Machine Learning in Finance
Expand your knowledge of the recent trends, development, and application of machine learning in finance.
This virtual course, spread over four days, will provide attendees with a practical understanding of machine learning applications by exploring key theories, models and more advanced tools in machine learning solutions.
Delivered through practical, 60-minute sessions this course will provide insight into strategies for integrating ML solutions within your organisation as well as the impact of big data and risk on ML.
This course will build on attendees’ knowledge of neural nets and reinforcement learning while providing the opportunity for participants to interact with course leaders to explore different ML models, natural language processing and the impact of ML in portfolio construction and optimisation.
- Understand the recent trends in ML application
- Address the challenges presented by data analysis and big data for ML
- The impact ML has on portfolio optimisation
- How to apply neural networks in your organisation
- Understand natural language processing models
- The application of machine learning in banking, risk management and modelling
Relevant departments may include but are not limited to:
- Quantitative analysis
- Financial engineering
- Quantitative modelling
- Data science
- Machine learning
- Portfolio management
- Model risk
- Risk management
Our live, virtual training courses have been designed to engage and inspire you. Much more than a webinar, our approach includes:
- Technical content compressed into 60-minute interactive sessions and spread out over two, three or four days
- Facilitated collaboration including Q&A, interactive polling and group workshops
- Live interaction with subject matter experts – get your questions answered in real time
- Receive comprehensive course materials and supporting content from Risk.net to reinforce your learning
- Stay connected with other learners and extend your network by joining our dedicated LinkedIn group for course participants
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.
Big data architect
Bank of America
Ranko Mosic is a consultant specializing in AI/ML applied to finance. He is helping clients with ML/AI concepts, use case identification, feature selection, algorithm selection and build, technical assessment, recommendations and implementation.
He advised State Street Corporation, Bank of America and other clients on ML/AI and Big Data initiatives.
Senior director, enterprise model risk management
Greg is Senior Director in Enterprise Model Risk Management at RBC. He has a decade of experience in market and model risk management, with specialization in enterprise and retail risk. In his present role, Greg is leading efforts related to responsible AI practices, as well as development of validation techniques both for AI and using AI.
Senior manager AI research - enterprise model risk management
CPD / CPE Accreditation
This course is CPD (Continued Professional Development) accredited and will allow you to earn up to 12 credits. One credit is awarded for every hour of learning at the event.
This course is CPE (Continuing Professional Education) accredited and will allow you to earn up to 12 credits. One credit is awarded for every hour of learning at the event in accordance with the standards of the National Registry of CPE Sponsors.