AI in FinanceStarts April 13,

2026

This opening module establishes a common language for AI in finance, covering its historical evolution, key terminology, and the main categories of tools and data. Through hands-on exercises, participants will discover how predictive and generative AI approaches can be applied to real industry contexts.

By the end of Core 1, participants will be able to:

• Define and apply core AI concepts and vocabulary to contemporary challenges in finance contexts.
• Distinguish between predictive and generative AI methods and understand their applications to contemporary challenges in finance.
• Identify and demonstrate familiarity with major AI tools and data types that support financial applications.

This module builds on the foundations from Core 1, examining how AI is applied across financial markets and institutions. Through real-world examples, participants will explore applications in investment research and management, credit risk and fraud detection, client engagement, compliance, and operational efficiency. These cases will demonstrate how AI is reshaping the core functions of banks, insurers, asset managers, and fintechs. Each application highlights the interaction of business objectives, data types, and modeling approaches, demonstrating both the potential and the limitations of AI in practice.

By the end of Core 2, participants will be able to:

• Recognize the breadth of AI applications across finance functions and industries.
• Demonstrate how different types of financial data (historical, tabular, text, sentiment, behavioral) enable applied AI use cases.
• Connect AI methods and applications to their own roles by answering: “How does AI help me in my job?”

Led by an industry expert, this module introduces the principles and practices of responsible AI. Building on previous material, participants will be able to evaluate how the complexity of AI models, their data requirements, and their behaviors create challenges distinct from traditional financial models. Participants will examine the full spectrum of risks, such as legal, reputational, financial, regulatory, and information security. The module also highlights current industry practices for managing these risks through guardrails and governance frameworks.

By the end of Core 3, participants will be able to:

• Identify and analyze the unique risk landscape of AI in finance, including legal, regulatory, reputational, and security risks.
• Recognize how new risks (e.g., hallucination, prompt injection) compound traditional financial model risks.
• Understand current approaches to risk mitigation, including AI guardrails, monitoring, and governance.

Delivered by industry experts at the forefront of AI innovation, this module explores how artificial intelligence is reshaping investment decision making at scale, alongside broader applications in risk management, operations, compliance, and client engagement. Drawing on real-world experience, participants also delve into the future of financial strategies in an AI-driven industry.

In this culminating module, participants integrate concepts from the program to think strategically about AI’s potential while pragmatically assessing the challenges of adoption in their organizations. Through case studies, readiness mapping, and leadership-focused exercises, participants will evaluate barriers, opportunities, and organizational changes needed to derive long-term value from AI.