Event Agenda

Agenda

September Virtual Training Agenda

Agenda timing is in BST
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Session one - 2pm BST / 9am EDT
Session two - 3.15pm BST / 10.15am EDT
End - 4.15pm BST / 11.15am EDT
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14:0015:00

Recent trends in ML application

14:00 - 15:00

  • Understanding the drivers of opportunity 
  • Regulation, data privacy, and RegTech
  • Application to risk management
  • Application to investment
  • Data quality and analytics
Jonathan Frieder

Compliance technology lead, U.S. regulatory compliance practice, finance & risk practice

Accenture

Jonathan has a broad background in Financial Services with expertise spanning Regulatory Affairs and Compliance, Conduct Risk, Operational Risk, Fraud and Financial Crime and IT Security. He is a hands-on leader with 25 years of experience leading and successfully delivering complex regulatory-driven business and technology initiatives for top-tier firms and currently assists clients with identifying and effectively integrating technology solutions to solve challenges, improve operational efficiency and reduce cost. Jonathan is a RegTech liaison to Accenture’s FinTech Strategy Group and FinTech Innovation Lab, an accelerator program for early stage FinTech and RegTech companies.

15:0015:15

Break

15:00 - 15:15

15:1516:15

Machine learning models

15:15 - 16:15

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • Deep learning
  • Advanced machine learning models
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.

16:1516:15

End of day one

16:15 - 16:16

14:0015:00

ML techniques for model risk management

14:00 - 15:00

  • Managing model risk with AI 
    • Validating data 
    • Data generation
    • Model risk quantification 
    • Monitoring 
  • Managing the risks of AI 
    • Model dependencies 
    • Safe AI framework 
    • Model assumptions and design 
    • Performance testing 
    • Limitations 
Jos Gheerardyn

Co-founder and CEO

Yields.io

Jos is the co-founder and CEO of Yields.io. Prior to his current role he has been active in quantitative finance both as a manager and as an analyst. Over the past 15 years he has been working with leading international investment banks as well as with award winning start-up companies. He is the author of multiple patents applying quantitative risk management techniques to imbalance markets. Jos holds a PhD in superstring theory from the University of Leuven (Belgium).

15:0015:15

Break

15:00 - 15:15

15:1516:15

Machine Learning and portfolio construction

15:15 - 16:15

  • Data science and machine learning in quantitative finance
  • New tools & techniques in large-scale machine learning and analytics
  • The impact ML has on portfolio optimisation
  • Identifying different types of portfolios requiring different mathematical models
  • Multi-period portfolio optimisation frameworks
David Jessop

Head of investment risk in EMEA

Columbia Threadneedle Investments

David Jessop is the Head of Investment Risk in EMEA for Columbia Threadneedle Investments. Prior to this he spent 17 years at UBS as the Global Head of Quantitative Research. Before joining UBS he spent time at Citigroup acting as the Head of Quantitative Marketing. He started his career at Morgan Grenfell; initially as a derivative analyst, and then as a quantitative fund manager. He has a MA in Mathematics from Trinity College, Cambridge. 

 

16:1516:15

End of day two

16:15 - 16:16


14:0015:00

Neural nets and reinforcement learning

14:00 - 15:00

  • Neural networks and their applications
  • Convolutional neural networks
  • Recurrent neural networks
  • Monte Carlo vs. temporal difference algorithms
  • Reinforcement learning and its applications
Ranko Mosic

Big data architect

Bank of America

Ranko Mosic is a consultant specializing in AI/ML applied to finance. He is helping clients with ML/AI concepts, use case identification, feature selection, algorithm selection and build, technical assessment, recommendations and implementation. 

He advised State Street Corporation, Bank of America and other clients on ML/AI and Big Data initiatives. 

https://medium.com/@ranko.mosic

https://twitter.com/mosicr

[email protected]

15:0015:15

Break

15:00 - 15:15

15:1516:15

Understanding natural language processing

15:15 - 16:15

  • Statistical foundations of NLP
  • Explainability vs Trust
  • Practical lessons learned from validating two unique NLP models
  • Deep dive: using influential instances and predictive uncertainty to establish model trust
Greg Kirczenow

Senior director, enterprise model risk management

RBC

Greg is Senior Director in Enterprise Model Risk Management at RBC. He has a decade of experience in market and model risk management, with specialization in enterprise and retail risk. In his present role, Greg is leading efforts related to responsible AI practices, as well as development of validation techniques both for AI and using AI.

Zain Nasrullah

Data scientist

RBC

Vathy Kamulete

Senior manager AI research - enterprise model risk management

RBC

16:1516:15

End of day three

16:15 - 16:16

14:0015:00

Machine learning and risk

14:00 - 15:00

  • Identifying and monitoring risk
  • Application of machine learning in banking, risk management & modelling
  • Analysis of rare events
    • Labelled, but unbalanced data: the case of credit card fraud
    • Unlabelled data: operational risk events
    • Discriminative vs. generative models: when are distributions relevant?
  • Network analysis
  • Time series spikes and breakouts
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.

15:0015:15

Break

15:00 - 15:15

15:1516:15

Applying machine learning in practice

15:15 - 16:15

  • Pros and cons of applying ML in finance 
    • Pros, cons(traints), risk, rewards? 
  • Strategies for ML in finance
    • ML strategy? 
  • Importance of making the right choices 
    • Case study: how to fail quick
Gilles Artaud

Head of model risk audit

Crédit Agricole Group

Gilles Artaud has been working in investment banking for the last 25 years, where he held various positions within Quant, Front Office, IT and Risk Departments, working all along on many underlying types, pricing, validation, regulatory and economic capital, market risk and counterparty credit risk topics. 

After managing IT and Quant teams in the 1st line of defence (Front Office), he moved to 2nd line of defence (Risk and Validation) to lead topics around CCR, XVA, IM and many regulatory topics.

Gilles is now in charge for the 3rd line of defence of Model Audit for Group Inspection in Group Crédit Agricole SA, managing the transformation of Model Audit induced by new models, framework, market and business evolutions, new fields of application and compliance with ever-increasing regulations. 

 

 

16:1516:15

End of course

16:15 - 16:16