Creating an AI Agent for Financial Report Analysis

Resources (including link to code along notebook): https://bit.ly/41cgavS AI agents are transforming industries by automating complex processes and delivering insights at scale. In financial services, AI agents can streamline decision-making, reduce manual effort, and improve the accuracy of report analysis can informs various downstream tasks like market research, credit scoring, report generation, etc. Designing and building such agents requires a strong understanding of their architecture, the data they rely on, and how to use AI to automate repetitive tasks effectively. In this hands-on code-along session, Jayeeta Putatunda, a Lead Data Scientist & Director at Fitch Group, guides you through creating an AI agent tailored for financial report analysis. You’ll learn how to design and architect AI agents, explore their applications in finance, and identify the key data needed for these systems. The session will also cover how AI agents can automate repetitive tasks to enhance efficiency. This webinar is ideal for data scientists and machine learning scientists looking to build practical AI solutions for financial applications. 00:00 Introduction & Welcome 00:21 Why AI Agents for Financial Reporting? 01:44 Guest Introduction – Jayta from Fitch Group 03:27 Understanding AI Agents vs. Agentic AI 05:56 Identifying Valuable Use Cases for AI Agents 07:44 Key Components of an AI Agent 10:58 Choosing the Right AI Agent Approach 12:19 AI in Financial Services – Real-World Applications 13:55 Today's Use Case: Financial Report Analysis 16:05 Setting Up the AI Agent Workflow 18:34 Required Tools & API Setup (Grok & Agonal) 22:06 Agent 1: Web Search-Based Research Agent 26:14 Running the Research Agent – Example Queries 31:51 Agent 2: Retrieval-Augmented Generation (RAG) 35:16 Setting Up Vector Database for RAG 38:53 Loading & Processing Financial Documents 42:30 Running Queries Against the Knowledge Base 44:27 Agent 3: AI-Driven Stock Market Analysis 47:40 Running Market Comparison & Trend Analysis 50:56 Agent 4: Automated Evaluation Framework 54:40 Reviewing Evaluation Metrics & Results 57:10 Best Practices for AI Agent Development 59:50 Q&A – Choosing the Right Vector Database 01:02:26 Q&A – LangChain vs. Agonal for AI Agents 01:05:02 Q&A – How AI Agents Improve Financial Workflows 01:10:28 Closing Thoughts & Upcoming Sessions