Xpersona
agentCLAWHUBUnverified

Insurance Anti Fraud

基于国家监管办法，提供保险理赔和核保欺诈识别、风险评分、调查流程及防范建议，助力风控合规。 Skill: Insurance Anti Fraud Owner: gechengling Summary: 基于国家监管办法，提供保险理赔和核保欺诈识别、风险评分、调查流程及防范建议，助力风控合规。 Tags: CBIRC:1.1.0, anti-fraud:5.0.4, banking:5.0.0, cbirc:1.0.1, china-insurance:1.1.0, claims-fraud:1.1.0, compliance:5.0.4, dianjin:5.0.0, finance:5.0.0, fraud-detection:1.1.0, insurance:5.0.4, insurance-anti-fraud:5.0.5, insurance-crime:1.1.0, insurance-fraud:1.1.0, latest:5.0.5, risk-control:5.0.4, underwriting:1

OpenClaw
View on ClawHubHomepage

Rank

62

Safety

84

Downloads

1.7k

Updated

Oct 10, 2026

Version

5.0.5

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.7K downloads reported by the source. Last updated 10/10/2026.

Avoid when

  • Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor
Clawhubvendor · observed Oct 10, 2026
Protocol compatibility
OpenClawcompatibility · observed Oct 10, 2026
Adoption signal
1.7K downloadsadoption · observed Oct 10, 2026
Latest release
5.0.5release · observed Sep 24, 2026
Handshake status
UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17ewqc4f2s6gpcbm88hy7fgvn85kg1g:insurance-anti-fraud
  1. Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
  2. Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-gechengling-insurance-anti-fraud/snapshot"

Documentation

CLAWHUB

147,235 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: Insurance Anti-Fraud Expert
description: AI-powered insurance anti-fraud analysis skill — detects and prevents insurance fraud across all major insurance types. Covers claim fraud identification (10-feature engine), underwriting risk control, fraud investigation SOP, AI-driven big data anti-fraud models, and "黑灰产打击" framework. Based on China NFRA Anti-Insurance Fraud Measures (2024) and 公安部联合打击金融领域黑灰产 2026 campaign. Built for Chinese insurance company claims departments, risk control teams, and compliance teams. Keywords: insurance fraud, anti-fraud, claims fraud, risk control, underwriting, insurance crime, NFRA, China insurance, 黑灰产, 反欺诈, 理赔风控, 骗保识别, 黑产打击, 欺诈检测, 核保风控, 异常行为分析, 数字风控.
slug: insurance-anti-fraud
version: 5.0.5

capabilities:
  - educational-reference
  - advisory-only
  - requires-human-review
  - illustrative-code-samples
---

# Insurance Anti-Fraud Expert / 保险反欺诈分析专家

> **⚠️ SECURITY NOTICE / 安全声明**
> - **Type:** Educational reference / analytical framework ONLY
> - **No executable code, scripts, or binaries are bundled or run by this skill** — the Python fragments below are illustrative scoring logic for the user's own environment
> - **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**



> **English:** AI-powered insurance anti-fraud analysis expert — the definitive skill for detecting and preventing insurance fraud in the Chinese insurance market. Covers the complete CBIRC Anti-Insurance Fraud Measures (2024) framework, claim fraud identification engine (10 detection features), underwriting risk control system, fraud investigation workflows, and big data anti-fraud technology applications. Built for insurance company claims departments, risk control teams, and compliance teams.
>
> **中文:** 保险反欺诈分析专家——中国《反保险欺诈工作办法》合规垂直Skill。覆盖四位一体反欺诈工作体系、理赔欺诈识别引擎（10大欺诈特征）、核保风控体系、欺诈案件调查流程（SOP）、大数据反欺诈技术应用。适用：保险公司理赔部、风控部、合规部、反欺诈调查员、保险公司核保岗。

---

## Trigger Keywords / 触发关键词

**English:** anti-fraud, insurance fraud, claims fraud, fraud detection, risk control, underwriting risk, fraud investigation, money laundering, CBIRC compliance, claim review, insurance crime, fraud prevention, China insurance

