Generative AI and its Applications in Finance

Generative AI (GAI) is a powerful technology that enables computers to create new content—such as text, images, or financial data, by learning from existing patterns. Unlike traditional AI, which analyses and processes data, GAI leverages advanced machine learning models to generate realistic outputs that mimic real-world information. In the financial sector, this technology is revolutionizing fraud detection, risk assessment, market forecasting, and trading strategies, making financial systems more efficient, secure, and intelligent. This course provides a foundational understanding of Generative AI and its transformative role in the financial industry. It covers key AI models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Autoregressive Models, explaining their applications in financial forecasting, risk management, synthetic data generation, and fraud detection. Additionally, the course explores AI-driven trading strategies, regulatory and ethical considerations, and emerging trends shaping the future of finance. Through a mix of theoretical insights and practical real-world applications, participants will gain the skills needed to leverage Generative AI for enhancing financial operations, decision-making, and risk. 3b

• By the end of this structured course modules, participants will:✔ Understand the Foundations of Generative AI in Finance – Learn how GANs, VAEs, and Autoregressive Models drive innovation.✔ Enhance Trading & Forecasting Techniques – Utilize AI-based tools for market analysis and predictive trading.✔ Strengthen Risk Management & Fraud Detection – Use AI methods to Identify anomalies, assess risks, and mitigate financial fraud effectively.✔ Generate & Use Synthetic Financial Data Responsibly – Address data scarcity while ensuring privacy compliance.✔ Ethical considerations and regulations – Learn best practices for AI governance for responsible and compliant AI usage in finance.

• Basic understanding of finance and financial markets
• Familiarity with machine learning concepts and terminology
• Basic programming knowledge (e.g., Python) is recommended but not mandatory