Generative AI in Finance: Forecasting, Risk & Portfolios

Name: Generative AI in Finance: Forecasting, Risk & Portfolios
Rating: 4.1 (33 reviews)

Use Generative AI for synthetic data, forecasting, fraud detection and portfolio optimization, with Jupyter demos

Created byTech Jedi

Last updated 10/2026

English

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What you'll learn

Generate synthetic financial data, simulate stock price patterns and anonymize sensitive records in Jupyter Notebook demos
Apply core finance concepts (markets, instruments, financial statements and valuation) and run a DCF calculation
Build time-series forecasts and see where Generative AI fits into financial forecasting workflows
Explore AI approaches to personalized investment recommendations, real-time risk management and fraud detection
Understand how deep reinforcement learning, neurosymbolic AI and ensemble methods support portfolio decisions
Assess data quality, regulatory and ethical considerations before you put AI-driven financial systems into use

Course content

6 sections • 37 lectures • 2h 14m total length

Overview of Generative AI5:34
Explore generative AI in finance, including synthetic data for testing, investment strategies, risk management, and fraud detection, plus data privacy and regulatory considerations.
Applications of Generative AI in Finance4:26
Investigate the challenges and opportunities of generative AI in finance, including synthetic data for testing models, simulations, data management, and privacy and governance under GDPR and HIPAA.
Challenges and Opportunities in Finance with Generative AI4:26
Explore how generative AI transforms finance by creating synthetic data, enhancing risk management, and guiding investment decisions, while addressing data quality, governance, privacy, GDPR, and HIPAA concerns.
Importance of Data Quality in Finance and AI4:25
Prioritize data quality in finance-enabled generative AI by cleansing, normalization, and feature engineering; ensure diverse, representative data, governance, validation, and monitoring for reliable, fair AI outputs.
Regulatory Considerations in AI-driven Finance3:16
Explore regulatory considerations for generative AI in finance, addressing data privacy and security under GDPR and HIPAA, and advancing transparency, algorithmic explainability, and bias and fairness through governance and collaboration.
Demo: Jupyter Notebook Environment Setup7:50
Demonstrates setting up a Jupyter notebook environment for generative AI in finance, presenting practical steps and configurations for advanced techniques and applications.
Demo: Generating Synthetic Financial Data2:46
Generate synthetic financial data with Python using pandas, random, and faker. Create a four-column dataset (transaction id, date, amount, category) and save as synthetic financial data.csv for demos and testing.

Financial Markets and Instruments3:06
Explore how generative AI reshapes financial markets and instruments, modeling instruments and simulating market scenarios with synthetic data for portfolio optimization, risk management, and regulatory compliance.
Financial Statements Analysis7:08
Explore how generative AI analyzes financial statements, automates data extraction, performs ratio analysis, detects trends and anomalies, and generates synthetic data for robust forecasting.
Demo: Reading a Financial Statement2:11
Analyze basic financial data with Python and pandas, compute profit margins for 2020–2022, and review revenue, expenses, and net income for clear financial insights.
Valuation Methods6:01
Explore valuation methods for generative AI, including the cost, market, and income approaches, to assess intrinsic worth, costs, market potential, and future revenue streams.
Demo: DCF Calculation2:38
The lecture demonstrates present value calculation from future cash flows of 10,000, 15,000, and 20,000 using an 8% discount rate in Python, illustrating the time value of money.
Risk Management in Finance3:27
Explore risk management for generative AI in finance, addressing data bias, model interpretability, and cybersecurity, while implementing governance and explainable techniques to enable safe innovation.
Financial Modeling and Forecasting6:19
Explore how generative AI transforms financial modeling and forecasting, using synthetic data, VAEs, GANs, RNNs, and LSTMs to enhance decision making, portfolio optimization, and risk management.
Demo: Time Series Forecasting4:48
Forecast future revenue using exponential smoothing on historical data with statsmodels, train the model to detect a linear trend, and visualize results with matplotlib for clear revenue forecasts.

Generating Synthetic Financial Data3:29
Explore how generative AI creates synthetic financial data, including stock prices, transaction records, and market trends, to test models and simulate market conditions using GANs, VAEs, and normalizing flows.
Demo: Generating Synthetic Financial Data2:14
See how generative AI creates synthetic financial data, showcasing advanced techniques and real-world applications in finance.
Demo: Simulating Stock Price Patterns1:28
Explore how generative AI techniques simulate stock price patterns, demonstrating practical approaches for modeling market dynamics in finance.
Personalized Investment Recommendations with AI4:30
Generative AI powers personalized investment recommendations by analyzing market trends, financial goals, and risk tolerance to tailor adaptive strategies for diverse portfolios.
Demo: Fraud Detection Anomaly Highlight1:37
Explore how generative AI techniques apply to fraud detection in finance, highlighting anomalies through a practical demo.
Real-time Risk Management using Generative AI3:05
Real-time risk management using generative AI generates synthetic data through advanced models to stress test portfolios under changing market conditions.

