Machine learning applications in finance

  • 3 days
  • Quant & model risk
View Agenda

Key reasons to attend

  • Identify current industry data-driven approaches 

  • Understand machine learning techniques used to maximise profits 

  • Align supervised and unsupervised learning methods

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Customised Solutions

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Working with the portfolio of expert tutors and’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. 

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About the course

This interactive learning event allows participants to learn the best practices of machine learning by focusing on supervised learning models, neural nets and further machine learning methods. Participants will identify core components essential to a successful machine leaning application. 

Led by the expert tutor, sessions will provide practical insights on the challenges machine learning presents for financial institutions. Interactive sessions will connect the expert tutor and participants through active discussion, Q&As and practical case studies. 

Participants will deep-dive into the application of machine learning in risk management and strengthen their understanding of integrating data science teams into the machine learning process. 

A basic understanding of statistics and data manipulation is required for participation in this event. 

Flexible pricing options:

  1. Early bird rate - book in advance and save $200 

  2. 3for2 group rate - book 3 delegates for the price of 2 and save over $2000 

  3. Season Tickets - book a team of 10 or more and save up to 50%

Learning objectives

  • Assess recent advances in sequential learning and deep learning

  • Interpret the early financial applications of machine learning

  • Employ the best practices of explainability and interpretability of AI models

  • Apply back-testing strategies with machine learning

  • Utilise alternative data that helps capture valuable information

  • Approach finance and regulatory compliance from different types of machine learning perspectives

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 


April 17–19, 2023

Time zones: Emea / Apac

Start time: 07:45 BST / 14:45 HKT
Finish time: 11:30 BST / 18:30 HKT


  • Introduction to machine learning in finance

  • Types of machine learning applied to financial data

  • Deep learning and sequential learning

  • Alternative data and natural language processing (NLP)

  • Investment strategies with machine learning

  • Graphical machine learning in finance

  • Integrating machine learning in risk management

  • Explainable AI in finance

  • Applying machine learning in practice

View detailed agenda


Eric Tham Risk Learning Faculty

senior lecturer, data science and fintech

Pre-reading materials

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April 17 - 19, 2023

07:45 am - 11:30 am




Earlybird Price

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Enquire about:

  • Agenda and registration process
  • Group booking rates
  • Customisation of this programme
  • Season tickets options
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