Machine Learning in Finance

Sessions will cover neural networks, reinforcement learning, NLP and machine learning in risk management

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Machine Learning in Finance:

A Quantitative Approach

May 2020 | ONLINE

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The fifth edition of our machine learning course is returning to Toronto to provide attendees with an in-depth understanding of machine learning applications. 

This course will give a technical look at machine learning and develop strategies for integrating it within your organization. 

The unique multi-tutor format will provide attendees with an understanding of key theories, models, and more advanced tools in machine learning solutions through a quantitative approach that will also consider risk management, audit, fraud detection and other business areas.

COVID-19 update

Due to the escalation of the COVID-19 developments and the restrictions being placed on travel, Risk Training has taken the decision to provide our May, June and July training courses virtually.

The decision to move remotely has not been taken lightly, but our utmost priority is to safeguard the wellbeing of all our delegates, speakers and staff.

We are hopeful that we will be able to return to our in-person events later this year, however as this unprecedented situation is changing every day, we remain watchful but also focused on delivering this much anticipated course.

 

What will you learn?
  • The theory behind machine learning, latest applications and how methods can be applied in your firm

  • A deeper dive in neural networks and reinforcement learning 

  • Latest approaches to machine learning applications from a quantitative viewpoint 

  • ML and AI capabilities, how they can help you solve problems more effectively and drive your business forward 

  • The importance of machine learning explainability from a risk perspective

 

Who should attend?

Relevant departments may include but are not limited to: 

  • Quantitative analysis, Quantitative modeling

  • Financial engineering

  • Data science 

  • Machine learning 

  • Portfolio management 

  • Model risk 

  • Risk management 

 

Course highlights
  • Introduction to machine learning and a tour of ML models

  • A deeper dive into neural networks, reinforcement learning and natural language processing

  • Machine learning in risk management and audit

  • The importance of explainability in finance 

  • ML in finance: putting it into practice 

  • Machine learning for fraud and Anti-Money Laundering (AML)

Pricing options

We offer flexible pricing options for this course:

  • Early bird rates - save up to $400

  • Group booking rate - save up to $2000

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

Course speakers

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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Rogelio Cuevas

Senior Manager, Data Scientist

TD Bank

Rogelio Cuevas is a Data Scientist, Senior Manager at TD Bank where his main responsibility is providing data science and machine learning solutions for different lines of business. Prior to his tenure with TD Bank, he was a Data Scientist at Scotiabank where he developed credit risk models in retail banking through the prototyping and implementation of machine learning techniques. 

He is an active member of the Toronto data science community where he offers mentoring and training to professionals. Part of these activities include being a panelist at Rotman School of Business, mentor at Insight Data Science Toronto and invited speaker at University of Toronto. 

Before his experience in the financial sector, Rogelio contributed with IBM through its Cognitive Class initiative, formerly known as Big Data University. 

Rogelio has a strong academic background and research experience that he acquired working in academic institutions that include McMaster University, Duke University and The University of Western Ontario. 
 

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John Hull

Maple Financial Professor of Derivatives and Risk Management, Joseph L. Rotman School of Management

University of Toronto

John Hull, Maple Financial Professor of Derivatives and Risk Management, Joseph L. Rotman School of Management, UNIVERSITY OF TORONTO

John Hull is an internationally recognized authority on derivatives and risk management and has many publications in this area. His work has an applied focus. In 1999 he was voted Financial Engineer of the Year by the International Association of Financial Engineers. He has acted as consultant to many North American, Japanese, and European financial institutions. He has won many teaching awards, including University of Toronto's prestigious Northrop Frye award.

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Vishal Gossain

Vice President, AML / ATF Analytics

Scotiabank

Vishal Gossain is currently the vice president of global risk management responsible for all global regulatory and non-regulatory retail models, and application of artificial intelligence and machine learning for retail products. Vishal has held previous senior retail modeling, risk management, business strategy, P&L management and finance positions in Latin America and North America. Some of them include Head of credit risk for HSBC in Latin America, Head of credit risk for Capital One Canada and several other risk/business management positions in HSBC USA and consulting firms. 
Vishal currently sits on the board of MIT Computer Science and Artificial Intelligence Laboratory and University of Western Post-graduate program.
Vishal holds an undergraduate degree in engineering from Indian Institute of Technology (IIT) Kharagpur and has completed his post-graduation in engineering from University of Texas at Austin.
 

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Lee Medoff

CEO

Hedgehog Analytics

Lee Medoff is the founder and CEO of Hedgehog Analytics, a data and analytics consulting firm that provides analytics services and solutions, with the Financial Services sector a primary area of focus. The firm advises tech startups as well, including those in the FinTech sector.

Lee began his career in finance with the Decision Sciences group of the credit card division of JPMorgan Chase, where he developed models to optimize the return on the bank’s card portfolio. He then joined the Models and Methodologies group of the New York Fed, where as part of the Bank Supervision group he focused on Credit and Operational risk, reviewing the models banks in the 2nd District used for Basel, Economic Capital and Stress Testing purposes.   Following the Fed he moved to Moody’s Analytics Risk Management Services, where he oversaw analytics teams in New York and India developing and customizing models for the firm’s software.  He also was a consultant for the Quantitative Advisory Services Group of EY’s Financial Services Risk Management practice, where he worked on stress testing, CCAR and CECL banking and trading book projects for large US and Global financial services clients.

He holds advanced degrees in Statistics from Columbia University and Economics from New York University.

Lee enjoys reading, cycling, and all things tech and is a die-hard foodie.

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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.

Vathy Kamulete

Senior manager AI research - enterprise model risk management

RBC

Zain Nasrullah

Data scientist

RBC

CPD / CPE Accreditation

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CPD Accreditation

This course is CPD (Continued Professional Development) accredited and will allow you to earn up to 8 credits. One credit is awarded for every hour of learning at the event.

CPE Member

CPE Accreditation

This course is CPE (Continuing Professional Education) accredited and will allow you to earn up to 8 credits. One credit is awarded for every hour of learning at the event in accordance with the standards of the National Registry of CPE Sponsors.

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