Course Outline
LangGraph Fundamentals for Finance
- Overview of LangGraph architecture and stateful execution for government financial systems.
- Finance use cases: research support, trade facilitation, and customer service enhancements.
- Regulatory constraints and auditability considerations for government operations.
Financial Data Standards and Ontologies
- Basics of ISO 20022, FpML, and FIX for government financial data management.
- Mapping schemas and ontologies into graph state for enhanced data integration.
- Data quality, lineage, and PII handling to ensure compliance with government regulations.
Workflow Orchestration for Financial Processes
- KYC and AML onboarding workflows for streamlined government financial processes.
- Trade lifecycle management, exception handling, and case management for government transactions.
- Credit adjudication and decisioning paths to support government financial services.
Compliance, Risk, and Controls
- Policy enforcement and model risk management for government financial systems.
- Guardrails, approvals, and human-in-the-loop steps to ensure regulatory compliance.
- Audit trails, retention policies, and explainability for transparent government operations.
Integration and Deployment
- Connecting to core systems, data lakes, and APIs for seamless government financial integration.
- Containerization, secrets management, and environment configuration for secure deployment.
- CI/CD pipelines, staged rollouts, and canary releases to support continuous improvement in government operations.
Observability and Performance
- Structured logs, metrics, traces, and cost monitoring for effective oversight of government financial systems.
- Load testing, SLOs, and error budgets to ensure high performance and reliability.
- Incident response, rollback procedures, and resilience patterns to maintain system stability in government operations.
Quality, Evaluation, and Safety
- Unit tests, scenario evaluations, and automated evaluation harnesses for robust quality assurance in government financial systems.
- Red teaming, adversarial prompts, and safety checks to enhance security and reliability.
- Dataset curation, drift monitoring, and continuous improvement practices to maintain high standards for government operations.
Summary and Next Steps
Requirements
- An understanding of Python and Large Language Model (LLM) application development for government
- Experience with APIs, containers, or cloud services
- Basic familiarity with financial domains or data models
Audience
- Domain technologists for government
- Solution architects for government
- Consultants building LLM agents in regulated industries for government
Testimonials (2)
it has opened my mind to new tool that can help me in creating automation
Alessandra Parpajola - Advanced Bionics AG
Course - Machine Learning & AI for Finance Professionals
I very much appreciated the way the trainer presented everything. I understood everything even if Finance is not my area, he made sure that every participant was on the same page, while keeping up with the time left. The exercises were placed at good intervals. Communication with the participants was always there. The material was perfect, not too much, not too little. He elaborated very well on a bit more complicated subjects so that it can be understood by everyone.
