Generative Ai In Finance: Advanced Techniques & Applications

Learn Generative AI for Finance with Practical Demos & Real-World Applications (2025)

What you'll learn

Master Generative AI techniques for financial applications like trading and fraud detection

Apply AI for generating synthetic financial data and portfolio optimization

Understand ethical and regulatory challenges in AI-driven finance

Implement AI models in real-world finance scenarios, including real-time trading

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

Unlock the future of finance with our cutting-edge course, "Generative AI in Finance 2025: Master Advanced Techniques." Designed for finance professionals, students, and AI enthusiasts, this course delves deep into the transformative power of Generative AI in reshaping the financial landscape. From understanding foundational concepts to mastering advanced integration techniques, you will gain the skills needed to lead in an AI-driven financial ecosystem.What You'll Learn:Introduction to Generative AI in Finance:Explore the fundamentals of Generative AI and its diverse applications in finance.Understand the challenges and opportunities AI presents in the financial sector.Discuss the importance of data quality and regulatory considerations for AI in finance.Fundamentals of Finance:Gain essential knowledge of financial markets, instruments, statements, and valuation.Master risk management strategies and financial modeling techniques crucial for AI integration.Integration of Finance and Generative AI:Generate synthetic financial data for analysis.Learn algorithmic trading strategies powered by AI.Explore personalized investment recommendations and fraud detection methods using Generative AI.Engage in a hands-on demo of generating synthetic data.Ethical and Social Considerations:Address biases in AI and ensure transparency and explainability in AI models.Dive into data privacy, security, and the social impact of AI in finance.Understand regulatory compliance and ethical practices with practical examples.Advanced Topics in Finance and AI Integration:Learn deep reinforcement learning for financial decision-making.Discover ensemble methods, neurosymbolic AI, and future trends in AI-driven finance.Participate in a demo on portfolio optimization using advanced AI techniques.Implementing Generative AI Models in Finance:Master data preprocessing, feature engineering, and model training.Fine-tune models through hyperparameter tuning and deployment strategies.Experience a real-world demo of deploying a Generative AI model for real-time trading.Why Enroll?Hands-on learning: Practice real-world applications and demos for immediate, practical skills.Expert insights: Stay ahead of industry trends and techniques in AI-powered finance.Career growth: Equip yourself with cutting-edge knowledge to excel in AI-driven finance roles.This course is structured to take you from foundational knowledge to advanced mastery, ensuring you can confidently apply AI techniques to solve complex financial challenges. Enhance your expertise, expand your career potential, and lead the future of finance with Generative AI.

Overview

Section 1: Introduction to Finance in Generative AI

Lecture 1 Overview of Generative AI

Lecture 2 Applications of Generative AI in Finance

Lecture 3 Challenges and Opportunities in Finance with Generative AI

Lecture 4 Importance of Data Quality in Finance and AI

Lecture 5 Regulatory Considerations in AI-driven Finance

Lecture 6 Demo: Jupyter Notebook Environment Setup

Lecture 7 Demo: Generating Synthetic Financial Data

Section 2: Fundamentals of Finance

Lecture 8 Financial Markets and Instruments

Lecture 9 Financial Statements Analysis

Lecture 10 Demo: Reading a Financial Statement

Lecture 11 Valuation Methods

Lecture 12 Demo: DCF Calculation

Lecture 13 Risk Management in Finance

Lecture 14 Financial Modeling and Forecasting

Lecture 15 Demo: Time Series Forecasting

Section 3: Integration of Finance and Generative AI

Lecture 16 Generating Synthetic Financial Data

Lecture 17 Demo: Generating Synthetic Financial Data

Lecture 18 Demo: Simulating Stock Price Patterns

Lecture 19 Personalized Investment Recommendations with AI

Lecture 20 Demo: Fraud Detection Anomaly Highlight

Lecture 21 Real-time Risk Management using Generative AI

Section 4: Ethical and Social Considerations

Lecture 22 Biases in AI and Finance

Lecture 23 Transparency and Explainability in AI Models

Lecture 24 Data Privacy and Security in AI-driven Finance

Lecture 25 Demo: Anonymizing Financial Data

Lecture 26 Impact of AI on Employment in Finance Sector

Lecture 27 Demo: Ethical Considerations in AI

Section 5: Advanced Topics in Finance and AI Integration

Lecture 28 Deep Reinforcement Learning for Financial Decision Making

Lecture 29 Neurosymbolic AI for Financial Planning

Lecture 30 Ensemble Methods in Finance and AI Integration

Lecture 31 Demo: Ensemble Model for Portfolio Optimization

Lecture 32 AI-driven Portfolio Optimization Strategies

Lecture 33 Future Trends and Research Directions in Finance and AI

Section 6: Implementing Generative AI Models in Finance

Lecture 34 Demo: Data Preprocessing and Feature Engineering

Lecture 35 Hyperparameter tuning

Lecture 36 Demo: Deploying a Generative AI Model for Real-time Trading

Finance professionals looking to enhance their expertise by integrating AI into financial decision-making, trading, and risk management,Data scientists and AI enthusiasts eager to apply Generative AI techniques in the finance sector,Students and graduates pursuing careers in finance or AI and seeking hands-on experience in the practical applications of AI,Tech professionals wanting to transition into finance and AI-focused roles, especially in algorithmic trading and financial modeling