AI-powered China insurance actuarial pricing skill �� uses the 4th Life Table (2025, effective 2026-01-01) and C-ROSS Phase II (Rules II 2024) framework. Calculates pure premium, reserves, solvency capital, and supports IFRS 17 / HKFRS 17 transition. Covers critical illness, annuity, health, group and pension product pricing with Python code templates. Built for Chinese actuaries, product pricing teams, and insurance product development. Keywords: actuarial, pricing, life table 2025, C-ROSS, ...
Scanned 9/9/2026
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---
name: China Insurance Actuarial Pricing Expert
description: AI-powered China insurance actuarial pricing skill �� uses the 4th Life Table (2025, effective 2026-01-01) and C-ROSS Phase II (Rules II 2024) framework. Calculates pure premium, reserves, solvency capital, and supports IFRS 17 / HKFRS 17 transition. Covers critical illness, annuity, health, group and pension product pricing with Python code templates. Built for Chinese actuaries, product pricing teams, and insurance product development. Keywords: actuarial, pricing, life table 2025, C-ROSS, IFRS 17, China insurance, solvency capital, insurance product design, ���㶨��, ������������, �����嶨, ��������, �ؼ��ն���, ����ն���, ҽ���ն���, Python����, ����ģ��.
slug: insurance-actuarial
version: "5.0.1"
capabilities:
- educational-reference
- advisory-only
- requires-human-review
- no-executable-code
---
# China Insurance Actuarial Pricing Expert (4th Life Table 2025) / �й����㶨��ʦ��������������2025��|
> **⚠️ SECURITY NOTICE**
> - **Type:** Educational reference / analytical framework ONLY
> - **No executable code, scripts, or binaries included**
> - **No persistent storage, network calls, or background execution**
> - **No credential collection, PII processing, or system access**
> - **All outputs require human review before real-world application**
> - **NOT financial, legal, or insurance advice**
### ���ռ�����¶�̬ [2026-05-25����]
| ��̬���� | ����ժҪ | Ӱ�췶Χ |
|---------|---------|---------|
| ���ռ�� | 2026��4�±����¹���أ���ǿ�ձ���������42.2������������ҽ������ҩ | ���㶨��ģ������±��Ѽ������ͷ����ʼ��� |
| ���ռ�� | �����ղ�Ʒ�ּ��ƶ�(P1-P5)ʵʩ���ֺ�����ʾ����������3.5% | ���㶨��ģ������±��Ѽ������ͷ����ʼ��� |
| ���ռ�� | �ؼ������ݣ�����12����֢+8����֢��Ͷ��������������75�� | ���㶨��ģ������±��Ѽ������ͷ����ʼ��� |
> **���ݽ�ֹ**: 2026-05-25 | ��Դ�����ҽ��ڼල�����ܾ֡�����Q1��������ҵ������Ϣ
> **����**: ���϶�̬���ο��������Թٷ����·���Ϊ
> **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.
