Detailed Course Outline
Module 1: Introduction to AI Agents in Finance
Exploring this module helps you see why AI agents are becoming essential in modern finance. You get a clearer view of how these systems elevate decisionmaking, adapt to real-time data, and take on tasks that traditional automation can’t handle. As financial operations grow more complex and fast-paced, this knowledge gives you an edge, allowing you to work smarter, respond faster, and contribute more effectively to data-driven financial environments. In this module, you dive into AI agents vs. traditional automation, how AI has evolved in financial services, and the main types of agents used today. You also examine autonomy, task delegation, workflow components, and real-world applications—from fraud detection to personalized advisory systems. To reinforce learning, the module includes a hands-on activity that lets you experience how AI agents operate within financial processes.
Module 2: Building and Understanding AI Agents in Finance
Gaining insight into this module helps you see why AI agents are becoming essential in modern financial systems. As financial services move toward automation, you benefit from knowing how intelligent agents enable faster decisions, stronger customer support, and more adaptive financial operations. This knowledge equips you to work effectively in environments where AI-driven tools handle complex tasks and continuously evolve with real-time data, giving you a clear advantage in a rapidly changing industry.
In this module, you explore the architecture of AI agents, the tools and libraries used to build them, and how they differ from static models. You examine the full agent lifecycle, review real-world examples like customer support agents in banking, and study Bank of America’s Erica. The hands-on activity helps you build an AI agent using practical tools tailored to finance.
Module 3: Intelligent Agents for Fraud Detection and Anomaly Monitoring
This module helps you see why fraud detection matters to your work as financial systems become faster, more digital, and more vulnerable to hidden risks. You gain clarity on how AI can protect transactions, reduce financial losses, and identify suspicious behaviour long before it causes damage. As fraud tactics evolve, you benefit from knowing how intelligent agents spot anomalies in real time, strengthen security, and support safer, more reliable financial operations.
In this module, you explore supervised and unsupervised ML techniques, pattern analysis, behavioural profiling, and real-time monitoring agents. You examine realworld applications such as anomaly detection in digital wallets and study PayPal’s graph-based fraud system, which achieves 99.9% accuracy. A hands-on activity allows you to build and experiment with fraud-detection agents to reinforce practical learning.
Module 4: AI Agents for Credit Scoring and Lending Automation
As lending rapidly shifts toward automation, this module helps you see why AI-driven credit decisions are becoming essential to your work. You learn how AI strengthens financial inclusion, reduces manual effort, and improves fairness by analyzing data beyond traditional credit reports. With lending models now expected to be transparent, unbiased, and accurate, this module prepares you to work confidently with AI systems that influence borrower approvals, risk assessments, and regulatory outcomes.
In this module, you explore non-traditional credit data, Explainable AI (XAI) for transparent decisions, and techniques for bias mitigation in lending agents. You study real-world examples of assessing new-to-credit individuals, analyze Upstart’s CFPB-approved lending model, and participate in a hands-on activity that walks you through automating credit scoring and lending workflows.
Module 5: AI Agents for Wealth Management and Robo-Advisory
As wealth management becomes increasingly data-driven, this module helps you see why AI-powered personalization, dynamic portfolio adjustments, and sentimentaware strategies are becoming essential to modern financial decision-making. You gain clarity on how AI agents enhance investment outcomes, adapt to changing markets, and deliver customized solutions that were previously impossible at scale. This prepares you to operate confidently in environments, where intelligent automation shapes long-term financial success. In this module, you explore profiling-based personalization, portfolio rebalancing algorithms, sentiment-aware investing, and real-world applications such as weekly AI-driven portfolio adjustments. You examine case studies like Wealthfront’s Path agent and analyze how AI models recommend tailored savings and investment paths. The hands-on activity guides you through automating portfolio workflows, allowing you to experience how AI agents personalize strategies and support wealth management tasks.
