Strategic Financial Analysis through AI

Course Info

Date: Apr-07-2025

Length: 1 Week

City: Singapore

Fees: 4,590

Type: In Classroom

Available Dates

  • Jan-06-2025

    Singapore

  • Apr-07-2025

    Singapore

  • July-07-2025

    Singapore

  • Oct-06-2025

    Singapore

Dates in Other Venues

  • Dec-23-2024

    Dubai

  • Dec-30-2024

    Kuala Lumpur

  • Dec-30-2024

    Istanbul

  • Dec-30-2024

    Barcelona

  • Jan-06-2025

    Kuala Lumpur

  • Jan-06-2025

    Istanbul

  • Jan-06-2025

    Paris

  • Jan-06-2025

    Barcelona

  • Jan-06-2025

    Amsterdam

  • Jan-27-2025

    Dubai

  • Jan-27-2025

    London

  • Mar-31-2025

    Dubai

  • Mar-31-2025

    London

  • Apr-07-2025

    Amsterdam

  • Apr-07-2025

    Barcelona

  • Apr-07-2025

    Paris

  • Apr-07-2025

    Istanbul

  • Apr-07-2025

    Kuala Lumpur

  • May-26-2025

    London

  • May-26-2025

    Dubai

  • July-07-2025

    Istanbul

  • July-07-2025

    Barcelona

  • July-07-2025

    Kuala Lumpur

  • July-07-2025

    Amsterdam

  • July-07-2025

    Paris

  • July-28-2025

    London

  • July-28-2025

    Dubai

  • Sep-08-2025

    Dubai

  • Sep-08-2025

    London

  • Oct-06-2025

    Barcelona

  • Oct-06-2025

    Paris

  • Oct-06-2025

    Amsterdam

  • Oct-06-2025

    Istanbul

  • Oct-06-2025

    Kuala Lumpur

  • Nov-03-2025

    Dubai

  • Nov-03-2025

    London

Course Details

Course Outline

5 days course

Introduction to AI in Finance

 

  • Overview of AI in Financial Markets: Understanding the Impact and Evolution of AI in the Finance Industry
  • Key Areas: Understanding key areas where we can apply AI in the finance industry
  • AI Tools & Technologies: Exploring tools and technologies for driving financial services.
  • Challenges and Opportunities in Financial AI: Navigating data privacy, accuracy, and ethical issues.
  • Case Studies: Examples of successful AI integration in financial systems.

Python Basics & Data Exploration

 

  • Introduction to Python for Finance: Basics of Python programming focused on financial applications.
  • Exploratory Data Analysis (EDA): Techniques using Python to visualize and analyze financial datasets.
  • Python Libraries for Financial Analysis: Introduction to libraries like Pandas, NumPy, and Matplotlib.
  • Workshop: Hands-on session where participants use Python to perform EDA on a provided financial dataset.
  • Discussion: Best practices for data handling and analysis in finance.

Machine Learning Models & Applications  

 

  • Introduction to Machine Learning Models: Exploring ML Algorithms.
  • Hands-On Workshop: Building a machine learning model to predict financial trends (e.g., stock prices or market movements).
  • Model Evaluation and Validation: Techniques to test model accuracy and prevent overfitting.
  • Real-Time Example: Applying standard ML models to a current financial problem, analyzing the outcomes and strategies.
  • Q&A session: discussing the applications and limitations of standard ML models in finance.

Advanced ML with Deep Learning 


  • Understanding Deep Learning Concepts
  • Deep Learning Applications in Finance: Using CNNs and RNNs for more complex financial modelling such as credit scoring and fraud detection.
  • Hands-on Exercise: Participants develop a deep learning model to analyze high-dimensional financial data.
  • Case Study Discussion: Detailed review of a real-time application of complex ML in finance.
  • Deployment: Discussing how to integrate AI models in finance systems.

Introduction to LLM & Generative AI


  • Basics of Large Language Models and Generative AI: Understanding the fundamentals and the power of LLMs and generative AI in finance.
  • Applications in Financial Content Generation and Analysis: How LLMs can be used for generating financial reports, analyzing earnings calls, and more.
  • Hands-On Workshop: Using an open-source LLM to generate insights from financial texts.
  • Discussion on Future Trends: Exploring the evolving role of LLMs and generative AI in financial strategies.
  • Final Recap and Participant Presentations: Review of the course content with a session for participants to present their findings and discuss their learning experience.