Course Description
This course provides a structured and practical introduction to Artificial Intelligence (AI) and Machine Learning (ML) applications in financial institutions, focusing on how modern data-driven techniques are used across finance functions.
Participants are introduced to AI and ML fundamentals, key machine learning building blocks, and recent tools used in financial forecasting and analytics. The program combines conceptual understanding with hands-on exposure to financial data, covering forecasting techniques, data preparation methods, and model selection approaches.
It further explores real-world AI and ML use cases in risk management, investment and portfolio management, banking stress testing, and trading, while also addressing data and big data challenges faced by financial institutions
Who Should Attend
This course is suitable for professionals working in or supporting financial institutions who seek to understand or apply AI and ML techniques, including:
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Banking professionals involved in risk management, investments, trading, or stress testing
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Finance, treasury, and portfolio management professionals
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Risk, compliance, and governance professionals seeking insight into AI-driven analytics
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Data, analytics, and technology professionals supporting financial functions
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Professionals interested in practical AI and ML applications in financial services
Learning Outcomes
Upon completion of this course, participants will be able to:
By the end of this course, participants will be able to:
- Understand core concepts of Artificial Intelligence and Machine Learning and their relevance to financial institutions
- Explain key machine learning building blocks and modern forecasting techniques
- Apply data preparation methods to financial datasets, including handling missing data
- Use machine learning models to predict future financial values using single and multiple variables
- Recognize challenges related to AI, ML, and big data in financial environments
- Compare and select appropriate data preparation techniques and forecasting models
- Understand practical AI and ML applications in:
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Risk management
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Investment and portfolio management
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Banking stress testing
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Trading activities
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NASBA Sponsor
Governance Dynamics is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.NASBARegistry.org.
Additional Information
Registration and Attendance Requirements: Click the “Inquire Now” button to register for the Certified AI and Machine Learning for Financial Institutions Program. In order to be awarded the full credits, you must respond to at least three polling or live questions every 50 minutes.
- Instructional Delivery Method: Group Live
- Field of Study: Information Technology
- Program Level: Intermediate
- Prerequisites: None
- Advanced Preparation: None
For more information regarding refund, complaint, and program cancellation policies, please contact the training department at info@governance-dynamics.com.
Details
- Date August 3-4, 2026
- Duration 2 Days
- Location Riyadh
- Program Level Intermediate
- CPE Credit 10 CPEs
- Certificate of Completion
Bilal Sidani
Training Advisor
Haya El Chimaitilly
Training Advisor
Course Outline
- Introduction to Machine Learning Building Blocks
- Understand the motivation for applying AI in finance-related Business
- Recent Tools and Applications of AL and ML
- Get hands‐on financial forecasting experience using machine learning with Python, Keres, and pandas
- Use a variety of data preparation methods with financial data
- Predict future values based on single and multiple values
- Apply key modern Machine Learning methods for forecasting
- Understand the process behind choosing the best-performing data preparation method and model
- Grasp Machine Learning forecasting on a specific real‐world financial data
- AI and ML Applications in Risk Management
- AI and ML Applications in Investment and portfolio Management
- AI and ML Applications in Banking Stress Test ‐ AI and ML Applications in Trading
- AI, ML and Big Data challenge
