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Global Financial Markets Intelligence

Global Financial Markets Intelligence

Machine Learning and its Applications for Financial Institutions

A Comprehensive Examination of Machine Learning and its Applications within Financial Markets including Risk Modelling, Valuation, and Market Predictions

9-10 Nov 2020
New York, NY, United States of America

Why You Should Attend

Machine Learning and its Applications for Financial Institutions

To ensure we meet your expectations and maximise your return on training investment, we favour a classroom/workshop style set

Data science and machine learning are playing increasingly important roles in the financial services industry. This course is designed to provide attendees with broad exposure to machine learning concepts and techniques, with emphasis both on practical applications and the management of machine learning projects. It begins with apresentation on the history of machine learning and the evolution of machine learning techniques. Afterwards, all aspects of designing, training and testing neural networks and decision tree models are presented. Importantly, techniques to prevent sub-optimal solutions and overfitting are demonstrated in detail. Also, a set of techniques that can be used to interpret neural network decisions are presented. Following that, the development and testing of the instructor’s neural networks to detect credit card fraud and trade US Treasury bonds are presented to show how the concepts explored earlier are reflected in actual model construction.

The course also examines practical examples of more complicated (i.e., deep learning) models. This starts with a deion of deep learning and the major types of deep learning models including convolutional neural networks, recurrent neural networks, and long short-term memory networks. The instructor will then present a detailed deion of a series of models he has built. These include a neural network for corporate bond relative value, a decision tree for predicting bond market moves from bond trading patterns, and a natural-language-processing based sentiment model for predicting bond market moves. Finally, there will be a discussion of when to use neural networks versus decision trees and how data science applications are changing the business of finance.


About your expert trainer:

Terry Benzschawel has had over 40 years of experience as a quantitative researcher. For the past 30 years he was aquant on Wall Street (Chase / Salomon Brothers / Citigroup) during which he built applications for systematic credit trading and risk management. Terry began his machine learning experience in 1984 when he built a neural network model of the visual system, followed in 1988 by work on genetic algorithms to predict corporate bankruptcy. In addition, in 1990 he built and implemented the first neural network model to detect fraud on Citibank’s credit card portfolio. While at Citigroup, hebuilt and patentedneural networks for trading US Treasury Bonds and the Mexican Peso. Most recently, Terry built a deep learning neural network to make markets in high yield corporate bonds.

Terry is a frequent speaker at industry conferences and events and has lectured on credit modeling at major universities. He is currently an Executive in Residence at the University of California at Berkeley. Terry has published over a dozen articles in refereed journals and has authored two books:  CREDIT MODELING: FACTS, THEORIES AND APPLICATIONS and CREDIT MODELING: ADVANCED TOPICS. In addition, he has beenthe instructor for courses in credit modelling for Incisive Media, the Centre for Finance Professionals, the Machine Learning Institute, and taught in UCLA’s Master of Financial Engineering program last fall.

Who should attend?

From Banks, Asset Managers, Broker/Dealers, Insurance Companies and all other financial institutions.

· Quantitative Developers

· Quantitative Analysts

· Quantitative Researchers

· Model Designers and Developers

· Scenario Designers

· Strategists

· Data Scientists

· Data Project Managers

· Risk Managers

· Operations Managers

· Anyone wishing to have a broad knowledge of machine learning techniques and their applications in finance

Key Topics

  • Overview of the evolution of machine learning techniques
  • Exposure to range of neural networks from multi-layer perceptrons to deep learning networks
  • Knowledge of construction, training and testing of neural networks
  • Exposure to successful neural network applications in financial markets
  • Gain perspective on how machine learning is changing the business of finance

  • Why Choose GFMI marcus evans?

    marcus evans specialises in the research and development of strategic events for senior business executives. From our international network of 63 offices, marcus evans produces over 1000 event days a year on strategic issues in corporate finance, telecommunications, technology, health, transportation, capital markets, human resources and business improvement.

    Above all, marcus evans provides clients with business information and knowledge which enables them to sustain a valuable competitive advantage and makes a positive contribution to their success.

    Voice of Our Customers
    • “The use of online mini-games and simulations was very engaging. I have enjoyed the interaction with the rest of the learners in the sessions” European Central Bank
    • “The practical approach solidified my understanding of the topic, well structured course” ING Bank
    • “Clear instructions and passionate delivery. Very open to answering questions and made everyone fell at ease” RBS
    • “A great event for practitioners” Citigroup
    Join the Discussion

    Event Contact

    For all enquiries regarding speaking, sponsoring and attending this conference contact:

    Kamelia Simeonova

    101 Finsbury Pavement, London, EC2A 1RS

    0044 203 002 3172
    Email: kamelias@marcusevansuk.com