Machine Learning in Finance
Sessions will cover neural networks, reinforcement learning, NLP and machine learning in risk management
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.
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.
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
Relevant departments may include but are not limited to:
Quantitative analysis, Quantitative modeling
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)
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.
Senior Manager, Data Scientist
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.
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.
Vice President, AML / ATF Analytics
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.
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.
Senior director, enterprise model risk management
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.
Senior manager AI research - enterprise model risk management
CPD / CPE Accreditation
This course is CPD (Continued Professional Development) accredited and will allow you to earn up to 12 credits. One credit is awarded for every hour of learning at the event.
This course is CPE (Continuing Professional Education) accredited and will allow you to earn up to 12 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.
Why it is pertinent for banks to make changes and transition to new technology.