Course Overview
TOPAI+ Finance Agent™ equips professionals with the ability to turn complex financial workflows into automated, intelligent systems. The course highlights how AI agents perform forecasting, analyze market movements, detect risks, and streamline compliance tasks with speed and precision. Participants see how agentic automation reshapes decision cycles and reduces operational burden across finance functions.
Scheduled Classes
TOP| 07/15/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 08/12/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 09/16/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 10/14/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 11/18/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 12/16/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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What You'll Learn
TOP- Understand how AI agents support financial analysis, forecasting, research, and automation
- Build task-driven agents using LLM reasoning, tool integration, and prompt pipelines
- Apply AI to portfolio evaluation, risk scoring, anomaly detection, and financial data processing
- Implement RAG (Retrieval-Augmented Generation) for context-rich market research and reporting
- Explore compliance, audit-readiness, and guardrails necessary for safe AI adoption in finance
- Design, test, and deploy autonomous finance agents to streamline workflows and enhance decision-making
Outline
TOPIntroduction to AI Agents in Finance
- Understanding AI Agents in Finance vs Traditional Financial Automation
- The Evolution of AI Agents in Financial Services
- Overview of Different Types of AI Agents in Finance
- Importance of Agent Autonomy and Task Delegation in Financial Settings
- Key Differences Between AI Agents in Finance and Traditional Automation
- Hands-On Activity: Exploring AI Agents in Finance
Building and Understanding AI Agents in Finance
- Architecture of AI Agents in Finance
- Tools and Libraries for Agent Development
- AI Agents vs. Static Models
- Overview of Agent Lifecycle
- Use Case: Customer Support Agents in Banks for Handling KYC, FAQs, and Transaction Disputes
- Case Study: Bank of America’s Erica: A Virtual Financial Assistant that Handles 1+ Billion Interactions Using Predictive AI
- Hands-On Activity: Building and Understanding AI Agents in Finance
Intelligent Agents for Fraud Detection and Anomaly Monitoring
- Supervised/Unsupervised ML for Fraud Detection
- Pattern Analysis & Behavioural Profiling
- Real-time Monitoring Agents
- Real-World Use Case: AI Agents Monitoring Transaction Behaviour and Flagging Anomalies for Real-Time Fraud Detection in Digital Wallets
- 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
- Hands-On Activity: Intelligent Agents for Fraud Detection and Anomaly Monitoring
AI Agents for Credit Scoring and Lending Automation
- Feature Generation from Non-Traditional Credit Data
- Explainability (XAI) in Credit Decisions
- Bias Mitigation in Lending Agents
- Real-World Use Case: Agents Assessing New-to-Credit Individuals Using Transaction and Mobile Data
- Case Study: Upstart’s AI-Based Lending Platform Approved by CFPB Showed 27% Increase in Approval Rate and 16% Lower APRs for Borrowers
- Hands-On Activity: AI Agents for Credit Scoring and Lending Automation
AI Agents for Wealth Management and Robo-Advisory
- Personalization Using Profiling Agents
- Portfolio Rebalancing Algorithms
- Sentiment-Aware Investing
- Real-World Use Case: AI Agent Adjusting Portfolio Weekly Based on Financial Goals and Market Trends
- Case Study: Wealthfront’s Path Agent Uses Financial Behavior Modeling to Recommend Personalized Savings Goals and Investment Paths
- Hands-On Activity: AI Agents for Wealth Management and Robo-Advisory
Prerequisites
TOPRequired
- Basic Knowledge of Financial Markets – Understanding of stock markets, trading, and financial instruments.
- Familiarity with Machine Learning – Basic concepts and algorithms of machine learning.
- Programming Skills – Proficiency in Python or similar languages for coding.
- Statistical Analysis Understanding – Knowledge of data analysis and statistical methods.
- Interest in Financial Technology – Enthusiasm for applying AI to solve financial challenges.
Who Should Attend
TOP- Finance Specialist
