Machine Learning in Finance

Develop a comprehensive understanding of the applications and challenges associated when implementing ML models for your organisation.

Machine Learning in Finance

October 11–14, 2022

Time zone: EMEA / Americas

 

REGISTration

Key reasons to attend

  • Develop skills to overcome challenges when implementing ML models

  • Focus on the importance of supervised and unsupervised models 

  • Keep updated on latest ML core components and explainability

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Customised learning

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

Working with the portfolio of expert tutors and Risk.net’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 how

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Join us for this 4-day virtual course where participants will be able to understand the probability and statistics of machine learning, as well as identifying current applications of machine learning in finance.

Sessions will focus on the importance of supervised and unsupervised learning models for participants to develop a deep knowledge of new advancements. Discussion will also include the neural nets and volatility prediction, as well as learning and option pricing surrounding proper implementation within your organisation.

Our veteran speaker, Jesús Calderón, a managing director from Maclear Data Solution, will teach delegates practical insights on the challenges ML presents for financial institutions and what limitations can arise when applying ML methods in risk management. Advancements have been significantly developing in finance and financial regulation, and Calderón will focus on the importance of achieving ML explainability in a world that demands transparency. (or ‘where transparency is in high-demand). 

This training course will provide the roles, tasks, and skills necessary to implement machine learning models in banking, risk management, and modelling. 
 

Learn how to

  • Identify the core components of the machine learning (ML) process and current applications of machine learning in finance

  • Explain the importance of regression models, super vector machines, and neural nets and deep learning

  • Apply ML methods in risk management

  • Interpret volatility prediction with neural nets

  • Identify challenges in anomaly detection 

  • Achieve ML explainability in finance

  • Reference architecture and integrate data science teams in the organisation 

Who should attend

Relevant departments may include but are not limited to: 

  • Machine learning (ML)

  • Risk management

  • Portfolio management

  • Data science

  • Financial engineering 

  • Quantitative analytics 

  • Quantitative modelling

Content support

For this course we have collated a selection of articles from Risk.net to supplement your learning.

Risk Training is a part of Risk.net - the world’s leading source of in-depth news and analysis on risk management, derivatives and complex finance.

View articles here

Course tutor

Jesús Calderón

Managing director

Maclear Data Solutions

Jesús Calderón advises Canadian and international clients in the financial services and energy industries on the implementation of data-driven solutions for risk management in  banking, insurance, capital markets, and energy trading, as well as anti-money laundering and regulatory activities. Jesús has over twelve years of experience in risk management, internal audit, and fraud investigations, where he has specialized in the application of data science and machine learning methods to optimize risk control activities and examinations.

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Great experience interacting with delegates from various geographies and industries about ML based systematic trading strategies

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The training was very informative. I learned a lot from the course, and would definitely attend such course again

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I found the training to be very good. I thought the depth of coverage was appropriate

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A very professional and serious training course

Live Virtual training courses

 

Our live virtual training courses have been designed to engage and inspire you. Much more than a webinar, our approach includes:

  • Technical content compressed into 60-minute interactive sessions and spread out over two, three or four days

  • Facilitated collaboration including Q&A, interactive polling and group workshops

  • Live interaction with subject matter experts – get your questions answered in real time

  • Receive comprehensive course materials and supporting content from Risk.net to reinforce your learning

  • Stay connected with other learners and extend your network by joining our dedicated LinkedIn group for course participants

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