Generative AI in Finance: Forecasting, Risk & Portfolios

About this course

Generative AI is already changing how financial teams work with data, build forecasts and manage risk. This course shows you where it helps, where it falls short, and how to try it yourself in Jupyter Notebook.

Who this course is for:

• Finance professionals who want to see how AI fits into analysis, forecasting and risk work
• Data scientists moving into financial services who need the finance basics
• Analysts and technical leaders evaluating AI use cases for their organization

What you'll do in the demos:

• Generate synthetic financial data
• Simulate stock price patterns
• Anonymize sensitive financial data
• Run DCF calculations and time-series forecasts

What's inside:1. Generative AI in finance. We start with the applications, challenges and opportunities. Then we cover the topics that decide whether an AI project succeeds in finance: data quality, regulatory considerations, and the ethical and social implications of AI-driven financial systems.2. Finance fundamentals. If you come from data or tech, this section gives you the domain grounding you need. It covers financial markets, instruments, financial statements, valuation methods, risk management and forecasting. You'll work through DCF calculations and time-series forecasting so the later AI material makes sense.3. Advanced AI applications. We look at personalized investment recommendations, real-time risk management, fraud detection, deep reinforcement learning for financial decision-making, neurosymbolic AI, and ensemble methods for portfolio optimization. Hands-on exercises walk you through implementing AI models for real-time trading and deploying them.

The course moves from concepts to practical demos. Finance experts get a clear view of the AI side, and technical learners get the finance context they often lack.

By the end, you'll be able to design and evaluate AI-driven financial solutions.

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