Biases in AI and Finance3:25
Explore how biases arise in generative AI used for credit risk, fraud detection, and portfolio optimization. Learn debiasing techniques, governance, and ethical strategies to ensure fair, accountable financial AI.
Transparency and Explainability in AI Models3:25
Examine how generative AI shapes finance through data and model biases, and explore transparency, explainability, fairness, debiasing, and governance for responsible deployment.
Data Privacy and Security in AI-driven Finance2:47
Generative AI in finance elevates efficiency but raises privacy and security concerns, requiring data governance and encryption. Adhere to regulatory standards, ensure transparency, and implement audits to protect customer data.
Demo: Anonymizing Financial Data1:30
Demonstrate techniques for anonymizing financial data using generative ai and explore practical applications of advanced data anonymization in finance.
Impact of AI on Employment in Finance Sector3:37
Generative AI automates financial analysis, report generation, and customer service, boosting efficiency while reshaping finance jobs and highlighting upskilling to address displacement risk.
Demo: Ethical Considerations in AI1:37
See a demo of ethical considerations in AI, highlighting responsible use of generative AI in finance and implications for advanced applications.

Deep Reinforcement Learning for Financial Decision Making5:15
Explore how generative AI and deep reinforcement learning transform financial decision making, enabling adaptive portfolio optimization, automated trading, and improved risk management in changing markets.
Neurosymbolic AI for Financial Planning6:23
Explore how neurosymbolic AI blends neural networks and symbolic reasoning to enhance financial planning, risk management, and forecasting with transparent, explainable models.
Ensemble Methods in Finance and AI Integration5:13
Explore how ensemble methods fuse generative AI models such as variational autoencoders (VAEs), GANs, and normalizing flows in finance to generate synthetic data and enhance risk management and forecasting.
Demo: Ensemble Model for Portfolio Optimization1:49
Explore how an ensemble model enhances portfolio optimization by combining multiple methods to improve risk-adjusted returns. See a practical demo of generative ai in finance applied to portfolio optimization.
AI-driven Portfolio Optimization Strategies3:39
Explore how generative AI enables portfolio optimization through synthetic market data, testing asset allocation, risk management, and rebalancing strategies, while addressing data quality, bias, and overfitting concerns.
Future Trends and Research Directions in Finance and AI2:38
Explore future trends and research directions at the finance ai frontier, highlighting generative ai for forecasting and personalized advice. Learn how these models automate tasks and support better decisions.

Demo: Data Preprocessing and Feature Engineering1:34
Demonstrate data preprocessing and feature engineering for generative AI in finance through a practical demo, highlighting techniques to prepare data and engineer features for advanced financial applications.
Hyperparameter tuning5:15
Explore how generative AI transforms finance through synthetic data generation, automated reporting, and enhanced investment strategies, with hyperparameter tuning, using grid search, random search, and Bayesian optimization for GAN models.
Demo: Deploying a Generative AI Model for Real-time Trading1:34
Demonstrate deploying a generative ai model for real-time trading and highlight practical deployment techniques for finance applications.
Bonus Lecture: Deepening Your AI Skills for Finance0:26

Requirements

Basic understanding of finance concepts (e.g., financial markets, risk management, valuation methods)
No prior experience in AI required—this course will teach you everything from the ground up
Familiarity with Python is helpful but not essential. We provide all necessary coding demonstrations
Access to a computer with an internet connection for hands-on demos and real-time model deployment

Description

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. You'll understand both their potential and their limits, and you'll have demo notebooks you can come back to.

If you want a practical, grounded introduction to using Generative AI in finance, enroll now and start with the first demo.

- Team Tech Jedi

Who this course is for:

Financial Analysts & Portfolio Managers seeking to generate synthetic data, optimize portfolios, and make AI-driven investment decisions.
Data Scientists & AI Engineers aiming to integrate Generative AI models with financial datasets for predictive analytics and anomaly detection.
Banking and FinTech Professionals interested in applying AI for risk management, fraud detection, and compliance.
Students & Researchers studying finance, AI, or computational economics and looking for hands-on experience with real-world applications.
Quantitative Analysts & Modelers who want to explore advanced AI techniques like deep reinforcement learning, ensemble models, and neurosymbolic AI in finance.

Generative AI in Finance: Forecasting, Risk & Portfolios

What you'll learn

Explore related topics

Course content

Introduction to Finance in Generative AI7 lectures • 33min

Fundamentals of Finance8 lectures • 36min

Integration of Finance and Generative AI6 lectures • 16min

Ethical and Social Considerations6 lectures • 16min

Advanced Topics in Finance and AI Integration6 lectures • 25min

Implementing Generative AI Models in Finance4 lectures • 9min

Requirements

Description

Who this course is for: