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Insurance Operations Automation

作者 1kalin · GitHub ↗ · v1.0.0
cross-platform ⚠ suspicious
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当前安装
1
版本数
在 OpenClaw 中安装
/install afrexai-insurance-automation
功能描述
Automates insurance workflows including underwriting, claims triage, policy management, renewals, compliance, and broker operations with industry benchmarks.
使用说明 (SKILL.md)

Insurance Operations Automation

Comprehensive insurance operations framework for AI agents. Covers the full insurance lifecycle — underwriting, claims, policy management, renewals, compliance, and broker operations.

What This Skill Does

Guides your AI agent through insurance-specific workflows with industry benchmarks, regulatory requirements, and automation priorities.

Capabilities

1. Underwriting Assessment

  • Risk scoring framework (12 factors, weighted by line of business)
  • Data enrichment checklist (credit, claims history, property data, telematics)
  • Referral triggers and authority limits by tier
  • Combined ratio targets by line: Auto (95-98%), Home (85-92%), Commercial (88-95%), Life (varies by mortality table)

2. Claims Processing Pipeline

  • FNOL intake automation (voice + digital, structured extraction)
  • Severity triage: Green (auto-approve \x3C$2K) → Yellow (adjuster review $2K-$25K) → Red (SIU referral >$25K or fraud indicators)
  • Subrogation identification triggers
  • Reserve estimation formulas by claim type
  • Settlement authority matrix

3. Policy Administration

  • Quote-to-bind workflow (target: \x3C15 min for personal lines)
  • Mid-term adjustment processing
  • Renewal scoring: retention probability model (7 factors)
  • Cancellation/non-renewal compliance by state/jurisdiction

4. Broker Operations (Jointly AI Model)

  • 5-agent pipeline architecture: Intake → Research → Quoting → Analysis → Delivery
  • Market panel management and placement optimization
  • Quote comparison normalization across carriers
  • FCA/state regulatory compliance verification
  • Parallel execution: up to 4 simultaneous carrier interactions

5. Compliance & Regulatory

  • US: State DOI requirements, NAIC model laws, Solvency II (reinsurers)
  • UK: FCA handbook (ICOBS, SYSC), Consumer Duty, IDD compliance
  • EU: Solvency II, IDD, GDPR for policyholder data
  • Anti-fraud indicators (SIU trigger list — 15 red flags)
  • SAR/suspicious activity reporting thresholds

6. Insurance Metrics Dashboard

Metric Personal Lines Target Commercial Target
Combined Ratio 95-98% 88-95%
Loss Ratio 60-70% 55-65%
Expense Ratio 25-32% 28-35%
Claims Settlement Time \x3C48h (auto) \x3C14 days
Policy Issuance Time \x3C15 min \x3C24h
Renewal Rate >85% >80%
Quote-to-Bind Ratio >25% >15%
NPS >40 >35

7. Automation Priority Matrix

Process Hours/Month (50-person broker) Agent-Ready? Expected Savings
Quote comparison 160h Yes — now $140K-$280K/yr
FNOL intake 120h Yes — now $105K-$210K/yr
Policy document generation 80h Yes — now $70K-$140K/yr
Renewal processing 100h Yes — now $87K-$175K/yr
Compliance checks 60h Yes — now $52K-$105K/yr
Subrogation identification 40h Partial $35K-$70K/yr
Complex claims adjustment 200h Human-in-loop $50K-$100K/yr

8. Insurance-Specific Agent Prompts

Underwriting Agent:

You are an underwriting assessment agent. For each submission:
1. Extract all risk factors from the application
2. Score each factor against the risk matrix (1-10 scale)
3. Calculate composite risk score (weighted by line of business)
4. Flag any referral triggers (prior losses >3 in 5yr, credit \x3C600, high-hazard occupation)
5. Recommend: Auto-approve / Refer to senior / Decline with reason
6. Generate underwriting memo with supporting data

Claims Triage Agent:

You are a claims triage agent. For each FNOL:
1. Extract: date of loss, type, description, estimated amount, policyholder details
2. Verify active coverage and applicable endorsements
3. Assign severity: Green (\x3C$2K auto-process) / Yellow ($2K-$25K adjuster) / Red (>$25K or fraud flags)
4. Check fraud indicators against the 15-point SIU trigger list
5. Set initial reserve based on claim type benchmarks
6. Route to appropriate handler with priority score

90-Day Deployment Roadmap

Month 1: Deploy intake + quote comparison agents. Target: 70% of personal lines quotes handled autonomously.

Month 2: Add claims triage + policy document generation. Target: FNOL processing \x3C5 minutes, auto-approval for Green claims.

