Insurance Claims Intelligence Expert / 保险行业智能理赔专家

⚠️ SECURITY NOTICE / 安全声明

• Type: Educational reference / analytical framework ONLY
• No executable code, scripts, or binaries are included in this skill
• No persistent storage, network calls, background execution, or credential collection
• All outputs are for reference only and require human review before real-world application
• This skill does NOT provide financial, legal, or insurance advice
• Users must exercise their own judgment and consult qualified professionals

⚠️ DISCLAIMER / 免责声明

• English: This skill provides advisory templates, checklists, and decision-support frameworks ONLY. It does NOT contain executable models, trained GNN weights, or production OCR integrations. All accuracy figures (e.g., "92%-96%") are literature-reported benchmarks or design targets, NOT validated results of this skill. ALL claim approvals, denials, payout amounts, and fraud labels MUST be reviewed and confirmed by a licensed insurance professional before use. This skill is NOT a substitute for human judgment or regulatory compliance review.
• 中文： 本Skill仅提供咨询模板、检查清单和决策支持框架，不含可执行模型、已训练GNN权重或生产级OCR集成。所有准确率数据（如"92%-96%"）均来自文献基准或设计目标，非本Skill实测结果。所有理赔核准、拒付、赔付金额及欺诈标签，必须经持证保险专业人士审核确认后方可使用。本Skill不可替代人工判断或监管合规审查。

🔒 DATA SECURITY / 数据安全

• Medical invoices, diagnosis records, and claimant data are sensitive personal information under China's Personal Information Protection Law (PIPL). Before using OCR features, obtain user consent, redact/remove unnecessary PII, prefer on-prem/private deployment for production, and confirm the OCR vendor's data retention and cross-border transfer terms.
• API keys and credentials MUST be stored in environment variables or a secret manager. Never hardcode keys in production systems.
• English: This skill provides advisory templates, checklists, and decision-support frameworks ONLY. It does NOT contain executable models, trained GNN weights, or production OCR integrations. All accuracy figures (e.g., "92%-96%") are literature-reported benchmarks or design targets, NOT validated results of this skill. ALL claim approvals, denials, payout amounts, and fraud labels MUST be reviewed and confirmed by a licensed insurance professional before use. This skill is NOT a substitute for human judgment or regulatory compliance review.
• 中文： 本Skill仅提供咨询模板、检查清单和决策支持框架，不含可执行模型、已训练GNN权重或生产级OCR集成。所有准确率数据（如"92%-96%"）均来自文献基准或设计目标，非本Skill实测结果。所有理赔核准、拒付、赔付金额及欺诈标签，必须经持证保险专业人士审核确认后方可使用。本Skill不可替代人工判断或监管合规审查。

Artifact Type / 作品类型

This is a documentation-and-template skill. It contains:

• ✅ Workflow checklists and decision trees
• ✅ Report templates and output formats
• ✅ Reference architectures and integration guidance
• ✅ Example Python code (requires your own API keys and data)

It does NOT contain:

• ❌ Pre-trained ML/GNN models
• ❌ Executable OCR or claims processing code
• ❌ Bundled third-party API credentials

Trigger Keywords / 触发关键词

English Triggers: insurance claims advisory, claims workflow, claim analysis, medical OCR guidance, insurance fraud assessment, claim liability review, policy clause analysis, anti-fraud checklist, insurance tech reference, claims report template

中文触发词： 保险理赔咨询 / 理赔流程指导 / 理赔分析 / 医疗发票识别指导 / 理赔判责参考 / 责任认定流程 / 医疗险理赔 / 重疾险理赔 / 寿险理赔 / 意外险理赔 / 车险理赔 / 财产险理赔 / 理赔反欺诈 / 欺诈检测参考 / 骗保识别指导 / 理赔风控参考 / 保险条款解读 / 责任免除说明 / 保障范围分析 / 赔付比例计算 / 产品对比参考 / 条款比对指导 / 合同解读参考

Core Capabilities / 核心能力（咨询框架）

1. Medical Receipt OCR — Guidance Framework / 医疗票据OCR识别（指导框架）

支持的票据类型（覆盖全场景）：

⚠️ OCR Data Handling / OCR数据处理提醒

• Only send necessary fields to OCR providers; redact/unnecessary PII beforehand.
• Confirm the OCR vendor's data retention policy (Prefer: no storage / auto-delete within 24h).
• For production use, prefer private on-prem OCR deployment to avoid third-party data transfer.
• 中文： 仅发送必要字段至OCR服务商；事前脱敏/删除非必要个人信息；确认OCR厂商数据留存策略（优先：不留存/24小时内自动删除）；生产环境优先使用私有化本地部署OCR，避免第三方数据传输。

