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Financial Modeling With Generative Ai Certification

Free Download Financial Modeling With Generative Ai Certification
Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 10.90 GB | Duration: 18h 6m
Build a Strong Foundation in Financial Modeling with Generative AI for Enhanced Decision-Making and Analysis

What you'll learn
Fundamentals of financial modeling and its applications in AI-enhanced finance.
Core principles of generative AI and its role in financial strategy.
Integrating generative AI into traditional financial models effectively.
Exploring key AI tools and platforms used in financial modeling.
Setting up and managing a generative AI framework for finance.
Understanding data quality and preparation requirements for AI models.
Building and optimizing data pipelines compatible with AI systems.
Utilizing generative AI for accurate time-series forecasting.
Applying AI to scenario planning and evaluating potential outcomes.
Fundamentals of risk assessment and AI-driven risk scoring techniques.
Enhancing asset valuation with AI-driven dynamic valuation models.
AI applications in financial statement and ratio analysis.
Portfolio management strategies using AI for diversification and risk.
Real-time financial data integration and high-frequency trading models.
Automating financial report generation with generative AI.
Ethical considerations and compliance standards for AI in finance.
Requirements
No Prerequisites.
Description
This course offers an in-depth exploration of the rapidly evolving field of financial modeling, particularly focusing on the integration of generative AI to enhance traditional models and decision-making processes. Students will begin with an introduction to financial modeling and the transformative role generative AI can play within this framework. The curriculum is meticulously designed to provide students with a foundational understanding of financial modeling and AI fundamentals while exploring the broader applications, limitations, and ethical considerations that accompany such advanced technologies. While the course is heavily rooted in theory, this theoretical foundation serves as a springboard for developing a sophisticated understanding of the complexities and nuances of AI-driven financial innovation.As students progress, they will delve into the structure and requirements for implementing a generative AI framework. A significant emphasis is placed on understanding the importance of data within this context, exploring data quality, compatibility, and the automation processes essential for effective AI integration. Through a thorough examination of data pipelines and the critical need for high-quality input, students will develop a nuanced understanding of how data quality directly impacts AI's effectiveness in financial modeling. By the end of this section, students will be able to assess and implement data pipelines that are structured and optimized for AI compatibility, setting a solid foundation for advanced AI applications in finance.The curriculum also addresses how generative AI contributes to forecasting and predictive modeling within financial contexts. This section explores predictive modeling techniques, including time series forecasting and scenario planning. Through a study of scenario generation and accuracy evaluation, students will gain insights into how predictive models can be optimized with AI, thereby offering enhanced foresight in financial predictions. This predictive modeling section provides a deep dive into statistical and probabilistic techniques combined with AI, allowing students to understand and evaluate the robustness of their forecasts. These insights, grounded in theory, encourage students to think critically about the application of AI in different forecasting scenarios and understand the conditions under which such models deliver maximum accuracy.One of the most impactful sections of the course is devoted to risk assessment, where students examine the role of generative AI in identifying and evaluating various financial risks. They will learn to assess risk scenarios using AI and explore different risk assessment frameworks. Theoretical underpinnings guide this exploration, covering aspects such as risk scoring, scenario simulations, and risk-adjusted returns. These topics encourage students to reflect on the traditional principles of financial risk assessment and consider how AI can enhance, support, and sometimes challenge these longstanding models. Students will gain the theoretical skills needed to not only implement these risk assessments but to evaluate the reliability and ethical implications of AI-driven risk analyses.A key component of this course is understanding how AI can support advanced predictive analytics in finance. Students will explore machine learning and generative AI techniques, their differences, and how each contributes to predictive analytics. The course also covers hyperparameter tuning, a process critical to refining predictive models, and various techniques for improving accuracy in financial predictions. This section is theory-heavy, preparing students to deeply understand the technical complexities of these models, which can then be applied to real-world predictive scenarios, demonstrating how AI-driven forecasts can become more precise and resilient in a fluctuating financial landscape.In addition, this course examines regulatory and ethical considerations inherent to using AI in finance. As AI increasingly influences decision-making processes and strategic directions in finance, regulatory frameworks and ethical implications must be carefully considered. This section provides students with a solid theoretical grounding in understanding the landscape of financial regulations, privacy concerns, and ethical challenges specific to AI. Students will discuss compliance, risk mitigation, and security issues that arise when deploying AI in financial contexts. The goal is to equip students with a robust understanding of how to navigate and manage ethical and regulatory risks, fostering a mindset that balances innovation with accountability and integrity.The final sections of the course bring together many of the concepts covered earlier, including real-time data integration, automation, and AI-driven decision-making processes. Students will learn how to integrate AI recommendations into financial decisions, understand board-level AI decision models, and explore future trends in financial AI, including sustainable finance and emerging technologies. These concluding topics synthesize students' accumulated knowledge, enabling them to comprehend the multifaceted role AI will play in the future of financial modeling. The course ultimately aims to build a comprehensive theoretical foundation, preparing students for both current and anticipated challenges and opportunities AI presents in financial modeling.