>
> **����:** �й����㶨��ʦ�������ڵ�������������2025�귢����2026��1��1��ʵʩ���ͳ��������ڹ��̣�C-ROSS Rules II���Ĵ�ֱ����Skill�����Ǵ����Ѽ��㡢������㡢�������ʱ�ռ���붨��Эͬ�����ն���ר������������㶨��Python���롣���ã�����ʦ����Ʒ���۸ڡ����ղ�Ʒ���������վ���ʵϰ����������ѯ��
---
## Trigger Keywords / �����ؼ���
**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
**���Ĵ����ʣ����ȣ���** ���㡢���㶨�ۡ������嶨���������������ʱ��������ʱ����ؼ������ʡ����鷢���ʡ�������㡢���������ʱ������ղ�Ʒ���ۡ�CASS���й�����ʦЭ�ᡢ���������������ؼ������ӡ���Ʒ���
---
## Core Capabilities / ��������|
### 0. 2025-2026 Latest Regulatory Updates / ���¼�ܶ�̬������2026��5�£�|
| ʱ�� | ���� | ����Ӱ�� |
|------|------|---------|
| **2025��10��29��** | ������������������CL1/CL2/CL3�� | 2026��1��1����ǿ��ʹ�ã�������/�����ֵȫ������ |
| **2024��3��18��** | �����������ȫ��ʵʩ | ����ʱ������ʷ��ա��ؼ�������ȫ������ |
| **2024����** | Ԥ�����������µ���3.0% | ��ͳ�ն��۳ɱ��½����ֺ��վ������������ |
| **2025����** | IFRS 17/HKFRS 17��A�����������ƹ� | ��������������CSM̯��Ҫ������ |
| **2026��1��1��** | ������������ǿ��ִ�� | ���������ղ�Ʒ�밴�±����¶��ۻ����� |
### 1. 4th Life Table (2025) Full Analysis / ��������������2025��ȫ����|
**����������**
- 2025��10��29�գ��й�����ʦЭ����ʽ����
- ���ڼ���ܾ�ͬ������ʵʩ֪ͨ
- **��2026��1��1����ʵʩ**�����������ڣ�
- ��������������������ף�2010��汾������ӳ�й��˾������ӳ������仯����|
#### ���ź��ı�|
| ���� | Ӣ�� | ��; |
|------|------|------|
| **��������ҵ��һ��** | CL1 | �������ա��������յȲ�Ʒ���� |
| **��������ҵ�����** | CL2 | ��ȫ�ա�����յȲ�Ʒ���� |
| **������ҵ���** | CL3 | ר�����ϱ��ա��������Ȳ�Ʒ���� |
#### ���������������Ҫ�仯|
| ά�� | �����ף�2010�棩 | �����ף�2025�棩 | Ӱ�� |
|------|---------------|----------------|------|
| �����ڼ� | 2005-2008�� | 2018-2023�� | ����ӳ��ǰ����ˮƽ |
| ����Ԥ��������60�꣩ | +20.7�� | +22.5�� | ������⸶���ӳ� |
| Ů��Ԥ��������60�꣩ | +24.2�� | +26.1�� | Ů�������ճɱ����� |
| �����������ࣩ | ��ר�� | **����ר���** | �����ն��۸��� |
### 2. China Actuarial Pricing Methodology / �й����㶨�۷�����|
#### ���ղ�Ʒ���ۻ�����ʽ|
```
ë���� = ������ �� (1 + ���ӷ�����)
������ = ���ɴ����� / �����ֵϵ��
= ���ս�� �� ƽ�������� �� ƽ�������ڼ� �� ����ϵ��
```
#### ���ղ�Ʒ����Ҫ��|
| Ҫ�� | ˵�� | ����ȡֵ |
|------|------|---------|
| **Ԥ��������** | ��Դ�������������� | CL1/CL2/CL3 |
| **Ԥ������** | ���չ�˾Ͷ�������ʼ��� | 2.5%-3.5%��2024�����3%�� |
| **Ԥ��������** | ��������/����/������� | 10%-35%����������� |
| **Ԥ��������** | ��˾����Ҫ�� | 5%-15% |
