account privileges, or credential/session access is described.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install insurance-actuarial-cn
  3. After installation, invoke the skill by name or use /insurance-actuarial-cn
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.1.0
v1.1.0 Bilingual optimization: English metadata + English summaries (4th Life Table 2025 + C-ROSS Phase II); Bilingual README.md; SEO title for China actuarial pricing.
v1.0.0
ClawHub首个中国精算定价垂直Skill,覆盖第四套生命表(2025 CL1/CL2/CL3)、费率厘定公式、准备金计算、团险定价、重疾险恶化因子。2025年第四套生命表10月发布,2026年1月1日起实施
Metadata
Slug insurance-actuarial-cn
Version 1.1.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 2
Frequently Asked Questions

What is China Insurance Actuarial Pricing Expert?

提供基于第四套生命表(2025)和CASS标准的中国保险产品精算定价、准备金计算及偿付能力资本分析。 It is an AI Agent Skill for Claude Code / OpenClaw, with 99 downloads so far.

How do I install China Insurance Actuarial Pricing Expert?

Run "/install insurance-actuarial-cn" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is China Insurance Actuarial Pricing Expert free?

Yes, China Insurance Actuarial Pricing Expert is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does China Insurance Actuarial Pricing Expert support?

China Insurance Actuarial Pricing Expert is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created China Insurance Actuarial Pricing Expert?

It is built and maintained by gechengling (@gechengling); the current version is v1.1.0.

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