参考技术架构（需自行集成）：

原始图像
  ↓
图像预处理（去噪/倾斜校正/二值化）
  ↓
CNN特征提取（ResNet50/EfficientNet）—— 需自行训练或调用云服务API
  ↓
RNN序列建模（BiLSTM）+ Attention机制
  ↓
CRF层解码 → 结构化文本输出
  ↓
字段标准化 → JSON/表格结构化结果

2. Liability Determination — Advisory Framework / 理赔判责引擎（咨询框架）

咨询级判责检查清单（需人工逐项确认）：

规则1：等待期检查（人工确认）
  └─ 出险日期 - 保单生效日 < 等待期 → 建议拒付，需人工复核

规则2：既往症筛查（人工确认）
  └─ 既往症库匹配 → 责任免除 → 建议拒付/比例赔付，需人工复核

规则3：免赔额校验（人工确认）
  └─ 累计自付金额 < 免赔额 → 建议暂不赔付，需人工复核

规则4：就诊机构核查（人工确认）
  └─ 非二级及以上公立医院（需视条款）→ 提示确认，需人工复核

规则5：险种责任匹配（人工确认）
  └─ 就诊科室/诊断是否符合条款保障范围 → 建议全额/比例/拒付，需人工复核

⚠️ IMPORTANT / 重要提醒 The liability determination output is a decision-support suggestion ONLY. Final approval/denial MUST be made by an authorized human reviewer. This skill does NOT auto-approve any claim amount. 中文： 判责输出仅为决策支持建议，最终核准/拒付必须由授权人工审核员作出。本Skill不对任何理赔金额进行自动审批。

3. Anti-Fraud Assessment — Advisory Framework / 反欺诈评估（咨询框架）

反欺诈检查清单（咨询级）：

检查项1：就诊频率异常
  └─ 同一被保人短期内多次就诊 → 标记，建议人工调查

检查项2：票据真实性验证
  └─ 发票号重复 / 医院不存在 / 金额异常 → 标记，建议人工调查

检查项3：诊断与用药匹配性
  └─ 诊断与开具药品明显不符 → 标记，建议人工调查

检查项4：关系网络异常
  └─ 同一医生/医院集中出现在多起理赔 → 标记，建议人工调查

🔒 Anti-Fraud Data Governance / 反欺诈数据治理

• Retention limit / 留存期限：反欺诈图谱数据建议留存不超过 2 年，除非监管要求的更长留存期。
• Access control / 访问控制：图谱查询权限仅开放给授权欺诈调查员，禁止非授权人员访问。
• Data correction workflow / 数据更正流程：被保人有权请求更正错误数据，必须在 15 个工作日内处理。
• Poisoning safeguard / 污染防护：新案件数据进入图谱前，须经人工审核确认，防止恶意污染。

4. Claims Report Templates / 理赔报告模板

# 理赔分析报告（咨询草稿）
**生成时间**: YYYY-MM-DD HH:mm
**案件编号**: CL-XXXXXXXX
**险种类别**: [险种名称]
**处理状态**: [咨询草稿 — 需人工审核]
**免责声明**: 本报告为AI辅助生成的咨询草稿，所有结论须经持证理赔师审核确认后方可生效。
---
## 一、票据识别结果（仅供参考）
## 二、责任认定分析（仅供参考）
## 三、赔付计算参考（仅供参考）
## 四、反欺诈风险评估（仅供参考）
## 五、建议下一步行动（需人工确认）

Compliance & Human Review / 合规与人工审核要求

ALL outputs of this skill are drafts requiring licensed professional review. / 本Skill所有输出均为草稿，须经持证专业人士审核。

Output Format / 输出格式规范

All outputs must include the following disclaimer:

> ⚠️ **免责声明 / Disclaimer**
> 本输出为AI辅助咨询草稿，所有理赔决定、拒付结论、赔付金额及欺诈标签
> 须经【持证保险理赔师】审核确认后方可生效。
> This is an AI-assisted draft. All claim decisions must be reviewed by a
> licensed insurance adjuster before taking effect.

References / 参考文件

⚠️ Reference files contain example code only. You must:

• Provide your own API keys and store them in environment variables
• Provide your own training data and models
• Ensure human review of all outputs before use
• 中文： 参考文件仅含示例代码，您必须：自行提供API密钥并存入环境变量；自行准备训练数据和模型；确保所有输出经人工审核后方可使用。