Machine learning applications in financeView Agenda
Key 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
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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.
- Early-bird rate: save up to $800 per person by booking in advance (refer to the booking section for the deadline)
- 3-for-2 rate: save over $2,000 by booking a group of three attendees (applicable to this course)
- Subscriber reward: save 30% off the standard rate if you are a Risk.net subscriber (use code SUB30)
- Season tickets: save over $1,000 per person by booking 10 or more tickets (available on selection of courses)
*The 30% subscriber reward discount is applicable only to current Risk.net subscribers. If this criteria is not met, we reserve the right to cancel the booking and issue an invoice for the correct rate. Discounts cannot be applied to already registered participants.
- 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
June 11–13, 2024
Live online. Timezones: Emea/Apac
- 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
This course is CPD (Continued Professional Development) accredited. One credit is awarded for every hour of learning at the event.
The Risk.net resources below have been selected to enhance your learning experience:
- Model risk management is evolving: regulation, volatility, machine learning and AI
- BloombergGPT: Terminal giant enters the LLM race
- Explainable artificial intelligence for credit scoring in banking
- Regtech, Suptech and Beyond: Innovation in Financial Services
- Data Science in Economics and Finance for Decision Makers
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