Presentation Outline

Slide 1: Title

1. Title: Privacy-First AI: Deploying Local LLMs on Windows for Secure, Scalable, and Cost-Efficient Data Workflows and Processes
2. Subtitle: Achieve Data Privacy, Cost Savings, and Enhanced Productivity
3. Presenter: CA Ankush Jain, FCA | CISA (US) | FAFD | DISA | CCAB(ICAI) | B.COM.(H) | CEH(US) | ISO 27001 LA | PCI DSS Imp (TUV) | Full Stack Software Developer |
4. Slide 2: Disclaimer
5. This presentation is for informational purposes only and is based on personal research and experience. It is not intended to promote or demotivate any technology or application.
6. The implementation of any technology solutions should be evaluated against your organization's specific requirements, security policies, and regulatory obligations.
7. Application of these use cases should be done in a safe environment and with proper guidance and research only.

Slide 3: The Challenge

1. Financial institutions face a critical dilemma:
2. Need to leverage AI capabilities for competitive advantage
3. Must protect highly sensitive financial data
4. Regulatory compliance requirements (DPDP, GDPR, etc.)
5. Data sovereignty concerns
6. Unpredictable cloud API pricing

Slide 4: The Solution - Local LLM Deployment

Security Architecture Deploying Local LLM

The security architecture ensures complete isolation of AI components while enabling secure integration with existing financial systems whether smallor large.

1. Complete data sovereignty: Data never leaves your organization
2. Enhanced security: No dependency on third-party security measures
3. Simplified compliance: Meet regulatory requirements more easily
4. Cost predictability: Eliminate per-token charges and API call costs
5. Full control: Customize models for financial industry needs

Slide 5: Demonstration Overview & Tech Stack Used for Implementation of Security Architecture and Deploying Local LLM

1. Ollama: Command-line local LLM deployment with API support
2. LM Studio: User-friendly interface for model management and testing
3. N8N Workflows:
4. Automated PDF processing and vector storage
5. Secure chat interface with locally stored data
6. Data redaction for sensitive financial information

1. Ollama LM Studio N8N Vector Databases Python Scripts, Pvt. Cloud Server
2. Local LLM Model used :-

• Granite3.3:2b (Local System) • Plutus-3B (Local System)

• Gemma-3-4b (Local System) • GPT-4o-mini (Pvt Cloud)

Slide 6: Ollama - Local LLM Runtime

1. Key Features:
2. Open-source Application
3. Download Link :-  https://ollama.com/download/windows
4. REST API with OpenAI-compatible interface
5. Simple installation and model management
6. Command-line and API access

1. Ollama – Demonstration for performing OCR on image
2. Command-line demonstration:
3. Model list and availability

Ollama ls

1. Download LLM model locally on system :-

Ollama pull granite3.2-vision

                            Run the application and Extract text and save it ocr.txt file using multi modal L

ollama run granite3.2-vision "Extract all text, focusing on extraction of full content D:\\Folder \\incometaxnotice.jpg" > ocr.txt

1. Benefits for financial applications :- 
2. Offline anytime anywhere
3. Perform OCR and Write letters secretly without any data leakage.

Slide 7: LM Studio - User-Friendly Interface

1. Key Features:
2. Open-source Application
3. Download Link :-  https://lmstudio.ai/
4. Graphical interface for model management

1. Built-in parameter tuning
2. Document handling (PDF, DOCX, CSV)
3. RAG capabilities for financial documents
4. LM Studio - Demonstration
5. User interface demonstration:
6. Model discovery and selection
7. Parameter configuration
8. Chat testing interface
9. Document processing capabilities
10. CPU-optimized models for hardware flexibility

Slide 8: N8N - Workflow Automation

1. Key Features:
2. Visual workflow builder
3. 400+ pre-built integrations
4. Self-hosted environment
5. Connects LLMs with business processes
6. Audit logging and security controls
7. Workflow #1 - Automated Document Processing
8. Process Flow:
9. Monitor folder for new financial documents
10. Extract text from PDFs
11. Split content into manageable chunks
12. Generate embeddings using local LLM
13. Store in PostgreSQL Vector Database

Trigger notification upon completion

1. Live demonstration:
2. Saving a financial PDF to monitored folder
3. Automatic processing and chunking
4. Vector storage process
5. Database examination
6. Setup and configuration overview
7. Workflow #2 - Secure Chat Interface
8. Process Flow:
9. User submits question about financial documents
10. System performs semantic search in vector database as created in Workflow 1
11. Retrieves relevant document sections
12. Local LLM generates response using document context
13. All processing remains on-premises
14. Workflow #2 – Demonstration

1. Live demonstration:
2. Chat interface for querying financial documents
3. Question processing workflow
4. Response generation using local LLM
5. Performance and accuracy comparison
6. Security advantages over cloud-based solutions

Slide 9: Workflow #3 - Data Redaction

1. Process Flow:
2. Extract text from financial documents
3. Identify sensitive information using regex patterns:
4. PAN numbers
5. TAN numbers
6. Aadhaar numbers
7. Bank account details
8. Apply redaction to masked patterns
9. Output redacted JSON for secure sharing
10. Workflow #3 – Demonstration

1. Live demonstration:
2. Processing document with sensitive information
3. Pattern matching for financial identifiers
4. Redaction process and output
5. Verification of data security
6. Use cases for redacted data

Slide 11: Hardware Requirements

Slide 12: Benefits for Financial Institutions

1. Data Privacy: Never expose sensitive financial data to third parties
2. Regulatory Compliance: Meet DPDPA, GDPR, GLBA, and other requirements
3. Cost Savings: Eliminate unpredictable API costs
4. Reduced Latency: Local processing for faster response times
5. Customization: Tailor models to specific financial workflows
6. Operational Control: Full visibility into all AI operations

Slide 13: Use Cases for Local LLMs in Finance

1. Document Analysis: Process statements, contracts, filings
2. Financial Advisory: Client portfolio analysis and recommendations
3. Compliance Monitoring: Regulatory checks and documentation
4. Risk Assessment: Credit analysis and fraud detection
5. Market Analysis: Processing financial news and trends
6. Customer Service: Secure chatbots for financial inquiries
7. Healthcare: Process medical records with privacy requirements
8. Legal: Contract analysis and document review
9. Insurance: Claims processing and policy analysis
10. Government: Sensitive document processing
11. Education: Student data analysis with privacy compliance

Slide 14: Conclusion

1. Local LLMs provide the security benefits of on-premises deployment while maintaining cutting-edge AI capabilities
2. Complete data sovereignty with no compromise on functionality
3. Cost-effective solution for financial institutions of all sizes
4. Start with specific high-value use cases and scale gradually