Machine learning applications in finance
View AgendaKey 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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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*
- 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 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
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
Tutors
![](/sites/default/files/styles/people_image_250x250/public/2024-01/Federico%20Crecchi.jpg.webp?itok=jhlu4QSa)
Federico Crecchi
Associate Partner and co-head of the data science practice
Prometeia
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.
![](/sites/default/files/styles/people_image_250x250/public/2024-02/Valerio%20Consorti%20-%20PP_0.jpg.webp?itok=Zo6lEA0t)
Valerio Consorti
Principal Data Scientist
Prometeia
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:
- Model risk management is evolving: regulation, volatility, machine learning and AI
- BloombergGPT: Terminal giant enters the LLM race
- Explainable artificial intelligence for credit scoring in banking
- Regtech, Suptech and Beyond: Innovation in Financial Services
- Data Science in Economics and Finance for Decision Makers
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