AI+ Finance Agent (AP 2201 Self-Paced Training)
Seminardauer: 1 Tag
Ziele der Schulung
The AI+ Finance Agent certification is tailored for professionals seeking to leverage artificial intelligence to automate and optimize financial operations. It equips learners with the knowledge and practical skills to design, build, and deploy intelligent AI agents for finance use cases such as trading, risk evaluation, fraud detection, forecasting, compliance, and wealth management. Learners gain hands-on experience through real-world projects while understanding how AI agents enhance strategic decision-making across financial institutions. What Will You Learn?
- Core AI concepts applied to finance
- Designing and building AI-powered finance agents
- Applying AI agents to trading, fraud detection, credit scoring, and forecasting
- Implementing responsible, fair, and auditable AI systems
- Solving real-world finance problems using AI agents
Zielgruppe Seminar
- Finance Professionals (analysts, accountants, financial managers)
- Investment and Portfolio Specialists
- Fintech Enthusiasts
- Data and Technology Professionals with analytical or programming backgrounds
- Business Leaders and Decision-Makers
Voraussetzungen
- Basic knowledge of financial markets
- Familiarity with machine learning concepts
- Programming skills (e.g., Python)
- Understanding of statistical analysis
- Interest in financial technology
Seminarinhalt
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
- 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 and Unsupervised Machine Learning for Fraud Detection
- 3.2 Pattern Analysis and Behavioural Profiling
- 3.3 Real-Time Monitoring Agents
- 3.4 Real-World Use Case: AI Agents Monitoring Transaction Behaviour
- 3.5 Case Study: PayPal’s AI System
- 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
- 4.5 Case Study: Upstart’s AI-Based Lending Platform
- 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
- 5.5 Case Study: Wealthfront’s Path Agent
- 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
- 6.5 Case Study: Renaissance Technologies Adaptive Trading Bots
- 6.6 Hands-On Activity: Trading Bots and Market-Monitoring Agents
Module 7: NLP Agents for Financial Document Intelligence
- 7.1 Large Language Models in Earnings Calls 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
- 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 and KYB
- 8.2 Regulation-Aware Rule Modelling
- 8.3 Transaction Graph Analysis
- 8.4 Real-World Use Case: Agent Tracking Suspicious Transfers
- 8.5 Case Study: HSBC Compliance Use Case
- 8.6 Hands-On Activity: Compliance and Risk Surveillance Agents
Module 9: Responsible, Fair and Auditable AI Agents
- 9.1 Governance Frameworks for AI in Finance
- 9.2 Transparency and Auditability in Decision Logic
- 9.3 Fairness and Explainability
- 9.4 Real-World Use Case
- 9.5 Case Study: Internal AI Fairness Reviews
- 9.6 Hands-On Activity: Responsible, Fair and 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
- 10.3 Case Study 3: AI-Driven Credit Scoring
- 10.4 Capstone Project
- 10.5 Key Takeaways of the Module
Hinweise
Prüfung und Zertifizierung
- Duration: 90 minutes
- Passing Score: 70% (35 out of 50)
- Format: 50 multiple-choice and multiple-response questions
- Delivery Method: Online proctored examination
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