Course Outline
LangGraph Fundamentals for Government Finance
- Review of LangGraph architecture and stateful execution processes.
- Finance use cases tailored for government: research assistance, trade support, and customer service automation.
- Regulatory constraints and auditability considerations specific to government operations.
Financial Data Standards and Ontologies for Government
- Basics of ISO 20022, FpML, and FIX standards.
- Mapping financial data schemas and ontologies into graph state for enhanced transparency and traceability.
- Data quality assurance, lineage tracking, and handling Personally Identifiable Information (PII) in compliance with government regulations.
Workflow Orchestration for Financial Processes in Government
- Know Your Customer (KYC) and Anti-Money Laundering (AML) onboarding workflows for government agencies.
- Management of trade lifecycle, exception handling, and case management processes within a government context.
- Credit adjudication and decision-making paths aligned with government financial policies.
Compliance, Risk, and Controls for Government Finance
- Enforcement of regulatory policies and management of model risks in government finance systems.
- Implementation of guardrails, approval processes, and human-in-the-loop steps to ensure compliance and accuracy.
- Maintenance of audit trails, data retention policies, and explainability mechanisms for transparency and accountability.
Integration and Deployment for Government Financial Systems
- Connecting government financial systems with core infrastructure, data lakes, and APIs to enhance interoperability.
- Utilizing containerization, secrets management, and environment control practices for secure deployment.
- Establishing Continuous Integration/Continuous Deployment (CI/CD) pipelines, staged rollouts, and canary releases to ensure smooth system updates.
Observability and Performance in Government Financial Systems
- Implementation of structured logging, metrics tracking, tracing mechanisms, and cost monitoring for enhanced visibility.
- Conducting load testing, setting Service Level Objectives (SLOs), and managing error budgets to ensure system reliability.
- Developing incident response plans, rollback procedures, and resilience strategies to maintain system integrity and performance.
Quality, Evaluation, and Safety for Government Financial Systems
- Development of unit tests, scenario testing, and automated evaluation frameworks to ensure high-quality outputs.
- Conducting red team exercises, adversarial prompts, and safety checks to identify and mitigate potential vulnerabilities.
- Curation of datasets, monitoring for data drift, and implementing continuous improvement processes to maintain system accuracy and reliability.
Summary and Next Steps for Government Finance Initiatives
Requirements
- An understanding of Python and the development of large language model (LLM) applications
- Experience with APIs, containers, or cloud services for government
- Basic familiarity with financial domains or data models
Audience
- Domain technologists within the public sector
- Solution architects in governmental agencies
- Consultants developing LLM agents for regulated industries, including government entities
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.
