Project Background
A joint-stock bank processing over 100,000 credit applications annually relied on manual review of borrower documents, credit reports, and financial data. Each approval took 3 days, and the risk miss rate was 3%. Regulatory compliance documents were voluminous and frequently updated, making it difficult for the compliance team to analyze them efficiently. The bank had strict data security requirements that prohibited any business data from leaving its premises.
Core Pain Points
Solution
Private Large Model Deployment
Deploy the Qwen2.5-72B large model on a local GPU cluster (8×A100) in the bank, using the vLLM inference framework to optimize throughput. All model inference and data flow are completed within the bank's intranet, with zero data leakage, fully meeting CBIRC data security regulatory requirements.
Intelligent Credit Risk Review
Build an intelligent credit risk review assistant based on the large model to automatically parse borrower documents, credit reports, and financial statements, cross-verify information consistency, identify potential risks, and generate review reports.
Intelligent Compliance Document Analysis
Develop an intelligent compliance document analysis system that supports automatic interpretation of regulatory documents, internal policy compliance checks, and impact assessment of policy changes.
Effect Data
| Indicator | Before Optimization | After Optimization | Improvement |
|---|---|---|---|
| Credit Approval Time | 3 days | 4 hours | ↓83% |
| Risk Miss Rate | 3% | 0.5% | ↓83% |
| Compliance Document Analysis Time | 2 days/document | 2 hours/document | ↓88% |
| Data Leakage Risk | Third-party dependency | Zero leakage | ↓100% |
> Quantitative summary: Credit approval efficiency improved 83%, risk miss rate reduced 83%, compliance analysis efficiency improved 88%, zero data leakage fully meets CBIRC regulatory requirements.
Tech Stack
Qwen2.5-72B, vLLM inference framework, NVIDIA A100 GPU cluster, LangChain, Python, Kubernetes, bank intranet isolated deployment
FAQ
How many GPU resources are needed for private LLM deployment in a bank?
Typically 2–8 A100/H800 class GPUs. This project used 8×A100 for Qwen2.5-72B. A 7B-level model can run inference on a single card; for a 70B model, a minimum of 4 cards is recommended. All inference runs within the bank's intranet, with zero data leakage.
How much can approval efficiency improve after private deployment?
Approval time shortened from 3 days to 4 hours (83% improvement), risk miss rate dropped from 3% to 0.5% (83% reduction), and compliance document analysis time reduced from 2 days/document to 2 hours/document.
Does private LLM deployment meet CBIRC regulatory requirements?
Yes. All model inference and data flow take place within the bank's intranet, with no third-party data dependency, fully meeting CBIRC's requirement that data stays within the domain.
