Machine learning applications in finance

  • AI and machine learning
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Key reasons to attend

  • Learn to effectively integrate data science into a business
  • Acquire the skills to improve accuracy and effectiveness of models
  • Explore supervised and unsupervised learning 

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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 brings together an industry expert and course participants to focus on the intersection between machine learning and finance. Participants will discover the many applications of novel machine learning methods in risk management, focusing primarily on supervised learning models, neural nets and deep learning. 

Learn best practices for the integration of data science into a financial institution through active discussion and Q&As. Frequent challenges will be addressed regarding anomaly detection, lack of AI explainability and classifying a highly imbalanced dataset. Participants will come away with the necessary knowledge to measure the performance of machine learning models used for effective risk management. 

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

Pricing options:

  • Early-bird rate: save up to $800 per person by booking in advance (refer to the booking section for the deadline)
  • 3-for-2 rate: save over $2,000 by booking a group of three attendees (applicable to this course)
  • Subscriber reward: save 30% off the standard rate if you are a subscriber (use code SUB30)
  • Season tickets: save over $1,000 per person by booking 10 or more tickets (available on selection of courses)

*The 30% subscriber reward discount is applicable only to current subscribers. If this criteria is not met, we reserve the right to cancel the booking and issue an invoice for the correct rate. Discounts cannot be applied to already registered participants.

Learning objectives

  • Explore the fundamental components of machine learning
  • Discover the necessity of explainable artificial intelligence (AI) for accountability
  • Learn about anomaly detection in machine learning to mitigate risks
  • Gain insights into leveraging AI in financial forecasting with neural nets 
  • Consider the potential of reinforcement learning in risk management
  • Discover key considerations for accurate predictive models

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 


June 11–13, 2024

Live online. Timezones: Emea/Apac


  • Introduction to machine learning and financial applications 
  • Supervised learning models
  • Applying machine learning methods in risk management
  • Neural nets and deep learning 
  • Unsupervised methods and reinforcement learning  
  • Anomaly detection 
  • Explainability in machine learning 
  • Explainable artificial intelligence (AI) in finance  
  • Implementing ML models

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Federico Crecchi

Associate Partner and co-head of the data science practice


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Federico is an associate partner and the co-head of the data science practice at Prometeia, specializing in banking analytics. He holds an M.Sc. from the University of Chicago in Physics and is a CFA charterholder. Before joining Prometeia, he worked for the Generali Group in investments and data science. Federico has successfully overseen numerous AI projects within complex institutions, spanning areas such as risk analytics, CRM enhancement, fraud analytics, and intelligent process automation.

Valerio Consorti

Principal Data Scientist


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Valerio is a principal data scientist in Prometeia. He holds a PhD from Albert-Ludwigs-Universität (Germany). Before joining Prometeia he worked as researcher at the CERN International Laboratories (Switzerland) and as a data science R&D manager in Generali Group. He is lecturer of the Data Science course at the “Master in High Performance Computing” (Italy) sponsored by the international research institutes SISSA and ICTP. In Prometeia he leads and implement innovative projects leveraging AI techniques with focus on risk management in the banking sector.


This course is CPD (Continued Professional Development) accredited. One credit is awarded for every hour of learning at the event.

Pre-reading materials

The resources below have been selected to enhance your learning experience:

A subscription will provide you access to these articles. Alternatively, register for free to read two news articles a month.


June 11–13, 2024

Online, Emea/Apac


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

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