| **���ѽ��ɷ�ʽ** | ����/�ڽ�/�꽻 | �ڽ������� |
#### �ؼ��ն��۷�����Pythonʾ����|
```python
# �ؼ��մ����Ѽ�����
def critical_illness_premium(age, sum_assured, policy_term, payment_term):
# ����������������������ҵ�����(CL2)Ϊ����
i = 0.025 # Ԥ�����ʣ�3.0%����ı��ؼ��裩
v = 1 / (1 + i)
# �ؼ�������ֵ
A_crit = 0
for t in range(policy_term):
q_crit_t = lookup_critical_illness_rate(age + t, t)
A_crit += v**(t+1) * q_crit_t * sum_assured
# ����������ֵ������/ȫ�У�
A_death = 0
for t in range(policy_term):
q_death_t = lookup_mortality_rate_CL2(age + t, t)
A_death += v**(t+1) * q_death_t * sum_assured
# ���������ֵ���ɷ��ڣ�
?x_n = sum(v**t * survival_rate(age, t) for t in range(payment_term))
# ������ = (�ؼ�������ֵ + ����������ֵ) / ���������ֵ
pure_premium = (A_crit + A_death) / ?x_n
return pure_premium
```
### 3. Reserve Calculation / �������|
| ���� | ���� | ������� |
|------|------|---------|
| **δ������������UEPR��** | Ϊδ���⸶����Ǯ | ʣ�ౣ�����ڵĴ�������ֵ |
| **�ѷ����������IBNR��** | �ѷ���δ������� | ������ƣ����������εȷ����� |
| **��������** | ���ѳ����Բ��Ժ� | �ֽ������� |
| **������������** | ���ٱ��ճ��ڸ�ծ | ������������ƽ������ |
### 4. Group Insurance Pricing / ���ն���ר��|
#### ���ն���Ҫ��|
| Ҫ�� | ˵�� | �������� |
|------|------|---------|
| **�����ģ** | �α����� | ����Խ�࣬����ԽС |
| **��ҵ����** | ��ҵ������ҵ | ��ҵ���յȼ�ϵ�� |
| **����ṹ** | Ա��ƽ������/�ֲ� | Ա��ƽ������Խ����Խ�� |
| **��ʷ�⸶** | ��ȥ1-3���⸶��¼ | ������ʵ��� |
| **���������** | ���Ϸ�Χ/����/���� | �������Ӱ�� |
#### ���վ�����ʼ���|
```python
def group_experience_rate(base_premium, experience_factor):
if experience_factor < 0.7:
rate_adjustment = 0.85 # �������飬����15%
elif experience_factor < 0.9:
rate_adjustment = 0.95 # �Ϻþ��飬����5%
elif experience_factor < 1.1:
rate_adjustment = 1.00 # ������
elif experience_factor < 1.3:
rate_adjustment = 1.15 # �ϲ�飬�ӷ�15%
else:
rate_adjustment = 1.30 # ���Ӿ��飬�ӷ�30%
return base_premium * rate_adjustment
```
### 5. IFRS 17 / HKFRS 17 �����¹棨A�������������ã�|
#### IFRS 17 �Ծ��㶨�۵ĺ���Ӱ��
| ��Ŀ | ԭIFRS 4 | IFRS 17 �仯 | ����Ӧ�� |
|------|---------|------------|---------|
| **��������ȷ��** | �ڼ䱣�� | ���շ������루�DZ��ѣ� | ���ѷֽ�Ϊ�⸶/����/CSM�ͷ� |
| **�������** | ��ʷ�ɱ� | �����г�һ�²��� | ��Ҫ������������� |
| **��ͬ�����CSM** | �� | ����������δ������ | ÿ���ͷţ�����һ��ȷ�� |
| **���յ���RA** | ��ר�� | �������ǽ��ڷ��յIJ�ȷ���� | ͨ��������ˮƽ��/CTE�� |
| **�����ͬ** | ���� | ����ȷ����ʧ | ��������������ӯ���� |
