The Enterprise Financial Risk Intelligence Platform is an advanced Retrieval-Augmented Generation (RAG) system designed to analyze SEC filings and generate AI-powered financial intelligence.
Unlike traditional chatbot projects, this platform combines:
- Hybrid Search
- Vector Databases
- Cross-Encoder Reranking
- Financial LLM Analysis
- Source Attribution
- Automated PDF Reporting
to provide enterprise-grade financial risk insights.
The system enables analysts, researchers, investors, and business professionals to quickly identify risks, extract key findings, compare companies, and generate professional reports directly from financial documents.
Financial analysts spend hundreds of hours reviewing:
- Annual Reports (10-K)
- Quarterly Reports (10-Q)
- Risk Disclosures
- Management Discussion & Analysis (MD&A)
- Regulatory Filings
Finding critical information manually is:
- time-consuming,
- expensive,
- difficult to scale.
This platform automates financial document analysis by retrieving the most relevant sections and generating structured risk intelligence reports powered by AI.
Generate:
- Executive Summaries
- Key Findings
- Risk Assessments
- Company Comparisons
- Financial Intelligence Reports
Combines:
- BGE Embeddings
- Semantic Search
- Qdrant Vector Database
- BM25 Search
- Keyword Matching
- Financial Term Retrieval
This improves recall and retrieval quality.
Uses:
Benefits:
- higher retrieval precision,
- better context quality,
- fewer hallucinations,
- more relevant financial insights.
Compare companies such as:
- Apple
- Microsoft
- NVIDIA
- Tesla
Generate:
- Comparative Risk Profiles
- Strategic Differences
- Financial Intelligence Summaries
Every insight is grounded in retrieved SEC filing content.
Sources include:
- Document Name
- Chunk ID
- Company Name
This improves transparency and explainability.
Built using Streamlit.
Features:
- Financial Question Answering
- Company Comparison Analysis
- Source Tracking
- Retrieval Metrics
- PDF Report Generation
Generate professional reports containing:
- Executive Summary
- Key Findings
- Risk Assessment
- Sources Used
Suitable for:
- presentations,
- portfolio demonstrations,
- analyst reporting.
User Question
│
▼
Company Detection
│
▼
┌───────────────────────────────────┐
│ Hybrid Retrieval │
│ │
│ Vector Search (Qdrant) │
│ + │
│ BM25 Search │
└───────────────────────────────────┘
│
▼
Candidate Financial Chunks
│
▼
Cross Encoder Reranker
│
▼
Top Relevant Chunks
│
▼
Financial LLM Analysis
│
▼
Risk Intelligence Report
│
▼
PDF Report Generation
Question
│
▼
Vector Search (Qdrant)
+
BM25 Search
│
▼
Candidate Chunks
│
▼
Cross Encoder Reranking
│
▼
Top Ranked Chunks
│
▼
Financial LLM
│
▼
Final Financial Analysis
User submits a financial question.
Example:
What are Apple's supply chain risks?
Hybrid retrieval fetches relevant chunks from SEC filings.
Retrieval sources:
- Vector Search
- BM25 Search
Cross Encoder reranker scores all candidate chunks.
Most relevant chunks are selected.
Selected chunks are sent to the Financial LLM.
LLM generates:
- Executive Summary
- Key Findings
- Risk Assessment
- Conclusion
The system can answer questions such as:
What are Apple's supply chain risks?
How is Microsoft using artificial intelligence?
What is NVIDIA's data center strategy?
Compare Apple and Tesla supply chain risks.
Compare NVIDIA and Microsoft AI strategies.
Provides:
- Financial Question Answering
- Risk Intelligence Reports
- Multi-Company Comparisons
Displays:
- Sources Retrieved
- Documents Used
- Chunks Analyzed
Users can inspect:
- Company
- Document
- Chunk ID
for every retrieved source.
One-click generation of:
- Financial Reports
- Risk Assessment Reports
- Company Comparison Reports
Apple's supply chain faces multiple interrelated risks, including geopolitical instability, supplier concentration, trade restrictions, cybersecurity incidents, and natural disasters that could impact manufacturing and delivery.
- Manufacturing is concentrated in a limited number of countries.
- Supply chain operations depend heavily on outsourcing partners.
- Cybersecurity incidents may disrupt logistics and operations.
- Trade restrictions can increase operational costs.
- Geopolitical tensions may disrupt manufacturing.
- Natural disasters could impact production facilities.
- Supplier financial instability may affect component availability.
- Cyber incidents can interrupt supply chain operations.
| Category | Technology |
|---|---|
| Programming Language | Python |
| Frontend | Streamlit |
| Vector Database | Qdrant |
| Embedding Model | BAAI BGE Small |
| Reranker | Cross Encoder |
| LLM Provider | OpenRouter |
| Retrieval | Hybrid Search |
| Sparse Search | BM25 |
| Data Processing | Pandas |
| PDF Reporting | ReportLab |
financial-risk-intelligence/
│
├── assets/
│
├── src/
│ │
│ └── archive/
│ ├── embeddings/
│ ├── pipeline/
│ ├── rag/
│ ├── reporting/
│ ├── search/
│ ├── ui/
│ └── vector_db/
│
├── data/
│
├── reports/
│
│
├── requirements.txt
│
│
├── LICENSE
│
├── .env
│
├── .gitignore
│
└── README.md
Implemented:
- Hybrid Search Retrieval
- Cross Encoder Reranking
- Context Filtering
- Source Attribution
- Company Detection
- Multi-Document Retrieval
Benefits:
- Higher retrieval precision
- Reduced hallucinations
- Better answer quality
- Improved explainability
This project demonstrates:
- Retrieval-Augmented Generation (RAG)
- Hybrid Search Architecture
- Vector Databases
- Semantic Search
- Cross Encoder Reranking
- Financial AI Applications
- Source Grounding
- Enterprise Reporting
- Interactive Dashboards
Potential enterprise extensions include:
- Earnings Call Audio Analysis
- Investor Presentation Analysis
- Financial Chart Understanding
- Automatic SEC Filing Tracking
- Risk Alerts
- Company Monitoring
- Company Relationship Mapping
- Risk Propagation Analysis
- Autonomous Research Workflows
- Multi-Step Financial Analysis
- Investment Research Automation
This closely resembles modern AI systems used in:
- Investment Research
- Financial Intelligence
- Risk Management
- Enterprise Knowledge Systems
- Financial Advisory Platforms