Machine learning applications in finance

  • 4 days
  • Quant & model risk
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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 

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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

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 apply case studies that include 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 tutors, 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 


April 24–26, 2023

Time zones: Emea / Apac

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April 24 - 26, 2023

08:00 am - 11:00 am




Earlybird Price

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