AI+ Finance Agent™

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

Exam: 50 questions, 70% passing, 90 minutes, online proctored exam

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