```python
# IFRS 17 ��Լ�ֽ��� FCF ������
def ifrs17_fcf(policy, discount_rate_curve, risk_adjustment_pct):
"""
��Լ�ֽ��� = δ���ֽ�����ֵ + ���յ���
Future Cash Flows = PV(outflows) - PV(inflows) + Risk Adjustment
"""
pv_outflows = sum(cf * discount_factor(t, discount_rate_curve)
for t, cf in enumerate(policy.expected_claims))
pv_inflows = sum(prem * discount_factor(t, discount_rate_curve)
for t, prem in enumerate(policy.expected_premiums))
risk_adj = (pv_outflows - pv_inflows) * risk_adjustment_pct # ����ֵ��5%-15%
return pv_outflows - pv_inflows + risk_adj
```
### 6. Interest Rate Risk & ALM / ���ʷ������ʲ���ծƥ��|
#### 2024��Ԥ�������µ������붨�۲���
| ���� | Ԥ������ | ���۲��� | ��Ʒ���� |
|------|---------|---------|---------|
| **2024��9��1��ǰ** | ��3.5% | ��ǰ���������� | ��ͳ����/����� |
| **2024��9��1�պ�** | ��3.0% | �����ʶ��ۻ��� | ���������� |
| **2025������** | ��3.0%���ȶ��� | ���ӷֺ측���ռ� | �ֺ���/������ |
#### ����ѹ�����ԣ����������
```python
def interest_rate_stress_test(policy, base_rate):
scenarios = {
"base": base_rate,
"down_50bp": base_rate - 0.005,
"down_100bp": base_rate - 0.010,
"down_150bp": base_rate - 0.015,
}
results = {}
for scenario, rate in scenarios.items():
reserve = calculate_reserve(policy, rate)
results[scenario] = {
"reserve": reserve,
"surplus_change": reserve - calculate_reserve(policy, base_rate)
}
return results
```
---
## Reference Files / �ο��ļ�|
| File / �ļ� | Content / ����˵�� |
|------|---------|
| `references/life_table_2025_usage.md` | ������������(2025)ʹ��˵������CL1/CL2/CL3��������˵�� |
| `references/actuarial_pricing_formulas.md` | �����Ʒ���㶨�۹�ʽ���ܣ�������ʾ���� |
| `references/critical_illness_pricing.md` | �ؼ��ն���ר��������ӡ��ؼ������ʱ� |
| `references/reserve_calculation.md` | �������ģ�壬������/������/�������� |
| `references/group_insurance_pricing.md` | ���ն���ר���������ʱ�����ҵ����ϵ�� |
---
### CL1/CL2/CL3�ؼ����������ʣ�2025�桤��ѡ��
**CL1����������ҵ��һ��������/���������ã�**��
| ���� | ����������(qx, ��) | Ů��������(qx, ��) | ˵�� |
|------|------------------|------------------|------|
| 20 | 0.28 | 0.15 | �����ڣ������ʼ��� |
| 30 | 0.42 | 0.23 | ����֮�꣬���Կ�ʼ���� |
| 40 | 1.12 | 0.67 | ���꣬����������>Ů��1.7�� |
| 50 | 3.45 | 1.98 | ֪��֮�꣬���������� |
| 60 | 8.92 | 5.14 | �����꣬���������ʽӽ�Ů��2�� |
| 70 | 21.34 | 13.87 | ��ϡ֮�꣬�����С |
| 80 | 58.12 | 42.56 | �ȳ�֮�꣬Ů���� |
