Course Prerequisites:
Basic understanding of financial workflows or finance-related domains (e.g., banking, investment, risk management, compliance) Interest in automation, AI, or financial innovation No prior coding experience required (coding templates provided for hands-on labs) Familiarity with digital tools (e.g., spreadsheets, dashboards, analytics platforms) is helpful but not mandatory Curiosity about AI technologies such as machine learning, natural language processing, and their impact on the finance industry.
Modules:
Module 1: Introduction to AI Agents in Finance
1.1 Basic Understanding AI Agents in Finance vs Traditional Financial Automation
1.2 The Evolution of AI Agents in Financial Services
1.3 Overview of Different Types of AI Agents in Finance
1.4 Importance of Agent Autonomy and Task Delegation in Financial Settings
1.5 Key Differences Between AI Agents in Finance and Traditional Automation
1.6 Hands-On Activity: Exploring AI Agents in Finance (This hands-on activity will introduce participants to
Module 2: Building and Understanding AI Agents in Finance
2.1 Architecture of AI agents in finance
2.2 Tools and libraries for agent development
2.3 AI agents vs. static models
2.4 Overview of agent lifecycle
2.5 Real-World Use Case: Customer support agents in banks for handling KYC, FAQs, and transaction
2.6 Case Study: Bank of America’s Erica, a virtual financial assistant that handles 1+ billion interactions using
2.7 Hands on Activity: Building and Understanding AI Agents in Finance
Module 3: Intelligent Agents for Fraud Detection and Anomaly Monitoring
3.1 Supervised/unsupervised ML for fraud detection
3.2 Pattern analysis & behavioral profiling
3.3 Real-time monitoring agents
3.4 Real-World Use Case: AI agents monitoring transaction behavior and flagging anomalies for real-time
3.5 Case Study: PayPal's AI system uses graph-based anomaly detection agents to flag 0.33% of all
3.6 Hands-On Activity: Intelligent Agents for Fraud Detection and Anomaly Monitoring
Module 4: AI Agents for Credit Scoring and Lending Automation
4.1 Feature generation from non-traditional credit data
4.2 Explainability (XAI) in credit decisions
4.3 Bias mitigation in lending agents
4.4 Real-World Use Case: Agents assessing new-to-credit individuals using transaction and mobile data.
4.5 Case Study: Upstart’s AI-based lending platform approved by CFPB showed 27% increase in approval
4.6 Hands-On Activity: AI Agents for Credit Scoring and Lending Automation
Module 5: AI Agents for Wealth Management and Robo-Advisory
5.1 Personalization using profiling agents
5.2 Portfolio rebalancing algorithms
5.3 Sentiment-aware investing
5.4 Real-World Use Case: AI agent adjusting user portfolio allocations weekly based on financial goals and
5.5 Case Study: Wealthfront’s Path agent uses financial behavior modeling to recommend personalized
5.6 Hands-On Activity: AI Agents for Wealth Management and Robo-Advisory
Module 6: Trading Bots and Market-Monitoring Agents
6.1 Reinforcement learning in trading agents
6.2 Predictive modeling using historical data
6.3 Risk-reward threshold management
6.4 Real-World Use Case: AI trading agents performing arbitrage between crypto exchanges.
6.5 Case Study: Renaissance Technologies utilizes AI to automate short-hold trades, generating consistent
6.6 Hands-On Activity: AI Trading Bots and Market-Monitoring Agents
Module 7: NLP Agents for Financial Document Intelligence
7.1 LLMs in earnings call and filings analysis
7.2 AI summarization and event detection
7.3 Voice-to-text and key-point extraction
7.4 Real-World Use Case: An NLP agent that parses quarterly earnings calls and flags forward-looking
7.5 Case Study: BloombergGPT processes and tags over 30,000 financial documents a day for market-
7.6 Hands-On Activity: AI Agents for Wealth Management and Robo-Advisory
Module 8: Compliance and Risk Surveillance Agents
8.1 AI for AML and KYB (Know Your Business)
8.2 Regulation-aware rule modelling
8.3 Transaction graph analysis
8.4 Real-World Use Case: Agent tracking suspicious cross-border money transfers in real-time across
8.5 Case Study: HSBC uses Quantexa’s AI agents to trace AML networks, increasing suspicious activity
8.6 Hands-On Activity: Compliance and Risk Surveillance Agents in Financial Systems
Module 9: Responsible, Fair & Auditable AI Agents
9.1 Governance frameworks for AI in finance (RBI, EU AI Act)
9.2 Transparency and auditability in decision logic
9.3 Fairness and explainability
9.4 Real-World Use Case: Auditable AI agent logs used during internal policy audits to ensure fair lending
9.5 Case Study: Wells Fargo implemented internal AI fairness reviews for lending bots post regulatory
9.6 Hands-On Activity: Responsible, Fair & Auditable AI Agents in Finance
Module 10: World Famous Case Studies
10.1 Case Study 1: JPMorgan’s COiN Platform
10.2 Case Study 2: AI in Fraud Detection - PayPal’s Decision Intelligence
10.3 Case Study: AI-Driven Credit Scoring - Upstart’s Lending Platform