AI+ Finance Agent™ eLearning

Empower organizations with AI+ Finance Agent™ to automate financial operations and improve decisions

• Core Concepts Covered: Learn AI fundamentals for finance, focusing on analytics, trading, risk, fraud, automation
  
• Capstone Application: Build practical AI finance agents supporting trading, risk evaluation, fraud monitoring, and forecasting
  
• Career Readiness: Gain expertise in AI-powered financial roles through mentorship, hands-on training, designing AI agents for finance innovation

Module 1: Introduction to AI Agents in Finance

• 1.1 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

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 Use Case: Customer Support Agents in Banks for Handling KYC, FAQs, and Transaction Disputes
• 2.6 Case Study: Bank of America’s Erica: A Virtual Financial Assistant that Handles 1+ Billion Interactions Using Predictive AI
• 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 & Behavioural Profiling
• 3.3 Real-time Monitoring Agents
• 3.4 Real-World Use Case: AI Agents Monitoring Transaction Behaviour and Flagging Anomalies for Real-Time Fraud Detection in Digital Wallets
• 3.5 Case Study: PayPal’s AI System Uses Graph-Based Anomaly Detection Agents to Flag 0.32% of All Transactions for Fraud with 99.9% Accuracy
• 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 Rate and 16% Lower APRs for Borrowers
• 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 Portfolio Weekly Based on Financial Goals and Market Trends
• 5.5 Case Study: Wealthfront’s Path Agent Uses Financial Behavior Modeling to Recommend Personalized Savings Goals and Investment Paths
• 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 Modelling Using Historical Data
• 6.3 Risk-Reward Threshold Management
• 6.4 Real-World Use Case: AI Trading Agents Performing Arbitrage Between Crypto Exchanges
• 6.4 Case Study: Renaissance Technologies Utilizes AI to Automate Short-Hold Trades, Generating Consistent Alpha via Adaptive Trading Bots
• 6.5 Hands-On Activity: 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
• 7.5 Case Study: BloombergGPT — A Financial-Grade Large Language Model
• 7.6 Hands-On Activity: NLP Agents for Financial Document Intelligence

Module 8: Compliance and Risk Surveillance Agents

• 8.1 AI for Anti-Money Laundering (AML) and Know Your Business (KYB)
• 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 multiple accounts.
• 8.5 Case Study: HSBC uses Quantexa’s AI agents to trace AML networks, increasing suspicious activity detection by 30%.
• 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 practices.
• 9.5 Case Study: Wells Fargo implemented internal AI fairness reviews for lending bots post regulatory scrutiny.
• 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
• 10.4 Capstone Project
• 10.5 Key Takeaways of the Module

Tools you will explore

• Python
• TensorFlow
• Pandas
• NumPy
• Power BI
• SQL
• OpenAI API
• APIs

Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam

Self study materials are available for 365 days.

Instructor-led OR Self-paced course + Official exam + Digital badge