Machine learning applications in finance

  • AI and machine learning
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Key reasons to attend

  • Learn about the analytical process through data analysis  
  • Explore supervised and unsupervised learning  
  • Acquire the skills to understand and improve explainability  

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Customised solutions

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About the course

In this learning event, participants will learn to converge machine learning and finance by studying diverse applications of machine learning methods in risk management.  

Participants will explore supervised and unsupervised learning, addressing common challenges regarding dimensionality reduction, implications of artificial intelligence (AI) explainability and classifying a highly imbalanced dataset. Sessions conclude with a practical application that will equip participants with a wider knowledge in machine learning topics, including the discussion of short-term non-maturing deposits, liquidity forecast and the explainability of behavioural probability in a default model.  

Demo sessions addressing exploratory data analysis and supervised model training will solidify previously learned concepts and prepare participants to implement course topics in their everyday business.  

Note: 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*
  • 3-for-2 rate: save over $2,000 by booking a group of three attendees*
  • Subscriber reward: save 30% off the standard rate if you are a Risk.net subscriber*
  • Season tickets: cost-effective option for groups of 10 or more. Learn more

*T&Cs apply

Learning objectives

  • Explore the fundamental components of machine learning
  • Discover the importance of explainability in machine learning
  • Analyse cross-industry standards for data mining
  • Examine different types of architectures in deep learning
  • Address and manage class imbalance in supervised learning
  • Execute dimensionality reduction in unsupervised learning 

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  
  • Model risk
  • Compliance  

Agenda

October 8–10, 2024

Live online. Timezones: Emea/Americas

Sessions:

  • Introduction to machine learning and financial applications
  • The analytical process
  • Supervised learning: regression
  • Supervised learning: classification
  • Unsupervised learning
  • Deep learning
  • Explainability in machine learning
  • Demo session: a real life business application – part 1
  • Demo session: a real life business application – part 2

Tutors:

  • Federico Crecchi, Associate partner and co-head of the data science practice, Prometeia
  • Valerio Consorti, Principal data scientist, Prometeia

View detailed agenda

Tutors

Federico Crecchi

Associate Partner and co-head of the data science practice

Prometeia

View bio

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

Prometeia

View bio

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.

Accreditation

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

Pre-reading materials

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

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

Registration

October 8–10, 2024

Online, Emea/Americas

Price

$2,999

Early-bird Price

$2,199
Ends September 6
Book now

Enquire about:

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

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