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Financial Report Fraud Detection AI Agent Delivers Risk Control Intelligence, Solo Entrepreneur Earns 22,000 RMB/Month

Workflow: At 1:00 AM daily, regular reports, temporary announcements, and exchange inquiry letters disclosed by all A-share listed

AGENT

Key Fields

FIELD STAMPS

IndustryFintech

RegionChina（中国大陆）

ScaleSME

ChannelOnline

🔧 Workflow

At 1:00 AM daily, regular reports, temporary announcements, and exchange inquiry letters disclosed by all A-share listed companies on the previous day are automatically crawled via the official CNINF interface, while simultaneously pulling the past 5 years of historical financial data of the corresponding enterprises as a baseline. After inputting the raw financial report text, audit report opinions, and fluctuation indicators, a Large Language Model cross-validates them with a preset rule base of 200+ fraud/risk characteristics, outputting a draft risk control brief containing a fraud probability score, high-risk signal breakdown, and peer comparison. After a human reviewer conducts a secondary verification of high-risk items, the brief is targetedly pushed to subscribed clients via encrypted email or private communities, and simultaneously archived into the knowledge base for subsequent iteration.

🛠 Setup Requirements

Building this requires basic Python syntax and n8n drag-and-drop workflow orchestration capabilities, familiarity with CNINF data interface calling specifications, and proficiency in writing Claude prompts for structured financial report text extraction. Initial investment takes about 10 working days to complete the development of the backend data crawling module, anomaly indicator scoring logic, and brief templates. The core difficulty lies in accumulating feature annotations of actual A-share fraud cases from the past 3 years, fine-tuning prompts to increase anomaly recognition accuracy to over 92%, and simultaneously building a data cleaning pipeline to reduce financial table parsing error rates.

🧰 Toolchain

🔧 CNINF Official API
🔧 Claude API
🔧 n8n Workflow Engine
🔧 OCR Parsing Tool
🔧 Python Pandas Data Processing Library

💰 Revenue

Adopting a quarterly subscription model, charging 12,000 RMB per quarter for a single institutional client, alongside offering single customized financial report risk-screening reports priced at 3,000 RMB. Steadily serving 6-8 private equity funds, retail stock moguls, and high-net-worth individual investors, supplemented by occasional customized report orders, a solo operator's monthly income stabilizes at around 22,000 RMB, which can rise to 35,000 RMB during financial reporting seasons (January, April, August, October).

💸 Cost

Fixed monthly costs include Claude API call fees of about 800 RMB, cloud server storage and computing power fees of about 400 RMB, with no other high expenses. The overall net profit margin remains above 85%, with API costs temporarily rising to under 1,500 RMB only during reporting seasons due to increased call volumes.

⏱ Time Investment

Daily maintenance requires only 2 hours, concentrated in the early morning to complete data crawling, brief generation, and distribution work; during peak reporting seasons, about 15 hours per week are invested in anomaly signal re-verification, responding to client customization demands, and rule base iterative optimization.

🚀 Getting Started

For the first step, beginners can download the open-source financial_research_report project on GitHub to run through the A-share financial data cleaning and basic indicator extraction process; then select a familiar niche industry (such as pharmaceuticals or new energy), manually annotate the financial characteristics of all fraud cases in that industry over the past 2 years, write exclusive prompts to generate free sample reports, and send them to 3-5 acquaintances in private equity or financial bloggers for trial reading, converting them into paying customers after iterating based on feedback.

🔑 Keys to Success

✅ Accumulate an exclusive feature rule base of industry-wide A-share fraud cases over the past 3 years, covering 12 high-risk indicators such as goodwill, deposits/loans, and related-party transactions
✅ Target high-goodwill, high-pledge, and other error-prone niche tracks to build a vertical professional risk-screening reputation barrier
✅ Continuously optimize financial table parsing and text extraction accuracy to over 95% to reduce manual review costs
✅ Build a private domain operation system for private equity investors, enhancing customer stickiness and repurchase rates through regular free industry risk alerts

⚠️ 风险

⚠️ If explicit disclaimers are not added to the briefs and data verification is not timely, major financial signal omission leading to client investment losses may result in reputational lawsuits and client claims for compensation
⚠️ Official data sources from CNINF occasionally experience disclosure delays, which may lead to insufficient timeliness of briefs and loss of paid value for high-end clients
⚠️ Free financial analysis tools launched by brokerage research institutes, Tonghuashun iFinD, and other institutions will squeeze the market space for personal paid products
⚠️ Financial fraud methods are constantly iterating; if the rule base is not updated in a timely manner, it will lead to a decline in anomaly signal recognition accuracy and customer churn

📌 Real Cases

📌 The open-source financial_research_report project by an independent developer received over 600 GitHub stars, verifying technical feasibility. A front-end developer in Beijing secondary-developed a pharmaceutical and biological industry financial risk warning brief based on this project, securing subscriptions from 5 private equity institutions within 3 months and achieving a monthly income of 22,000 RMB
📌 A financial self-media team in Guangzhou used n8n + Claude to build an automated financial screening system focusing on the new energy industry risk warning, currently stably serving 12 high-net-worth individual investors with monthly subscription revenue reaching 36,000 RMB
📌 A quantitative private equity researcher in Shanghai developed a SME board goodwill impairment risk brief in their spare time, precisely distributed through private equity communities, adding 7 new institutional subscription clients in a single month and pushing cumulative stable monthly income to break 40,000 RMB

Sources