Machine Learning Use Cases in Finance
The success of machine learning, and in particular deep learning in image recognition and natural language processing applications, has created high expectations and their use has rapidly spread to many different areas. The financial sector is no exception and the last six years have seen an increase in these types of models in financial, banking and insurance contexts. Data science and advanced analytics teams in the financial and insurance community are implementing these models regularly and have found a place for them in their toolbox.
In this course, we will first present a review of some of the applications of machine learning and deep learning. We will then illustrate their use in financial applications through concrete examples that we have seen have sparked interest in the industry. Our examples will illustrate how we can add value through ad hoc construction of architectures rather than a simple exercise of replacing classical models with more complex ones, such as multi-layer networks.
We will see
- Neural network architectures on graphs to integrate new information dimensions in financial markets and bitcoin transactions
- Portfolio design using reinforcement learning and
- Natural Language Processing and information extraction methods from financial disclosures in the in an ESG and sustainable finance context
Upcoming start dates
- Self-paced Online
Who should attend?
These are the topics of each module:
- Module 1 - Introduction and Background
- Module 2 - Reminder Machine Learning and Deep Learning
- Module 3 - GNN in Finance
- Module 4 - ESG Evaluation
- Module 5 - Portfolio Design using Reinforcement Learning
- Module 6 - Conclusion
Course delivery details
This course is offered through Université de Montréal, a partner institute of EdX.
4-5 hours per week
- Verified Track -$150
- Audit Track - Free
Certification / Credits
What you'll learn
At the end of the MOOC, participants should be able to:
- Recognize when and how to use machine learning models according to the business context.
- Apply the best practices of machine learning and in particular of deep learning in a financial application context.
- Identify some models and architectures of deep networks that can be used to solve problems in finance and insurance:
- Graph neural networks in financial markets
- Reinforcement learning in portfolio optimization
- Information extraction and ESG metrics
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