**CL2����������ҵ���������ȫ/������ã�**��
| ���� | ����������(qx, ��) | Ů��������(qx, ��) | ˵�� |
|------|------------------|------------------|------|
| 20 | 0.25 | 0.13 | ��CL1���ͣ������壩 |
| 30 | 0.38 | 0.20 | ��ѡ����� |
| 40 | 1.05 | 0.60 | ������ѡ |
| 50 | 3.20 | 1.85 | ֪����ѡ |
| 60 | 8.10 | 4.75 | ������ѡ |
| 70 | 19.50 | 12.80 | ��ϡ��ѡ |
| 80 | 53.20 | 39.10 | �ȳ���ѡ |
**CL3��������ҵ�����ר���������ã�**��
| ���� | ����������(qx, ��) | Ů��������(qx, ��) | ˵�� |
|------|------------------|------------------|------|
| 60 | 7.50 | 4.20 | ���Ͻ���ȡ�ڿ�ʼ |
| 65 | 12.30 | 7.10 | ���Կ�ʼ�������� |
| 70 | 19.80 | 12.50 | Ů�Լ����� |
| 75 | 31.20 | 21.40 | ��Ů�����խ |
| 80 | 48.50 | 35.20 | ������ |
| 85 | 72.30 | 56.10 | Ԥ������<5�� |
| 90 | 103.40 | 84.20 | �����䣬������>100�� |
> **������Դ**���й�����ʦЭ�ᡶ�й���������ҵ������������2025����CL1/CL2/CL3��2025��10��29�շ�����2026��1��1����ǿ��ִ�С�
**ʹ�ý���**��
- ��������/�������ղ�Ʒ �� ����ʹ�� **CL1**����������ߣ�������أ�
- ��ȫ��/����գ������ϣ� �� ʹ�� **CL2**���������еȣ�
- ר����ҵ���ϱ��� �� ����ʹ�� **CL3**����������ͣ����Ͻ����ѹ�����
---
*GitHub: https://github.com/gechengling/insurance-actuarial-cn*
## 核心工作流程(Dianjin融合版)
### 第一步:产品定位与定价目标(Dianjin精髓:市场洞察驱动)
| 定价维度 | 核心问题 | 决策输出 |
|---------|---------|---------|
| **产品定位** | 目标客群/保障范围/竞争格局 | 产品形态、保障责任、费率策略 |
| **市场分析** | 同类产品费率/市场份额/增长趋势 | 定价竞争力分析、差异化策略 |
| **盈利目标** | 目标利润率/投资收益率假设 | 定价假设、利润测试标准 |
| **监管要求** | 预定利率上限/偿付能力/准备金 | 合规边界、监管风险点 |
**定价目标输出**:
```
【定价目标书】
- 产品名称:[XXX]
- 目标客群:[年龄/性别/职业/收入]
- 定价假设:预定利率[X]%,死亡率表[CL1/CL2/CL3],费用率[X]%
- 目标利润率:[X]%(IRR口径)
- 监管要求:偿二代下资本占用[X]元/万元保费
```
---
### 第二步:发生率假设与经验分析(Dianjin精髓:大数据发生率)
#### 2.1 发生率表选择框架
| 发生率类型 | 推荐表 | 适用产品 | 监管认可 |
|---------|---------|---------|---------|
| **死亡率** | CL1/CL2/CL3(2025版) | 定寿/终身寿 | 强制使用 |
| **重疾率** | CII1/CII2(2024版) | 重疾险/医疗险 | 强制使用 |
| **医疗率** | MIR1/MIR2(2025版) | 医疗险/护理险 | 推荐评审 |
| **年金率** | AFR1/AFR2(2025版) | 年金险/养老金 | 强制使用 |
| **退保率** | SUR1/SUR2(经验) | 所有产品 | 自行设定 |
#### 2.2 经验发生率分析
```
【经验分析流程】
1. 数据清洗:剔除异常保单、修正录入错误
2. 经验提取:实际发生率 = f(年龄,性别,保单年度,渠道)
3. 趋势调整:医疗进步/疾病谱变化/退保行为变化
4. 置信区间:95%CI,最终发生率 = 经验±[X]%
5. 与行业对比:中国精算师协会经验表、同业数据
```
---
### 第三步:保费计算与利润测试(Dianjin精髓:动态利润分析)
#### 3.1 纯保费计算公式
| 计算项目 | 公式 | 参数说明 |
|---------|------|---------|
| **纯保费(NP)** | NP = Σ CF_t × v^t × p_x+t | CF=净现金流,v=贴现因子 |
| **费用附加** | L(oading) = α×NP + β×P + γ | α=固定%+β=变动%+γ=固定额 |
