Overview

Master the future of financial AI in this program. Perfect prompting strategies to build an automated risk analysis and compliance engine that thinks like a risk analyst. Scale agentic workflows to secure international transactions against fraud. Bridge data and decisions by building a multi-tool assistant that synthesizes SEC filings, SQL databases, and live market feeds. Architect multi-agent autonomous OTC trading systems. From compliance to algorithmic trading, gain the skills to deploy audit-ready AI agents for multiple Fintech applications.

Syllabus

Prompting for LLM Reasoning and Planning for Financial Services

This course equips you with essential skills to harness Large Language Models (LLMs) in the finance sector. It covers foundational concepts including role-based prompting, chain-of-thought, and ReACT prompting, complemented with practical Python implementations tailored for financial applications. You will explore techniques for refining prompt instructions and chaining prompts to enable agentic reasoning. The course also emphasizes the integration of feedback loops to enhance LLM performance. The final project is a comprehensive transactional risk analysis and compliance engine. By the end, you will be adept at deploying LLMs for strategic financial decision-making.

Agentic AI Workflows for Financial Services

Master the design and implementation of advanced agentic workflows, specifically for the financial sector. Using Python and Large Language Models (LLMs), you will build autonomous systems capable of processing SWIFT transactions, detecting fraud, and automating compliance. You will implement agentic workflow patterns including Evaluator-Optimizer, Parallelization, Prompt Chaining, Routing, and Orchestrator-Workers. The course culminates in the "SwiftGuard" project, where you will engineer a complete multi-agent ecosystem to secure international financial messaging against sophisticated threats.

Building AI Agents for Financial Services

This course equips you with the skills to design and implement AI agents tailored to the financial sector. Beginning with an introduction to AI agents, you will explore extending agents with tools, structured outputs, and state management. The course emphasizes practical programming in Python, covering agent memory, API integrations, and database interactions. Key concepts include short-term and long-term memory management, Agentic Retrieval Augmented Generation (RAG), and agent evaluation techniques. At the end of this course, you will apply your knowledge in a project, creating a comprehensive FinTool Analyst AI agent that synthesizes course principles.

Multi-Agent AI Systems for Financial Services

Master the architecture of Multi-Agent AI for finance. You will move beyond simple chatbots to build specialized agent teams that collaborate on high-stakes workflows like OTC trading. Using Python and PydanticAI, you will learn to implement orchestration, intelligent routing, and persistent state management to create immutable audit trails. You will master the critical balance of blending non-deterministic AI reasoning with deterministic compliance logic. In the course project, "Agentic Alpha," you will architect a fully functional, autonomous trading system that analyzes markets, calculates risk, and enforces regulatory rules in real-time.