Machine Learning in Finance

Explore practical examples of machine learning successes, challenges, and advancements in risk management

Advancements in machine learning (ML) applications have developed significantly in banking, risk management & modelling. During this 4-day, interactive virtual course, explore these advancements and what opportunities and limitations they present for financial institutions.

Subject-matter expert Eric Tham will focus on practical examples and use cases of ML and develop participants knowledge on key topics: unsupervised and supervised learning, alternative data, and explainable AI.

The course will develop participants understanding of the behavioural and quantitative finance applications of ML in finance and the addition of a practical case study on ML use cases will allow participants to apply the theoretical knowledge taught in the course sessions to real world examples.

What will you learn?
  • What the impacts of recent approaches to finance and regulatory compliance for unsupervised learning entail 

  • Understand the recent advances in sequential and deep learning

  • Develop skills to create and apply investment strategies with ML and graphical ML

  • Identify the challenges of using alternative data with practical examples

  • Understand what the metrics of explainability/ interpretability of AI models are

  • What applying ML for finance looks like in practice 

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

Pricing options

We offer flexible pricing options for this course:

  • Early bird rates 

  • Group rate

  • Enterprise rates

  • Visit registration page for further information

  • Subscribe to receive Risk Training Newsletter and avoid missing out on additional savings

Content support

For this course we have collated a selection of articles from Risk.net to supplement your learning.

Risk Training is a part of Risk.net - the world’s leading source of in-depth news and analysis on risk management, derivatives and complex finance.

View articles here

 

Eric Tham

Senior lecturer, data science

Australian University

Dr. Eric Tham is a senior lecturer in data science in a top Australian University. Prior to joining academia, he has over 16 years of financial experience spanning quantitative development, risk management and Fintech. He has a Phd in Finance with research interests in asset pricing, AI/ML in Finance and behavioural finance. His extensive practical experiences have helped introduce a practical inclination to his teaching. 

His research interests are at the intersection of behavioural finance and AI/ML, and have been presented at the American Economics Association (AEA/AFA) meetings, Miami Behavioural Finance conference, the European Financial Management Association (EFMA) meetings, the Australasian Banking and Finance conference (ABFC) and the Risk management conference at NUS. He has also won a research grant from the Think Forward Initiative for his research on social media trust.

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CPD Accreditation

This course is CPD (Continued Professional Development) accredited and will allow you to earn up to 8 credits. One credit is awarded for every hour of learning at the event.

 

 

Live Virtual training courses

 

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