Machine learning applications in finance
View AgendaKey reasons to attend
-
Learn to effectively integrate data science into a business
-
Acquire the skills to improve accuracy and effectiveness of models
-
Explore supervised and unsupervised learning
Customised solutions
Does your team require a tailored learning solution on this or any other topic?
Working with the portfolio of expert tutors and Risk.net’s editorial team, we can develop and deliver a customised learning to make the most impact for your team, from initial assessment to final review.
About the course
This interactive learning event brings together an industry expert and course participants to focus on the intersection between machine learning and finance. Participants will discover the many applications of novel machine learning methods in risk management, focusing primarily on supervised learning models, neural nets and deep learning.
Learn best practices for the integration of data science into a financial institution through active discussion and Q&As. Frequent challenges will be addressed regarding anomaly detection, lack of AI explainability and classifying a highly imbalanced dataset. Participants will come away with the necessary knowledge to measure the performance of machine learning models used for effective risk management.
A basic understanding of statistics and data manipulation is required for participation in this event.
Flexible pricing options:
-
Early-bird rate: book in advance and save $200
-
3-for-2 group rate: book three delegates for the price of two and save more than $2,000
-
Season tickets: book a team of 10 or more and save up to 50%
Learning objectives
-
Explore the fundamental components of machine learning
-
Discover the necessity of explainable artificial intelligence (AI) for accountability
-
Learn about anomaly detection in machine learning to mitigate risks
-
Gain insights into leveraging AI in financial forecasting with neural nets
-
Consider the potential of reinforcement learning in risk management
-
Discover key considerations for accurate predictive models
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
Agenda
December 11–13, 2023
Live online. Timezones: Emea/Americas
Sessions:
-
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 ML 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:
-
Model risk management is evolving: regulation, volatility, machine learning and AI
-
Explainable artificial intelligence for credit scoring in banking
-
Regtech, Suptech and Beyond: Innovation in Financial Services
A Risk.net subscription will provide you access to these articles. Alternatively, register for free to read two news articles a month.
Expand your learning
Events
The premier meeting place for the risk community. Providing clarity and guidance on the fast-changing regulatory landscape of capital, credit and market risk, liquidity and derivates use.
Journals
Risk Journals deliver academically rigorous, practitioner-focused content and resources for the rapidly evolving discipline of financial risk management.
Books
Risk Books are authored by leading professionals and academics. With over 150 books spanning 1,000s of chapters, our publications team is committed to connecting readers with these world class experts.
Glossary
Regularly updated by our team analysts, journalists and producers, our glossary demystifies the terminology and acronyms used in risk management, risk transfer, policy, technology and innovation.