AI+ Finance Agent Specialty™ eLearning
Empower organizations with AI+ Finance Agent Specialty™ 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
- Leer in je eigen tempo, waar en wanneer jij wilt
- Betalen op factuur, annuleren volgens heldere voorwaarden
- Ook incompany voor je hele team
Ruim 5.000 cursisten per jaar volgen een training bij ons
Voor wie
Voor wie is deze training
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.
De training
Dit leer je tijdens de training
Praktijkgericht, met een trainer die zelf in het veld werkt. Geen theorie om de theorie.
Na deze training
Wat deze training je oplevert
- Financial Accuracy & Reliability: AI automation reduces manual errors and enhances precision across reconciliation, reporting, and day-to-day finance tasks.
- Strategic Insight & Intelligence: Data-driven forecasting and analytics empower faster, smarter decisions in budgeting, planning, and financial strategy.
- Risk Management & Compliance Strength: AI tools elevate fraud detection, regulatory oversight, and secure handling of sensitive financial data.
- Operational Efficiency in Finance: Intelligent automation streamlines routine workflows, enabling teams to focus on high-impact financial initiatives.
- Career Advancement in Digital Finance: Certification positions professionals at the forefront of AI-enabled finance transformation, increasing market relevance.
Inhoud
Het programma
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Python
- TensorFlow
- Pandas
- NumPy
- Power BI
- SQL
- OpenAI API
- APIs
Praktisch
Goed om te weten
Lesmethode
Kenmerken
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Voorkennis
- Basic Knowledge of Financial Markets, Familiarity with Machine Learning, Programming Skills, Statistical Analysis Understanding, Interest in Financial Technology
Inschrijven
Alles op een rij
AI+ Finance Agent Specialty™ eLearning
- Betalen op factuur mogelijk
- Verschuiven of annuleren volgens onze voorwaarden
- Ook incompany voor je hele team via maatwerk
Zekerheid
Inschrijven zonder verrassingen
Heldere voorwaarden
Verschuiven of annuleren doe je volgens onze algemene voorwaarden. Geen kleine lettertjes achteraf.
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