Insurance Claims Intelligence

👤 gechengling 📦 v5.0.3 ⭐ 4.4 ⬇️ 1.3K 下载
💼 行业专业 免费

📖 技能介绍


name: Insurance Claims Intelligence Expert description: Advisory skill for insurance claims processing workflows — provides templates, checklists, and decision-support frameworks for medical OCR, liability determination, anti-fraud assessment, and claims reporting. Human review required for all claim decisions. Keywords: insurance claims, claims advisory, medical OCR, anti-fraud, insurance tech, China insurance, decision support, 智能理赔, 理赔风控, 医疗单据识别, 责任认定, 理赔报告, 秒赔, 理赔决策, 医疗险理赔, 重疾理赔, 车险理赔. slug: insurance-claims-intelligence version: 1.2.0

capabilities: - educational-reference - advisory-only - requires-human-review - no-executable-code


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):

Receipt Type / 票据类型 Extracted Fields / 识别内容 Insurance Types / 适用险种
全国统一门诊发票 发票号、医院、金额、明细项目 医疗险、意外险
全国统一住院发票 入院/出院日期、总金额、自费比例 医疗险、重疾险
医疗费用明细清单 药品明细、检查项目、单价、数量 医疗险
医保结算单 医保账户支付、自付金额、报销比例 医疗险
出院小结 诊断、住院天数、治疗经过 重疾险、寿险
病历首页 主要诊断、手术名称、ICD编码 重疾险
检查报告单 影像报告、检验结果 重疾险
费用结算单 分项金额、总计金额 财产险、责任险

⚠️ 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。

发现更多技能插件,请访问7w4.net。

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

Compliance Item / 合规项 Regulatory Basis / 监管依据 Human Review Requirement / 人工审核要求
理赔时效 《保险法》第23条 核定结果须经人工确认后发出
材料完整性 理赔管理办法 缺失材料列表由人工最终确认
反欺诈合规 《反保险欺诈工作办法》2024 欺诈标记须经人工调查确认
数据安全 《个人信息保护法》 医疗数据脱敏处理须经人工检查
资金安全 反洗钱规定 大额理赔须人工复核 + 主管审批

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

File / 文件 Content / 内容说明
references/claims_ocr_tech.md OCR技术架构参考 + 4家服务商对比 + Python示例代码(需自行配置API Key)
references/claims_liability_engine.md 判责规则参考 + 机器学习模型参考 + 3家公司实践参考
references/claims_report_templates.md 报告模板 + 7种险种通知书模板参考

⚠️ 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。

🤖 AI 评测

这个Skill质量较好,是一个实用的保险理赔咨询工具。它提供了完整的理赔检查清单、多种险种的报告模板,以及医疗票据识别的技术指导,对理赔工作有实际帮助。最大优点是安全意识强,处处强调人工审核的重要性,数据保护提醒也很到位。不足之处是文档内部版本号不一致,可能造成混淆;而且某些代码示例在实际使用时需要自己申请API密钥,有一定使用门槛。总体适合需要参考框架的开发者或理赔团队使用,但不建议普通用户直接用于实际理赔决策。

📊 多维度评分

适应性4.6
规范性4.4
有效性4.2
可靠性4.3
可信度5

📁 包含文件 (7 个)

📄 README.md 7.4 KB
📄 SKILL.md 12.3 KB
📄 _meta.json 148 B
📄 references/claims_liability_engine.md 18.4 KB
📄 references/claims_ocr_tech.md 13.7 KB
📄 references/claims_report_templates.md 11.1 KB
📄 skill-card.md 2.1 KB