Machine learning applications in finance
View AgendaKey reasons to attend
- Learn about the analytical process through data analysis
- Explore supervised and unsupervised learning
- Acquire the skills to understand and improve explainability
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About the course
In this learning event, participants will learn to converge machine learning and finance by studying diverse applications of machine learning methods in risk management.
Participants will explore supervised and unsupervised learning, addressing common challenges regarding dimensionality reduction, implications of artificial intelligence (AI) explainability and classifying a highly imbalanced dataset. Sessions conclude with a practical application that will equip participants with a wider knowledge in machine learning topics, including the discussion of short-term non-maturing deposits, liquidity forecast and the explainability of behavioural probability in a default model.
Demo sessions addressing exploratory data analysis and supervised model training will solidify previously learned concepts and prepare participants to implement course topics in their everyday business.
Note: a basic understanding of statistics and data manipulation is required for participation in this event.
Pricing options:
- Early-bird rate: save up to $800 per person by booking in advance*
- 3-for-2 rate: save over $2,000 by booking a group of three attendees*
- Subscriber reward: save 30% off the standard rate if you are a Risk.net subscriber*
- Season tickets: cost-effective option for groups of 10 or more. Learn more
*T&Cs apply
Learning objectives
- Explore the fundamental components of machine learning
- Discover the importance of explainability in machine learning
- Analyse cross-industry standards for data mining
- Examine different types of architectures in deep learning
- Address and manage class imbalance in supervised learning
- Execute dimensionality reduction in unsupervised learning
Who should attend
Relevant departments may include but are not limited to:
- Machine learning
- Risk management
- Portfolio management
- Data science
- Financial engineering
- Quantitative analytics
- Quantitative modelling
- Model risk
- Compliance
Agenda
Sessions:
- Introduction to machine learning and financial applications
- The analytical process
- Supervised learning: regression
- Supervised learning: classification
- Unsupervised learning
- Deep learning
- Explainability in machine learning
- Demo session: a real life business application – part 1
- Demo session: a real life business application – part 2
Accreditation
This course is CPD (Continued Professional Development) accredited. One credit is awarded for every hour of learning at the event.
Pre-reading materials
The Risk.net resources below have been selected to enhance your learning experience:
- The fine line with LLMs: financial institutions’ cautious embrace of AI
- Benchmarking machine learning models to predict corporate bankruptcy
- How Bloomberg got liquidity seekers to trust its machine learning models
A Risk.net subscription will provide you access to these articles. Alternatively, register for free to read two news articles a month.
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