Machine Learning for Risk Managers

This course is a must-have for all risk managers wishing to understand the basics of machine learning

Machine learning tools can detect and prevent risk incidents, while also alleviating many of the menial tasks of risk managers. This course gives a broad overview of the potential uses and challenges of AI in risk management. Through relevant case studies, attendees will learn how real risk management AI tools are built and monitored in firms.  

This course explains how risk managers can ensure their firms capitalise on the benefits and avoid the risks associated with these tools.

The engaging, in-depth sessions are delivered in 60-minute presentations across three days, allowing numerous opportunities for attendees to interact with the course tutors

This course is a must-have for all risk managers wishing to understand the benefits and risks of machine learning. It provides an overview of the current uses of machine learning in risk management including two beneficial-use case studies. Attendees will leave with a thorough knowledge of how to make use of potential benefits and avoid the risks associated with these tools. 

No previous background in machine learning is required.

What will you learn?
  • A comprehensive overview of the current uses of machine learning in risk management 

  • Understand the challenges firms face in AI adoption 

  • Review the major steps in building machine learning tools, using a case study approach 

  • Understand the risk managers role in building and maintaining machine learning models 

Who should attend?

Relevant departments may include but are not limited to: 

  • Risk management 

  • Operational risk

  • Enterprise risk 

  • Model risk

  • Senior directors 

  • Compliance officers 

  • Consultants 

  • Regulators 

Sessions include
  • Applications of machine learning in risk management

  • Challenges in AI implementation 

  • Case study 1: text analysis & Event Categorization Model 

  • Case study 2: predicting program and project performance 

  • Machine learning model risk management  

  • Managers’ role in mitigating machine learning risks   

Pricing options

We offer flexible pricing options for this course:

  • Early bird rates - save up to $500

  • Group booking rate - save over $1500

  • Subscribe to receive Risk Training updates and avoid missing out on additional savings 

 

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Ariane Chapelle

Director

Chapelle Consulting

Dr, Ariane Chapelle, is Honorary Reader at University College London and is an internationally recognised trainer and consultant in Risk. She teaches at UCL 'Operational Risk Measurement for Financial Institutions’ and is a Fellow of the Institute of Operational Risk..

In 2019, the firm received the Risk.net Award for ‘Outstanding Achievement in the Year in Operational Risk’. She published at Wiley Finance Series the textbook Operational Risk Management: Best Practices in the Financial Services Industry, in December 2018 that rapidly became the No.1 best seller in its field and is now translated in French by Pearson France. In 2020, the book got elected “Book of the Year” by risk.net.

Dr. Chapelle founded and runs her adivsory and training practice in risk management, serving all sizes of financial organisations and international institutions, including central banks and UN agencies. She is a former holder of the Chair of International Finance at the University of Brussels with backgrounds in internal audit, credit risk and investment risk. She has been active in operational risk management since 2000 and was formerly head of operational risk management at ING Group and Lloyds Banking Group.

Natalie Gapp

research consultant

Chapelle Consulting

Natalie Gapp is a research consultant at Chapelle Consulting and co-founder of Chapelle Analytics, one of its subsidiaries. Natalie holds a Master degree in Computational Finance from University College London (UCL) where her study centered around data analysis, statistics, and operational risk.

She is currently working toward a PhD in machine learning applied to operational risk and reputation impact through UCL. Her current research focuses on building machine learning models for operational risk prediction and the measurement of reputation damage to firm following adverse news. Natalie previously built machine learning models for financial news analysis during work on her MSc dissertation. 

Alongside her research activity, she is developing ML online training courses for general managers and advanced courses on ML techniques and coding.

In her prior work she taught high school special education maths courses in New York City. As a teacher, she sought to create engaging curriculum that used real-world applications to make content relevant beyond the classroom.

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

Not the course for you?

Risk Training offers a great selection of courses providing practical guidance on the latest trends, challenges and regulatory changes that span risk management, regulation and derivatives.

View all courses

E-Learning

Risk Training’s self-paced E-Learning platform offers Essentials of Operational Risk programme, plus more topics to come soon.
 

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