Machine learning applications in finance

  • 4 days
  • Quant & model risk
View Agenda

Key reasons to attend

  • Understand the application of effective machine learning for financial risk  

  • Explore different types and challenges of machine learning models  

  • Learn frameworks for implementing machine learning models 

Find out more

Customised Solutions

Does your team require a tailored learning solution on this or any other topic?

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. 

Find out more

About the course

During this interactive learning event, participants will identify the current applications of machine learning in finance, as well as the core components essential to successful ML models.  
Sessions will focus on the importance of supervised and unsupervised learning models, neural nets, and other ML methods, as well as use cases including applications including default prediction, volatility prediction, fraud detection, model risk management, and exploring the skills necessary to implement ML models effectively. 

During this event, participants will deep-dive into the application of ML in risk management and strengthen their understanding on how to integrate data science teams into the ML process. Led by expert tutor sessions will provide practical insights on the challenges and limitations ML presents for financial institutions. 

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

Learning objectives

  • Identify core components of the machine learning (ML) process  

  • Apply ML methods in risk management 

  • Interpret volatility prediction with neural nets 

  • Mitigate challenges in anomaly detection  

  • Achieve ML explainability in finance 

  • Integrate data science teams in the organisation  

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 


October 11 - 14, 2022

Time zones: EMEA / Americas
Start time: 14.00 BST / 9.00 EDT
Finish time: 16.15 BST / 11.15 EDT


  • Introduction to machine learning and financial applications

  • Supervised learning models  

  • Applying ML methods in risk management 

  • Neural nets and deep learning 

  • Unsupervised methods and reinforcement learning   

  • Anomaly detection

  • Explainability in machine learning  

  • Implementing ML models

View detailed agenda

October 25 - 28, 2022

Time zones: APAC
Start time: 09:00 HKT/SGT
Finish time: 11:15 HKT/SGT

  • Session 1: Machine learning in finance: an introduction 

  • Session 2: Types of machine learning - supervised and unsupervised learning applied to financial data 

  • Session 3: Recent advances - deep learning and sequential learning 

  • Session 4: Alternative data for traders and natural language processing 

  • Session 5: Investment strategies with machine learning 

  • Session 6: Graphical ML in finance 

  • Session 7: Explainable AI in finance  

  • Session 8: Applying machine learning in practice 

View detailed agenda


Course tutor - October 11–14, 2022

  • Jesús Calderón, managing director, Maclear Data Solutions 

Course tutor - October 25–28, 2022

  • Eric Tham, senior lecturer in data science, PhD Finance 


October 11 - 14, 2022

02:00 pm - 04:15 pm




October 25 - 28, 2022

02:00 am - 04:15 am



Book now

Enquire about:

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

You need to sign in to use this feature. If you don’t have a account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here: