AI+ Finance Agent™

Cílová skupina

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Finance Professionals: Ideal for analysts, accountants, and financial managers looking to integrate AI into everyday workflows.

Investment & Portfolio Specialists: Suited for individuals aiming to enhance forecasting, risk modeling, and data-driven investment strategies.

Fintech Enthusiasts: Perfect for learners interested in the intersection of AI, automation, and modern financial technologies.

Data & Tech Professionals: Great for those with analytical or programming backgrounds seeking to apply AI in financial domains.

Business Leaders & Decision-Makers: Beneficial for executives wanting to leverage AI for smarter budgeting, planning, and strategic financial growth.

Struktura kurzu

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Module 1: Introduction to AI Agents in Finance

1. 1.1 Understanding AI Agents in Finance vs Traditional Financial Automation
2. 1.2 The Evolution of AI Agents in Financial Services
3. 1.3 Overview of Different Types of AI Agents in Finance
4. 1.4 Importance of Agent Autonomy and Task Delegation in Financial Settings
5. 1.5 Key Differences Between AI Agents in Finance and Traditional Automation
6. 1.6 Hands-On Activity: Exploring AI Agents in Finance

Module 2: Building and Understanding AI Agents in Finance

1. 2.1 Architecture of AI Agents in Finance
2. 2.2 Tools and Libraries for Agent Development
3. 2.3 AI Agents vs. Static Models
4. 2.4 Overview of Agent Lifecycle
5. 2.5 Use Case: Customer Support Agents in Banks for Handling KYC, FAQs, and Transaction Disputes
6. 2.6 Case Study: Bank of America’s Erica: A Virtual Financial Assistant that Handles 1+ Billion Interactions Using Predictive AI
7. 2.7 Hands-On Activity: Building and Understanding AI Agents in Finance

Module 3: Intelligent Agents for Fraud Detection and Anomaly Monitoring

1. 3.1 Supervised/Unsupervised ML for Fraud Detection
2. 3.2 Pattern Analysis & Behavioural Profiling
3. 3.3 Real-time Monitoring Agents
4. 3.4 Real-World Use Case: AI Agents Monitoring Transaction Behaviour and Flagging Anomalies for Real-Time Fraud Detection in Digital Wallets
5. 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
6. 3.6 Hands-On Activity: Intelligent Agents for Fraud Detection and Anomaly Monitoring

Module 4: AI Agents for Credit Scoring and Lending Automation

1. 4.1 Feature Generation from Non-Traditional Credit Data
2. 4.2 Explainability (XAI) in Credit Decisions
3. 4.3 Bias Mitigation in Lending Agents
4. 4.4 Real-World Use Case: Agents Assessing New-to-Credit Individuals Using Transaction and Mobile Data
5. 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
6. 4.6 Hands-On Activity: AI Agents for Credit Scoring and Lending Automation

Module 5: AI Agents for Wealth Management and Robo-Advisory

1. 5.1 Personalization Using Profiling Agents
2. 5.2 Portfolio Rebalancing Algorithms
3. 5.3 Sentiment-Aware Investing
4. 5.4 Real-World Use Case: AI Agent Adjusting Portfolio Weekly Based on Financial Goals and Market Trends
5. 5.5 Case Study: Wealthfront’s Path Agent Uses Financial Behavior Modeling to Recommend Personalized Savings Goals and Investment Paths
6. 5.6 Hands-On Activity: AI Agents for Wealth Management and Robo-Advisory

Module 6: Trading Bots and Market-Monitoring Agents

1. 6.1 Reinforcement Learning in Trading Agents
2. 6.2 Predictive Modelling Using Historical Data
3. 6.3 Risk-Reward Threshold Management
4. 6.4 Real-World Use Case: AI Trading Agents Performing Arbitrage Between Crypto Exchanges
5. 6.4 Case Study: Renaissance Technologies Utilizes AI to Automate Short-Hold Trades, Generating Consistent Alpha via Adaptive Trading Bots
6. 6.5 Hands-On Activity: Trading Bots and Market-Monitoring Agents

Module 7: NLP Agents for Financial Document Intelligence

1. 7.1 LLMs in Earnings Call and Filings Analysis
2. 7.2 AI Summarization and Event Detection
3. 7.3 Voice-to-Text and Key-Point Extraction
4. 7.4 Real-World Use Case
5. 7.5 Case Study: BloombergGPT — A Financial-Grade Large Language Model
6. 7.6 Hands-On Activity: NLP Agents for Financial Document Intelligence

Module 8: Compliance and Risk Surveillance Agents

1. 8.1 AI for Anti-Money Laundering (AML) and Know Your Business (KYB)
2. 8.2 Regulation-aware Rule Modelling
3. 8.3 Transaction Graph Analysis
4. 8.4 Real-World Use Case: Agent tracking suspicious cross-border money transfers in real-time across multiple accounts.
5. 8.5 Case Study: HSBC uses Quantexa’s AI agents to trace AML networks, increasing suspicious activity detection by 30%.
6. 8.6 Hands-On Activity: Compliance and Risk Surveillance Agents in Financial Systems

Module 9: Responsible, Fair & Auditable AI Agents

1. 9.1 Governance Frameworks for AI in Finance (RBI, EU AI Act)
2. 9.2 Transparency and Auditability in Decision Logic
3. 9.3 Fairness and Explainability
4. 9.4 Real-World Use Case: Auditable AI Agent Logs Used During Internal Policy Audits to Ensure Fair Lending practices.
5. 9.5 Case Study: Wells Fargo implemented internal AI fairness reviews for lending bots post regulatory scrutiny.
6. 9.6 Hands-On Activity: Responsible, Fair & Auditable AI Agents in Finance

Module 10: World Famous Case Studies

1. 10.1 Case Study 1: JPMorgan’s COiN Platform
2. 10.2 Case Study 2: AI in Fraud Detection – PayPal’s Decision Intelligence
3. 10.3 Case Study: AI-Driven Credit Scoring – Upstart’s Lending Platform
4. 10.4 Capstone Project
5. 10.5 Key Takeaways of the Module

Certifikace

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