Module 6: Trading Bots and Market-Monitoring Agents
In today’s fast-moving financial markets, you need the ability to analyse trends, react instantly, and make informed trading decisions. AI-driven trading bots give you an edge by automating complex tasks, spotting opportunities faster than manual methods, and reducing emotional errors. By exploring how these systems operate, you equip yourself to work smarter, improve accuracy, and stay competitive in environments where speed, data, and adaptability define success.
In this module, you explore reinforcement learning for trading decisions, predictive modelling built on historical data, and techniques to manage risk-reward thresholds effectively. You also dive into real-world examples like crypto arbitrage, examine how firms such as Renaissance Technologies use adaptive trading bots, and complete a hands-on activity where you build and test your own market-monitoring agent.
Module 7: NLP Agents for Financial Document Intelligence
As financial operations become more complex, you need the ability to work with agents that plan, adapt, and execute tasks independently. Autonomous financial agents help you streamline decision-making, reduce manual effort, and respond faster to changing market or customer conditions. By learning how these agents operate with minimal oversight, you equip yourself to build solutions that improve efficiency, maintain consistency, and support large-scale financial workflows across institutions.
In this module, you explore autonomy levels, agent planning strategies, memory integration, action execution, and optimization methods. You examine real-world applications such as automated loan processing, portfolio adjustments, and multistep task automation in financial environments. You also work through a hands-on activity that guides you in designing an autonomous agent using goals, context, tools, and structured workflows tailored for finance.
Module 8: Compliance and Risk Surveillance Agents
As financial regulations evolve rapidly and risks grow more complex, you need strong insight into how AI strengthens compliance and surveillance. This module helps you see why real-time monitoring, advanced risk detection, and automated verification are essential for staying ahead of financial crime. By learning how AI enhances AML and KYB processes, you equip yourself to work with systems that reduce manual effort, improve accuracy, and protect institutions from regulatory penalties and reputational damage.
In this module, you explore AI in AML/KYB, regulation-aware rule modelling, transaction graph analysis, and real-world applications like cross-border surveillance. You examine HSBC’s use of Quantexa’s AI to boost detection accuracy by 30%, study entity resolution and sanctions screening, and dive into techniques such as adverse media analysis, behavioural anomaly detection, and GNN-based fraud detection. A hands-on activity guides you in designing compliance agents using real-time monitoring and rule-driven workflows.
Module 9: Responsible, Fair & Auditable AI Agents
As AI takes on a larger role in financial decision-making, you need the ability to ensure these systems operate ethically, transparently, and in line with regulatory expectations. This module helps you see why fairness, auditability, and governance matter when AI models influence lending, credit scoring, fraud checks, and customer outcomes. By learning how responsible AI minimizes bias, reduces regulatory risk, and strengthens trust, you prepare yourself to work confidently with systems that must withstand audits, explanations, and real-world scrutiny.
In this module, you explore governance frameworks such as RBI guidelines and the EU AI Act, principles of fairness and explainability, and techniques for traceable decision logic. You examine real-world examples, including auditable AI logs and fairness reviews from Wells Fargo and JPMorgan. The hands-on activity equips you to design AI agents that are transparent, compliant, and auditable, with a focus on fair lending and ethical financial practices.
Module 10: World Famous Case Studies
As financial systems evolve, you benefit from seeing how global institutions use AI to solve large-scale challenges. Real case studies show you how AI delivers massive time savings, improves accuracy, strengthens fraud prevention, and expands access to credit. By exploring proven, high-impact examples, you gain a clear view of what works in real operations, helping you make stronger decisions, design smarter agents, and recognize the measurable value AI brings across financial services. In this module, you examine three major implementations: JPMorgan’s COiN platform, which reviews 12,000 loan documents in seconds; PayPal’s Decision Intelligence system, which detects fraud with 99.9% accuracy; and Upstart’s AI-driven credit scoring model, which boosts approvals by 27% and reduces defaults by 16%.You also explore key insights on efficiency, fairness, and risk reduction demonstrated through these cases.