Machine learning applications in finance
View AgendaKey reasons to attend
- Discover how financial institutions can leverage machine learning to drive growth and efficiency
- Explore supervised and unsupervised learning models
- Examine how to build explainability in a machine learning project
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About the course
This course provides practical insights into machine learning applications in finance, with a focus on risk management. Participants will gain expertise in the latest machine learning techniques, from supervised and unsupervised learning to deep learning and reinforcement learning.
Attendees will learn how to implement machine learning models that are explainable and transparent, ensuring alignment with regulatory expectations. Our quantitative finance expert will leverage his industry knowledge to analyse key machine learning challenges, such as handling imbalanced datasets and anomaly detection.
Participants will leave this course with the practical skills to apply machine learning techniques to financial processes.
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
- Understand core machine learning concepts
- Assess how ensemble methods are used to improve model performance
- Discuss how machine learning methods can be applied in risk management
- Explore the applications of deep learning
- Learn how to enhance risk management with reinforcement learning
- Develop strategies to enhance anomaly detection methods
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
July 22–24, 2025
Live online. Timezones: Emea/Americas
- Introduction to machine learning and financial applications
- Supervised learning models
- Applying machine learning methods in risk management
- Neural nets and deep learning
- Unsupervised methods and reinforcement learning
- Anomaly detection
- Explainability in machine learning
- Explainable artificial intelligence (AI) in finance
- Implementing machine learning models
Tutor:
- Jesús Calderón, Managing director, Maclear Data Solutions
Tutors
Jesús Calderón
Managing director
Maclear Data Solutions
Jesús Calderón advises Canadian and international clients in the financial services and energy industries on the implementation of data-driven solutions for risk management in banking, insurance, capital markets, and energy trading, as well as anti-money laundering and regulatory activities. Jesús has over twelve years of experience in risk management, internal audit, and fraud investigations, where he has specialized in the application of data science and machine learning methods to optimize risk control activities and examinations.
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 articles.
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