Overview
Section 1: Course Resources and Downloads
Lecture 1 Course Resources and Downloads
Section 2: Introduction to Financial Modeling with Generative AI
Lecture 2 Section Introduction
Lecture 3 Basics of Financial Modeling
Lecture 4 Case Study: TechNova's AI Integration: Balancing Innovation with Strategy
Lecture 5 Overview of Generative AI
Lecture 6 Case Study: Transforming Financial Modeling
Lecture 7 Integrating AI in Financial Models
Lecture 8 Case Study: Revolutionizing Credit Risk Assessment
Lecture 9 AI-Enhanced Decision-Making
Lecture 10 Case Study: AI-Driven Transformation in Financial Modeling
Lecture 11 Tools and Platforms for Financial AI
Lecture 12 Case Study: Harnessing AI for Financial Innovation
Lecture 13 Section Summary
Section 3: Setting Up a Generative AI Framework
Lecture 14 Section Introduction
Lecture 15 Understanding Data Requirements
Lecture 16 Case Study: Optimizing Data Quality for AI-Driven Financial Modeling Success
Lecture 17 Selecting Appropriate Models
Lecture 18 Case Study: Navigating Financial Model Selection
Lecture 19 Building AI-Compatible Data Pipelines
Lecture 20 Case Study: Optimizing AI-Ready Data Pipelines
Lecture 21 Automating Data Preparation
Lecture 22 Case Study: Enhancing Financial Modeling
Lecture 23 Evaluating Data Quality for AI
Lecture 24 Case Study: GreenBank's Data Quality Revolution
Lecture 25 Section Summary
Section 4: Generative AI in Forecasting and Predictive Modeling
Lecture 26 Section Introduction
Lecture 27 Introduction to Predictive Modeling
Lecture 28 Case Study: Enhancing Financial Predictions with Generative AI
Lecture 29 Using Generative AI for Time Series Forecasting
Lecture 30 Case Study: Harnessing Generative AI for Enhanced Financial Forecasting
Lecture 31 Scenario Planning with AI
Lecture 32 Case Study: AI-Driven Scenario Planning: Transforming Financial Modeling
Lecture 33 Evaluating Forecast Accuracy
Lecture 34 Case Study: Harnessing Generative AI for Accurate Forecasting
Lecture 35 Improving Predictive Models with AI
Lecture 36 Case Study: Enhancing Financial Forecasting at FinBank
Lecture 37 Section Summary
Section 5: Scenario Analysis with Generative AI
Lecture 38 Section Introduction
Lecture 39 Importance of Scenario Analysis
Lecture 40 Case Study: Generative AI in Transforming Scenario Analysis for Strategic Growth
Lecture 41 AI-Driven Scenario Generation
Lecture 42 Case Study: AI-Driven Scenario Generation: Transforming Financial Modeling
Lecture 43 Evaluating AI-Generated Scenarios
Lecture 44 Case Study: Evaluating AI Scenarios in Strategic Decision-Making
Lecture 45 Applications of Scenario Analysis
Lecture 46 Case Study: TechNova's AI-Enhanced Scenario Analysis: Navigating Uncertainty
Lecture 47 Scenario Analysis in Financial Planning
Lecture 48 Case Study: Revolutionizing Scenario Analysis
Lecture 49 Section Summary
Section 6: Risk Assessment with Generative AI
Lecture 50 Section Introduction
Lecture 51 Fundamentals of Risk Assessment
Lecture 52 Case Study: Transforming Risk Assessment
Lecture 53 AI Approaches to Risk Scoring
Lecture 54 Case Study: AI Revolutionizing Risk Assessment
Lecture 55 Predicting Financial Risks with AI
Lecture 56 Case Study: Enhancing Financial Risk Assessment with AI
Lecture 57 Risk Scenario Simulations
Lecture 58 Case Study: Navigating Risks with AI: GlobalTech's Strategic Expansion
Lecture 59 Risk Assessment Frameworks
Lecture 60 Case Study: Integrating Generative AI in Finance: Transforming Risk Assessment
Lecture 61 Section Summary
Section 7: Financial Statement Analysis and AI Insights
Lecture 62 Section Introduction
Lecture 63 Overview of Financial Statements
Lecture 64 Case Study: Transforming Financial Analysis: Leveraging AI at QuantumTech
Lecture 65 AI for Enhanced Financial Analysis
