fraud-risk-assessment

fraud-risk-assessment is a skill for Claude Code, Codex from guoliang1114-boop/AriaAI. It costs 39 tokens per session (2,440 once invoked), scanned A, original, MIT.

A framework for finding and assessing fraud risks inside a business. It uses the fraud triangle—pressure, opportunity, and justification—and groups common fraud such as theft, bribery, and false financial reporting.

In plain words
What is it for?
Use it for annual fraud reviews, pre-acquisition checks, whistleblower investigations, financial-data concerns, and designing anti-fraud controls.
Why use it?
It helps organizations spot warning signs and weaknesses in internal controls before losses grow. It also supports focused reviews when reports or unusual activity raise concerns.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it for annual fraud reviews, pre-acquisition checks, whistleblower investigations, financial-data concerns, and designing anti-fraud controls.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/guoliang1114-boop/ariaai/fraud-risk-assessment
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add guoliang1114-boop/AriaAI --skill fraud-risk-assessment
Clone the repo
git clone --depth 1 https://github.com/guoliang1114-boop/AriaAI

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for fraud-risk-assessment

README.md
[![agentmods](https://agentmods.dev/badge/skills/guoliang1114-boop/ariaai/fraud-risk-assessment/github.svg)](https://agentmods.dev/skills/guoliang1114-boop/ariaai/fraud-risk-assessment)
Your own site
<a href="https://agentmods.dev/skills/guoliang1114-boop/ariaai/fraud-risk-assessment"><img src="https://agentmods.dev/badge/skills/guoliang1114-boop/ariaai/fraud-risk-assessment/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for fraud-risk-assessment

Your own site · 80×15
<a href="https://agentmods.dev/skills/guoliang1114-boop/ariaai/fraud-risk-assessment"><img src="https://agentmods.dev/badge/skills/guoliang1114-boop/ariaai/fraud-risk-assessment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 39 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,440 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00039 $0.02440
Opus 5 $0.00019 $0.01220
Sonnet 5 $0.00008 $0.00488
Haiku 4.5 $0.00004 $0.00244

Measured 12d ago against content hash ea1274619e6e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

fraud-risk-assessment scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/fraud-risk-assessment/SKILL.md · 250 lines

How it starts

The opening of the file, as written. The whole thing — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.

舞弊风险评估

When To Use

  • 企业需要建立或完善反舞弊管理体系
  • 并购前对目标公司的舞弊风险排查
  • 内部举报或异常信号触发的专项评估
  • 年度舞弊风险评估(COSO/SOX合规要求)
  • 管理层或股东对财务数据真实性的质疑

Tools

  • ACFE舞弊树(Fraud Tree)分类工具
  • 舞弊风险评估问卷
  • 红旗信号检查清单
  • 数据分析工具(Benford定律、异常交易检测)
  • 内控缺陷评估矩阵

Framework

Fraud Triangle(舞弊三角理论)

由Donald Cressey提出,舞弊行为的发生需要三个条件同时存在:

  1. Pressure(压力/动机)

    • 财务压力:个人债务、生活方式超出收入
    • 经营业绩压力:业绩目标、股价压力、融资条件
    • 行业压力:竞争激烈、利润下滑
    • 制度压力:监管处罚、合同违约
  2. Opportunity(机会)

    • 内控缺陷:职责分离不足、审批流程缺失
    • 监督薄弱:管理层凌驾、审计缺位
    • 复杂交易:关联交易、跨境交易、特殊目的实体
    • 信息不对称:管理层掌握信息远超股东/审计师
  3. Rationalization(合理化)

    • "我是在借,不是在偷"
    • "公司欠我的"
    • "大家都这么做"
    • "只是暂时的,等有钱了就还"

ACFE Fraud Tree — 舞弊分类

Category 1: Asset Misappropriation(资产侵占)

  • Cash Misappropriation
    • Skimming(收入截留)
    • Larceny(盗窃现金)
    • Fraudulent Disbursements(虚假支付)
      • Billing Schemes(虚假供应商)
      • Payroll Schemes(虚假员工)
      • Expense Reimbursement(虚假报销)
      • Check Tampering(支票篡改)
  • Non-Cash Misappropriation
    • Inventory/Asset Theft
    • Misuse of Assets

Category 2: Corruption(腐败)

  • Conflicts of Interest(利益冲突)
  • Bribery(行贿/受贿)
  • Illegal Gratuities(非法馈赠)
  • Economic Extortion(经济勒索)

Category 3: Financial Statement Fraud(财务报表舞弊)

  • Fictitious Revenues(虚假收入)
  • Timing Differences(提前/推迟确认)
  • Concealed Liabilities(隐瞒负债)
  • Improper Disclosures(不当披露)
  • Improper Asset Valuation(不当资产估值)

Red Flags(红旗信号)

个人层面

  • 生活方式明显超出收入水平
  • 不愿休假或交接工作
  • 与供应商/客户关系异常密切
  • 财务困难的迹象
  • 控制欲强,拒绝他人介入

组织层面

  • 管理层频繁凌驾于内控之上
  • 关联交易异常频繁
  • 会计估计频繁大幅调整
  • 审计师更换频繁
  • 组织架构过于复杂

交易层面

  • 临近期末的大额异常交易
  • 无商业实质的交易
  • 付款对象为个人账户或新设公司
  • 缺少完整单据的交易
  • 与业绩目标高度吻合的交易

Workflow

1. 舞弊风险识别
   ├─ 行业特定风险(参考ACFE行业报告)
   ├─ 组织层面风险(治理、文化、内控)
   ├─ 业务流程风险(收入、采购、资产、财务报告)
   ├─ 账户/交易层面风险
   └─ 历史舞弊事件回顾

2. 风险评估
   ├─ 可能性评估(1-5)
   ├─ 影响程度评估(1-5)
   ├─ 风险等级矩阵
   ├─ 现有控制措施评估
   └─ 剩余风险评估

3. 红旗信号分析
   ├─ 个人行为红旗
   ├─ 组织行为红旗
   ├─ 交易行为红旗
   ├─ 数据分析异常
   └─ 举报线索评估

4. 控制评估
   ├─ 预防性控制(职责分离、审批、授权)
   ├─ 检测性控制(审计、对账、分析)
   ├─ 举报机制(举报热线、保护政策)
   └─ 控制缺陷识别

5. 应对建议
   ├─ 高风险领域专项治理
   ├─ 控制措施加强
   ├─ 监控机制优化
   └─ 文化建设建议

Read the full file on GitHub · 250 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 250 lines · 39 tokens per session scan A ea1274619e6e

Subscribe to this mod's changes

fraud-risk-assessment is a skill published in the GitHub repository guoliang1114-boop/AriaAI (37 stars, last pushed today), licensed MIT. It adds 39 tokens to every session and 2,440 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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