Artificial intelligence in the financial sector

Course ID : AI-010

Duration In-class (в days) : 16 academic hours

Duration Online : 16 academic hours

Сurriculum : in-class, Virtual Instructor-Led Training - ONLINE

Overview

This course covers the practical aspects of using AI in the financial sector, from basic concepts to industry case studies. Participants will explore how AI technologies are used in the financial sector to optimize routine processes, including rapid data retrieval, calculation automation, and presentation preparation.

The program covers machine learning, large-scale language models, and predictive analytics, as well as tools for big data processing, decision making, and text processing.

The practical component is based on case studies of AI applications in the financial sector, from routine procedures to digital transformation.

This course will help students understand where AI application in the financial sector can yield measurable results, and where its implementation is risky and impractical.

Audience for this course

  • Executives
  • Analysts
  • Managers

Outline

  1. Introduction to Artificial Intelligence (AI)
  • The Concept of Artificial Intelligence: Definition, Key Development Areas
  • The Difference between AI and Traditional Automated Systems
  • General Classification of AI Technologies: Weak and Strong AI, Highly Specialized Systems
  • Machine Learning and Neural Networks: Concepts, Differences, and Applications
  1. What is Machine Learning: Operating Principles, Types of Learning
  • Neural Networks: Operating Principles, Architecture, and Applications
  • The Relationship between Concepts: AI, Machine Learning, and Neural Networks
  • Artificial Intelligence Tools
  1. An Overview of Modern AI Platforms and Solutions (Including Generative AI, LLM, Data Analysis Systems)
  • Large Language Models
  • Predictive Tools
  • Tools for Big Data Processing, Visualization, and Decision Making
  • Practice – Case Study
  • Prompt Engineering Basics
  1. What is a prompt and why is it needed?
  • Principles of creating effective prompts for solving applied problems
  • Practical examples: preparing reports, analyzing regulations, formulating solutions, working with documentation
  • Limitations and risks of using generative AI
  • Practice – Case Study
  1. Practical Application AI
  • Cases of using AI locally to solve routine tasks
  • Examples of using AI for digital transformation
  • Experience of leading global companies
  1. Interactive practice and group assignments
  • Working with real-life scenarios similar to your company’s activities — discussion and gathering hypotheses
  • Preparing cases and developing hypotheses in groups (2 groups) with instructors
  • Analysis of the generated cases (presentation of group results): identifying opportunities for AI implementation, discussion
  • Discussion of strategic initiatives and barriers to digital transformation

 

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