Lecture 66 Case Study: AI-Driven Financial Analysis: Transforming Insights
Lecture 67 Interpreting AI-Generated Financial Insights
Lecture 68 Case Study: FinCorp's AI Integration: Transforming Financial Analysis
Lecture 69 Ratio Analysis with AI Assistance
Lecture 70 Case Study: AI-Enhanced Ratio Analysis: Transforming Financial Insights
Lecture 71 Applying AI to Historical Financial Data
Lecture 72 Case Study: Unlocking AI's Transformative Power in Financial Statement Analysis
Lecture 73 Section Summary
Section 8: Asset Valuation and AI-Driven Insights
Lecture 74 Section Introduction
Lecture 75 Basics of Asset Valuation
Lecture 76 Case Study: Enhancing Asset Valuation with AI
Lecture 77 AI in Real Estate and Stock Valuation
Lecture 78 Case Study: Harnessing AI for Transformative Asset Valuation
Lecture 79 Dynamic Valuation Models with AI
Lecture 80 Case Study: AI-Driven Dynamic Valuation
Lecture 81 Risk-Adjusted Returns Using AI
Lecture 82 Case Study: Optimizing Risk-Adjusted Returns
Lecture 83 AI in Future Valuation Trends
Lecture 84 Case Study: Revolutionizing Asset Valuation
Lecture 85 Section Summary
Section 9: Portfolio Management and Optimization with AI
Lecture 86 Section Introduction
Lecture 87 Introduction to Portfolio Theory
Lecture 88 Case Study: Leveraging Generative AI and Portfolio Theory
Lecture 89 AI for Portfolio Diversification
Lecture 90 Case Study: Enhancing Portfolio Diversification with AI
Lecture 91 Optimizing Asset Allocation with AI
Lecture 92 Case Study: Harnessing AI for Precision Asset Allocation
Lecture 93 Real-Time Portfolio Adjustments
Lecture 94 Case Study: Optimizing Portfolio Management with Generative AI
Lecture 95 Portfolio Risk Management with AI
Lecture 96 Case Study: AI Revolutionizes Portfolio Risk Management
Lecture 97 Section Summary
Section 10: Stress Testing Financial Models with AI
Lecture 98 Section Introduction
Lecture 99 Purpose of Stress Testing
Lecture 100 Case Study: Enhancing Financial Resilience
Lecture 101 AI Approaches to Stress Testing
Lecture 102 Case Study: Harnessing AI for Enhanced Stress Testing
Lecture 103 Applying AI to Financial Shocks
Lecture 104 Case Study: Enhancing Financial Resilience
Lecture 105 Analyzing Stress Test Results
Lecture 106 Case Study: Transforming Stress Testing
Lecture 107 Stress Testing in Market Contexts
Lecture 108 Case Study: Leveraging Generative AI for Robust Financial Stress Testing
Lecture 109 Section Summary
Section 11: Advanced Predictive Analytics in Finance
Lecture 110 Section Introduction
Lecture 111 Machine Learning vs. Generative AI
Lecture 112 Case Study: Harnessing AI: Transforming Financial Modeling with Machine Learning
Lecture 113 Advanced Forecasting Techniques
Lecture 114 Case Study: Integrating Advanced AI for Enhanced Financial Market Forecasting
Lecture 115 Predictive Accuracy Improvement with AI
Lecture 116 Case Study: Unlocking AI's Potential: FinEdge Solutions' Journey
Lecture 117 Hyperparameter Tuning in Financial Models
Lecture 118 Case Study: Optimizing Financial Models with AI
Lecture 119 Refining Predictive Algorithms
Lecture 120 Case Study: Enhancing Financial Predictions with Advanced Algorithmic Strategies
Lecture 121 Section Summary
Section 12: Regulatory and Ethical Considerations
Lecture 122 Section Introduction
Lecture 123 Overview of Financial Regulations
Lecture 124 Case Study: Balancing Innovation and Compliance
Lecture 125 AI Compliance and Risk Mitigation
Lecture 126 Case Study: Navigating AI Compliance and Risk
Lecture 127 Ethical Implications in AI-Driven Finance
Lecture 128 Case Study: Navigating AI Ethics in Finance
Lecture 129 Privacy and Security in Financial AI
Lecture 130 Case Study: Balancing AI Innovation with Privacy and Security