**中文触发词（优先）：** 反保险欺诈、保险欺诈识别、理赔欺诈、反欺诈调查、理赔风控、核保风控、道德风险、保险诈骗、欺诈特征、异常理赔、保险黑产、虚假投保、带病投保、理赔调查、欺诈渗漏、反欺诈模型

---

## 数据最小化与合规前置声明 / Data Minimisation（2026-09-24 新增）

使用本技能任何模块前，先执行下列四条：
1. **只处理必要字段**：核查所需的最小信息集（如出险时间、就诊机构、金额、险种），不采集与目标无关的病史细节、家庭成员信息、位置轨迹。
2. **脱敏后输入**：姓名用“张**”，证件号仅保留前后各 2 位，联系电话与银行账号以哈希或编号替代。
3. **授权与最小必要**：调取医保、公安、法院、银保信等外部数据须有明确授权与业务必要性说明，禁止“先取后想”。
4. **预览后落盘**：任何风险评分、调查报告、移送建议，先在对话中完整展示给用户预览，经明确确认后再写入文件或对外报送。

> 本技能不内置可运行的查询接口与外部数据连接；文中的查询函数名为**示意性命名**，
> 需由使用方在自己的合规环境中实现。

---

## Core Capabilities / 核心能力

### 0. 最新监管动态（截至 2026-09-24）

| 时间 |

_meta.json

{
  "ownerId": "kn74e704j3ygjcygnpf02rdvd185js13",
  "slug": "insurance-anti-fraud",
  "version": "5.0.5",
  "publishedAt": 1790227501668
}

skill-card.md

## Description:

AI-powered insurance anti-fraud analysis skill for detecting and preventing insurance fraud across major insurance types, with claim fraud indicators, underwriting risk controls, investigation workflows, and big-data anti-fraud model guidance for the Chinese insurance market.

This skill is ready for commercial/non-commercial use.

## Publisher:

[gechengling](https://clawhub.ai/user/gechengling)

### License/Terms of Use:

MIT-0

## Use Case:

Insurance claims, risk control, underwriting, compliance, and fraud investigation teams use this skill to review fraud indicators, structure risk scoring, plan investigation steps, and draft human-reviewed anti-fraud analysis. It is advisory only and should not be used as the sole basis for claim denial, regulatory reporting, or legal conclusions.

### Deployment Geography for Use:

China

## Known Risks and Mitigations:

Risk: Regulatory statements, scoring thresholds, and workflows may be outdated or incomplete for a live insurance decision.

Mitigation: Verify China-specific regulatory content against current official sources and qualified legal or compliance reviewers before operational use.

Risk: Fraud scores or model outputs could be treated as conclusive evidence and cause incorrect claim denial or escalation.

Mitigation: Use scores only as investigation signals, require independent supporting evidence, and keep human review in the decision process.

Risk: Anti-fraud investigations can involve sensitive personal, medical, financial, and law-enforcement-related data.

Mitigation: Apply data minimization, de-identification, explicit authorization, and documented business necessity before using external or sensitive data.

Risk: Generated reports or referral recommendations could be sent externally before review.

Mitigation: Preview risk scores, investigation reports, and referral recommendations with the user, then require explicit confirmation and legal or compliance review before saving or reporting.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/gechengling/skills/insurance-anti-fraud)
- [Artifact skill definition](artifact/SKILL.md)

## Skill Output:

**Output Type(s):** [text, markdown, code, guidance]

**Output Format:** [Markdown with structured tables, checklists, workflows, and illustrative Python snippets]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Advisory outputs require human review and user confirmation before file creation, external reporting, or operational use.]

## Skill Version(s):

5.0.5 (source: frontmatter and server release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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Machine-readable data

The same record, as JSON, for agents and crawlers.

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      "label": "Vendor",
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      "sourceUrl": "https://clawhub.ai/gechengling/skills/insurance-anti-fraud",
      "sourceType": "profile",
      "confidence": "medium",
      "observedAt": "2026-10-10T05:33:45.965Z",
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    },
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      "sourceUrl": "https://www.xpersona.co/api/v1/agents/clawhub-gechengling-insurance-anti-fraud/contract",
      "sourceType": "contract",
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      "label": "Adoption signal",
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      "sourceUrl": "https://clawhub.ai/gechengling/insurance-anti-fraud",
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      "confidence": "medium",
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      "isPublic": true
    },
    {
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      "sourceUrl": "https://clawhub.ai/gechengling/insurance-anti-fraud",
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  "events": [
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      "confidence": "medium",
      "observedAt": "2026-09-24T05:25:01.668Z",
      "isPublic": true
    }
  ]
}

Record generated Oct 10, 2026.

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