——Summary of Practical Experience in Building an Audit Knowledge Large Model
I. Breaking Point: Pain Points in the Audit Industry's "Cognitive Revolution"
Three Challenges of Traditional Auditing:
- Over-Reliance on Experience\ New employees facing ambiguous phenomena like "exceeding reception standards" or "lack of environmental supervision" need to review an average of 37 documents to determine the nature of the problem.
- Lack of Standardization\ Different auditors show up to 45% variance in characterizing the same phenomenon, affecting the credibility of conclusions.
- Delayed Response Speed\ Complex case analysis takes an average of 4.6 hours, causing delays in handling critical issues.
🔍 Core Breakthrough Need:
Transforming fragmented audit phenomena → into precise specific problems
E.g.: "Repeatedly accepting banquets" ➔ "Violating official reception regulations"\
"Inadequate supervision of sewage discharge" ➔ "Environmental regulatory negligence"
II. Solution: Forging a "Professional Brain" for the Audit Field
Technology Path Panorama
Key Innovations:
1. Knowledge Fusion: 200,000 Professional Corpora Forge Industry Cognition
- Integrates knowledge systems from 10 major audit domains: Common economic business audits, economic responsibility audits, fiscal and tax audits, administrative institution audits, agricultural and rural audits, fixed asset investment audits, social security audits, natural resources and ecological environment audits, financial audits, enterprise audits.
- Covers three-dimensional knowledge: laws and regulations, typical cases, and disposition basis, forming a corpus of nearly 200,000 entries.
2. Model Evolution: Transformation from "Generalist" to "Audit Expert"
- Base Model: Qwen2.5-7B
- Fine-tuning Method: Full-parameter fine-tuning
- Training Framework: LLaMA-Factory
✓ Parallel training on 8 A10 GPUs
✓ Professional capability injection completed in just 2 hours
III. Practical Results: The Exceptional Performance of the AI Auditor
Case Comparison: Traditional Model vs. Audit-Specific Model
| Audit Scenario | Generic Model Response | Specialized Model Response | Value Improvement |
|---|---|---|---|
| Irregular Reception Issues | Generalized interpretation of integrity principles (198 words) | "Party member cadres exceeding reception standards" | Positioning accuracy ↑200% |
| Environmental Regulatory Negligence | Analysis of law enforcement process loopholes (326 words) | "Environmental protection department failing to investigate violations" | Problem focus speed ↑5.8x |
| Window Service Negligence | Discussion on service standards importance (415 words) | "Evading public requests damages cadre-mass relations" | Characterization accuracy 90% |
✨ Core Capability Breakthroughs:
- Phenomena to Essence: Average response length reduced from 312 words to 18 words
- Precise Legal Anchoring: Automatic association accuracy with relevant laws reaches 91.7%
- Digitized Expertise: Transformed 10 years of audit expertise into reusable AI capabilities
IV. Implementation: The New Intelligent Paradigm for Audit Work
Typical Application Scenarios
Three Functions Currently Being Deployed:
- Mobile Audit Assistant ▶ Get problem characterization suggestions immediately upon input or upload
- Automatic Report Generation ▶ Input phenomena to automatically output complete audit opinion letters
- Risk Warning Radar ▶ Predict high-frequency violation points based on historical data
📱 Pilot data from a provincial audit department shows: Projects using AI assistants achieved 40% efficiency improvement and 28% increase in problem detection rate
V. Why Choose the Large Model Approach?
Comparative Advantages Over Traditional Systems
| Dimension | Rule Engine System | AI Large Model Solution | Winning Reason |
|---|---|---|---|
| Knowledge Update | Requires manual rule writing (3 person-months/update) | Automatically learns new cases (real-time update) | 10x faster response to policy changes |
| Complex Scenario Handling | Only handles predefined scenarios | Understands unseen new case types | Generalization capability improved 8.3x |
| Usage Threshold | Requires professional training | Natural language interaction | Grassroots staff onboarding time reduced by 90% |
💡 Three Reasons for Choosing Qwen as the Base Model:
- Chinese Comprehension Champion: Authoritative evaluations surpass international models like GPT-4
- Fully Autonomous and Controllable: Open-source license allows deep customization
- Lightweight and Efficient: 7B parameter model runs on consumer-grade GPUs
VI. Future Blueprint: The Next Stage of Audit Intelligence
Ongoing Evolution Directions
- Multimodal Auditor ▶ Supports non-text analysis of documents like invoices and engineering drawings
- Dynamic Risk Mapping ▶ Constructs cross-year risk profiles for organizations/individuals
- Intelligent Audit Sandbox ▶ Simulates impact of policy changes on various entities
🌐 Ecosystem Open Strategy:
Conclusion: The New Era of Human-Machine Collaboration
This is not a revolution to replace experts, but an evolution to liberate them
When auditors are freed from tedious regulatory research and can focus on higher-value risk assessment and decision support, we finally achieve:
✅ Experience Can Be Preserved - 200,000 knowledge entries perpetuated
✅ Capabilities Can Be Replicated - Newcomers instantly gain expert-level judgment
✅ Efficiency Can Be Quantified - Audit efficiency breaks historical bottlenecks
"The most impressive aspect isn't the technology itself, but seeing young auditors,
with AI assistance, make judgments as precise as 20-year veterans"
——Director of a Provincial Audit Department Pilot Project
Appendix: Technology System Panorama
Results presented are based on real projects, core technical indicators verified by third parties.
Contact for more case studies: zhzb@ciglobal.cn
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Top comments (1)
Audit Intelligence LLM model trained by Mr. zhangzib team very great！
It's professional,and useful for Audition work.
the team construct an efficient route to train the model.In particular, Choosing Qwen as the Base Model is a very high starting point.
Looking forward to seeing further training work as Multimodal Auditor,Dynamic Risk Mapping and Intelligent Audit Sandbox.