Month 3: Compliance monitoring + renewal automation. Target: 85%+ renewal rate, zero compliance gaps.

Cost Framework

  • Solo broker/MGA: $2K-$5K/month (2-3 agents)
  • Mid-size broker (20-50 staff): $5K-$15K/month (5-8 agents)
  • Carrier/large broker (100+ staff): $15K-$50K/month (10-20 agents)

Resources

  • Calculate your insurance automation ROI → AI Revenue Leak Calculator
  • Full Insurance & Fintech Context Pack → AfrexAI Context Packs — $47, includes fintech agent configurations, compliance frameworks, and industry benchmarks
  • Configure your insurance agent stack → Agent Setup Wizard
  • Bundle: Pick 3 packs for $97 | All 10 for $197 | Everything $247
安全使用建议
This skill appears to be a detailed template/playbook rather than a turnkey integration. Before installing or using it: (1) Verify the vendor/source — no homepage and an unknown owner ID are additional risk factors. (2) Do not provide any production credentials (carrier APIs, DBs, telephony keys) until you have a clear adapter/connector implementation and a data-handling/privacy policy. (3) Expect the SKILL.md to instruct agents to handle PII and regulated data — enforce data governance, sandbox the skill, and test with synthetic data first. (4) Ask the author for an integration spec listing required env vars, endpoints, and where sensitive data flows; require documentation on retention, encryption, and compliance obligations. (5) Be cautious about the external links and paid context packs — they are marketing assets and not necessary to verify the skill's technical behavior.
功能分析
Type: OpenClaw Skill Name: afrexai-insurance-automation Version: 1.0.0 The skill bundle provides comprehensive instructions and context for an AI agent to perform insurance operations, including underwriting, claims processing, and policy administration. The `SKILL.md` and `README.md` files contain detailed business logic, agent prompts, and external links to `github.io` resources, all of which are aligned with the stated purpose. There is no evidence of data exfiltration, malicious execution, persistence mechanisms, prompt injection attempts to subvert the agent's core function, or obfuscation. The `clawhub install` command is a standard platform instruction. All content appears to be legitimate for an insurance automation skill.
能力评估
Purpose & Capability
The README/SKILL claim full lifecycle automation (parallel carrier interactions, voice + digital FNOL, verifying active coverage) but there are no integration adapters, required binaries, or declared credentials. Either this is a conceptual playbook (harmless) or it expects the agent to access external systems without specifying how — a mismatch worth flagging.
Instruction Scope
The SKILL.md instructs agents to extract and process sensitive policyholder details, verify coverage, check fraud indicators, and perform regulatory actions. Those instructions could cause an agent to access or request PII and system data, but the skill gives no constraints, data handling rules, or endpoints — increasing the risk of unintended data access or exfiltration when combined with connectors.
Install Mechanism
No install spec and no code files are included; the skill is instruction-only, so it writes nothing to disk and does not pull external binaries. This minimizes supply-chain risk.
Credentials
The skill requests no environment variables or credentials despite describing carrier interactions, policy database checks, and parallel external calls. Real deployments would require API keys, database access, telematics feeds, or telephony integrations; the absence of declared secrets is an inconsistency that could lead to ad-hoc credential requests by agents or operators.
Persistence & Privilege
always:false and no install means the skill does not demand permanent presence or elevated platform privileges. It does not request changes to other skills or system-wide settings.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install afrexai-insurance-automation
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /afrexai-insurance-automation 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release — comprehensive insurance operations automation framework for AI agents. - Covers full insurance lifecycle: underwriting, claims, policy admin, renewals, compliance, and broker ops. - Provides risk scoring, claims triage, broker workflow, compliance checklists, and insurance metrics benchmarks. - Includes automation priority matrix with ROI, cost framework, and deployment roadmap. - Features insurance-specific agent prompts for underwriting and claims triage. - Supplies key tools and resources for insurance agent configuration and ROI calculation.
元数据
Slug afrexai-insurance-automation
版本 1.0.0
许可证
累计安装 2
当前安装数 2
历史版本数 1
常见问题

Insurance Operations Automation 是什么?

Automates insurance workflows including underwriting, claims triage, policy management, renewals, compliance, and broker operations with industry benchmarks. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 598 次。

如何安装 Insurance Operations Automation?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install afrexai-insurance-automation」即可一键安装,无需额外配置。

Insurance Operations Automation 是免费的吗?

是的,Insurance Operations Automation 完全免费(开源免费),可自由下载、安装和使用。

Insurance Operations Automation 支持哪些平台?

Insurance Operations Automation 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Insurance Operations Automation?

由 1kalin(@1kalin)开发并维护,当前版本 v1.0.0。

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