Lecture 131 Addressing Ethical Challenges
Lecture 132 Case Study: Navigating Ethical Challenges in AI-Driven Financial Modeling
Lecture 133 Section Summary
Section 13: Real-Time Financial Data Integration
Lecture 134 Section Introduction
Lecture 135 Connecting Real-Time Data Sources
Lecture 136 Case Study: Harnessing Real-Time Data and AI for Enhanced Financial Modeling
Lecture 137 AI in High-Frequency Trading Models
Lecture 138 Case Study: Harnessing AI for Strategic Advantage in High-Frequency Trading
Lecture 139 AI-Enhanced Real-Time Data Analysis
Lecture 140 Case Study: AI-Enhanced Real-Time Data Analysis
Lecture 141 Applying Generative AI to Market Movements
Lecture 142 Case Study: Leveraging Generative AI for Enhanced Trading Strategies
Lecture 143 Real-Time Model Integration
Lecture 144 Case Study: Revolutionizing Financial Modeling: Real-Time Data Integration
Lecture 145 Section Summary
Section 14: Automating Financial Reports with Generative AI
Lecture 146 Section Introduction
Lecture 147 Automating Report Generation
Lecture 148 Case Study: Transforming Financial Modeling
Lecture 149 Enhancing Report Quality with AI
Lecture 150 Case Study: Transforming Financial Reporting
Lecture 151 Building Interactive Financial Dashboards
Lecture 152 Case Study: AI-Powered Dashboards: Transforming Financial Reporting
Lecture 153 AI-Driven Insights in Reports
Lecture 154 Case Study: AI-Driven Insights Revolutionize Financial Reporting
Lecture 155 Streamlining Reporting Workflows
Lecture 156 Case Study: Revolutionizing Financial Reporting: Harnessing Generative AI
Lecture 157 Section Summary
Section 15: Integrating AI in Decision-Making Processes
Lecture 158 Section Introduction
Lecture 159 Financial Decision-Making with AI
Lecture 160 Case Study: Harnessing AI for Enhanced Financial Decision-Making
Lecture 161 AI and Human Collaboration in Finance
Lecture 162 Case Study: AI-Driven Innovation in Finance
Lecture 163 Interpreting AI Recommendations
Lecture 164 Case Study: Integrating AI for Strategic Financial Decisions
Lecture 165 AI in Board-Level Decision Processes
Lecture 166 Case Study: AI-Driven Transformation: GlobalMart's Strategic Decision-Making
Lecture 167 Decision-Making Models with AI
Lecture 168 Case Study: AI-Driven Financial Modeling: Transforming Market Predictions
Lecture 169 Section Summary
Section 16: Future Trends and Innovations in Financial AI
Lecture 170 Section Introduction
Lecture 171 Future of Generative AI in Finance
Lecture 172 Case Study: Harnessing Generative AI for Sustainable Growth
Lecture 173 Emerging Technologies in Financial Modeling
Lecture 174 Case Study: Transforming Financial Modeling
Lecture 175 Ethical AI Development in Finance
Lecture 176 Case Study: Navigating Ethical AI in Finance
Lecture 177 Preparing for AI Disruptions in Finance
Lecture 178 Case Study: Integrating Generative AI
Lecture 179 AI in Sustainable and Green Finance
Lecture 180 Case Study: AI-Driven Innovations Transforming Sustainable Finance
Lecture 181 Section Summary
Section 17: Course Summary
Lecture 182 Conclusion
Aspiring financial analysts looking to integrate AI into financial modeling.,Finance professionals aiming to enhance decision-making with AI insights.,Students interested in foundational knowledge of AI-driven financial tools.,Data analysts seeking skills in AI-enhanced forecasting and risk assessment.,Business strategists aiming to incorporate generative AI in financial planning.,Professionals curious about AI's role in asset valuation and portfolio management.,Those interested in ethical and regulatory aspects of AI in financial contexts.

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Financial Modeling With Generative Ai Certification

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