AI in Financial Analysis
Professor
Edgars Voļskis
About the professor
Edgars Volskis is Head of Risk and Financial Advisory Department at BDO Latvia. Edgars has 25 years of experience in international consulting in the financial sector. At the same time, Edgars is a lecturer at RBS, teaching financial and management accounting courses in the BBA, MBA and EMBA programmes, as well as involved in the RBS AI Studio development project. Edgars attended the School of Computer Science and Engineering at the University at Buffalo, USA, where he studied several computer science disciplines, including the AI Startup course.
Course Description
This course empowers financial professionals to harness the transformative potential of artificial intelligence. By providing a solid foundation in AI concepts and their practical applications, participants will gain the ability to enhance decision-making, forecasting, and risk management within their organizations. Through a blend of theoretical knowledge and hands-on application, this course equips financial professionals with the skills to identify, evaluate, and implement AI solutions that drive business value and optimize financial performance.
Course Goals
Introduce participants to the basics of AI and its application in financial management.
Develop an understanding of how AI can be used to improve financial forecasting and analysis.
Demonstrate the use of AI tools and techniques in financial risk management and decision making.
Provide practical knowledge of AI integration in financial management processes.
After the course participants will be able to:
- Understand AI fundamentals
- Enhance your decision-making and forecasting
- Gain practical AI application skills within the field of finance
- Optimize financial performance
Course will be:
Online and In Person
Prerequisites
Basic knowledge in financial management and analysis.
Interest in technology and its application in business.
No prior knowledge of artificial intelligence is required.
Teaching methods used:
Theoretical lectures with practical examples.
Demos with real AI tools and platforms.
Group discussions and case studies.
Practical tasks to apply the acquired knowledge.
Lecture 1
November 7th:
1. Introduction to artificial intelligence and its role in financial management
Fundamentals and history of development of artificial intelligence.
Application possibilities of AI in various fields of financial management.
Examples from industry: How AI is being used in the financial sector.
2. Artificial intelligence technologies in financial forecasting and analysis
AI models in financial forecasting (eg time series analysis).
Data analysis and interpretation using AI.
Hands-on demo: Financial forecasting model with AI tool.
Lecture 2
November 21st:
3. Application of AI in financial risk management and decision-making
Risk modeling with AI: How to predict and mitigate financial risks.
AI decision support systems.
Practical exercise: Risk assessment using the MI model.
4. AI integration in financial management processes
AI technology implementation strategy in the organization.
Challenges and opportunities in AI integration.
Panel discussion: AI implementation examples and experiences.
Professor
Edgars Voļskis
About the professor
Edgars Volskis is Head of Risk and Financial Advisory Department at BDO Latvia. Edgars has 25 years of experience in international consulting in the financial sector. At the same time, Edgars is a lecturer at RBS, teaching financial and management accounting courses in the BBA, MBA and EMBA programmes, as well as involved in the RBS AI Studio development project. Edgars attended the School of Computer Science and Engineering at the University at Buffalo, USA, where he studied several computer science disciplines, including the AI Startup course.
Course Description
This course empowers financial professionals to harness the transformative potential of artificial intelligence. By providing a solid foundation in AI concepts and their practical applications, participants will gain the ability to enhance decision-making, forecasting, and risk management within their organizations. Through a blend of theoretical knowledge and hands-on application, this course equips financial professionals with the skills to identify, evaluate, and implement AI solutions that drive business value and optimize financial performance.
Course Goals
Introduce participants to the basics of AI and its application in financial management.
Develop an understanding of how AI can be used to improve financial forecasting and analysis.
Demonstrate the use of AI tools and techniques in financial risk management and decision making.
Provide practical knowledge of AI integration in financial management processes.
After the course participants will be able to:
- Understand AI fundamentals
- Enhance your decision-making and forecasting
- Gain practical AI application skills within the field of finance
- Optimize financial performance
Course will be:
Online and In Person
Prerequisites
Basic knowledge in financial management and analysis.
Interest in technology and its application in business.
No prior knowledge of artificial intelligence is required.
Teaching methods used:
Theoretical lectures with practical examples.
Demos with real AI tools and platforms.
Group discussions and case studies.
Practical tasks to apply the acquired knowledge.
Lecture 1
November 7th:
1. Introduction to artificial intelligence and its role in financial management
Fundamentals and history of development of artificial intelligence.
Application possibilities of AI in various fields of financial management.
Examples from industry: How AI is being used in the financial sector.
2. Artificial intelligence technologies in financial forecasting and analysis
AI models in financial forecasting (eg time series analysis).
Data analysis and interpretation using AI.
Hands-on demo: Financial forecasting model with AI tool.
Lecture 2
November 21st:
3. Application of AI in financial risk management and decision-making
Risk modeling with AI: How to predict and mitigate financial risks.
AI decision support systems.
Practical exercise: Risk assessment using the MI model.
4. AI integration in financial management processes
AI technology implementation strategy in the organization.
Challenges and opportunities in AI integration.
Panel discussion: AI implementation examples and experiences.