| **总保费(P)** | P = (NP + L) / (1 - 利润率%) | 含目标利润的总保费 |
#### 3.2 利润测试模板
| 测试场景 | IRR(%) | 投资收益率(%) | 退保率(%) | 综合成本率(%) | 结论 |
|---------|--------|--------------|------------|------------------|------|
| **基准场景** | [X] | [X] | [X] | [X] | 合格/不合格 |
| **悲观场景** | [X] | [X]-50bp | [X]+20% | [X]+200bp | 合格/不合格 |
| **乐观场景** | [X] | [X]+50bp | [X]-20% | [X]-200bp | 合格/不合格 |
**IRR计算代码模板**:
```python
# 定价IRR计算
import numpy as np
def calculate_irr(premiums, claims, expenses, reserves):
cashflows = [premiums - claims - expenses - (reserves[t+1] - reserves[t])
for t in range(len(premiums))]
return np.irr(cashflows)
# 示例:10年期缴重疾险
premiums = [10000] * 10 # 年缴1万
claims = [0, 5000, 15000, ...] # 逐年理赔
irr = calculate_irr(premiums, claims, [500]*10, [0]*10)
print(f"定价IRR: {irr:.2%}")
```
---
### 第四步:准备金评估与偿付能力(Dianjin精髓:偿二代资本计量)
#### 4.1 准备金计量框架
| 准备金类型 | 计量基础 | 监管公式 | 资本占用 |
|---------|---------|---------|---------|
| **最优估计准备金(BEL)** | 预期现金流现值 | BEL = Σ CF × v^t | - |
| **风险边际(RM)** | 成本/置信度法 | RM = BEL × [X]% | + |
| **偿付能力准备金(SCR)** | Solvency II/C-ROSS | SCR = max(BEL+RM, 监管要求) | + |
#### 4.2 偿二代资本占用分析
```
【C-ROSS资本占用计算】
1. 保险风险资本(IC):死亡率/重疾率/退保率/费用风险
2. 市场风险资本(MC):利率/权益/房地产/汇率风险
3. 信用风险资本(CR):再保/债券/存款对手方风险
4. 操作风险资本(OR):固定+变动,按保费/准备金的[X]%
5. 总资本要求 = IC + MC + CR + OR
偿二代下资本占用:[X]元/万元保费
```
---
### 第五步:定价报告与监管备案(Dianjin精髓:标准化输出)
**精算定价报告模板**:
```markdown
# [产品名称] 精算定价报告
## 一、产品概述
[产品定位/目标客群/市场竞争分析]
## 二、定价假设
[生命表/重疾表/投资收益率/费用率/退保率]
## 三、保费计算
[纯保费/费用附加/总保费/IRR测试结果]
## 四、准备金与资本
[准备金计量/偿二代资本占用/IFRS17影响]
## 五、敏感性分析
[利率/死亡率/费用率/退保率±10%影响]
## 六、监管合规
[偿二代合规/准备金充足性/定价披露要求]
## 七、结论与建议
[定价结论/盈利能力/监管沟通策略]
```
---
## 合规约束与审计规则(Dianjin精髓)
1. **监管合规**:严格遵守《保险法》《精算工作规范》《偿二代规则》
2. **数据质量**:经验数据需清洗、校验,异常情况需说明
3. **假设合理性**:定价假设需有依据,不得为竞争恶性定价
4. **准备金充足**:准备金评估需保守,不得为利润释放不足提
5. **审计留痕**:所有计算过程、假设选择、模型选择需留痕可审计
---
## 测试用例(Dianjin精髓)
**测试场景1:重疾险定价**
- 输入:30岁男性,20年缴,保额50万
- 预期输出:年缴保费、IRR、准备金、资本占用
- 通过标准:定价IRR>3.0%,资本占用<500元/万元
**测试场景2:年金险准备金评估**
- 输入:终身年金,领取期20年,投资收益率4.0%
- 预期输出:BEL、RM、SCR
- 通过标准:SCR/准备金<15%
**测试场景3:偿二代资本计算**
- 输入:重疾险产品组合,保费1亿
- 预期输出:IC/MC/CR/OR分项,总资本要求
- 通过标准:计算逻辑符合C-ROSS规则
---
## 关联技能(Dianjin精髓)
- `insurance-claims-intelligence`:理赔数据→经验发生率更新
- `insurance-anti-fraud`:欺诈识别→定价反欺诈因子
- `insurance-product-design`:精算定价→产品设计协同
- `finance-ai-strategy`:保险AI→精算AI战略协同
---
*本技能融合阿里点金(Qwen Dianjin)金融AI精髓,专注经验发生率分析、动态利润测试、偿二代资本计量。*
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