The Analytic Executive,
Quarterly Breakfast Series

Session 1

Thursday, March 23, 2017
8:00 am - 10:30 am

In this session, you’ll learn how Machine Learning & Artificial Intelligence initiatives are shaping how organizations can gain a competitive advantage. With the ability to automate decisions and increase productivity, ML and AI have the potential to affect all functional areas within an organization, from marketing, to fraud and risk management, to logistics and operational intelligence.

The Analytic Executive seminar series is an event designed specifically for data driven executives, or those hoping to drive their organizations to a high level of analytic competency across all functional areas of the business.

These sessions will be hosted by the Global SAS Best Practices team as they share insights from their experiences in working with other executives in marketing, IT, fraud, analytics/insights, etc. Additionally, a second presentation will be delivered by a SAS partner within academia or consulting to drive further relevance as it applies to each sessional topic.


Dr. Joseph Geraci
Data Scientist, Equifax

Dr. Joseph Geraci has a doctorate in mathematical physics from the University of Toronto/University of Southern California, with postdocs in oncology, psychiatry, and machine learning. He has been working on creating machine learning models for 8 years and created his own paradigm of machine learning based on a novel mathematical approach. He is currently a data scientist at Equifax and is running his own AI startup that specializes in creating robust predictive models for the complex populations found in medicine and finance. He loves predictive analytics and has an expertise in implementing models that explain the phenomenon being modelled through his extensive knowledge of feature selection methods. 

Steve Holder
Strategy Executive, Analytic Ecosystems, SAS Canada.

As Strategy Executive, Analytic Ecosystems for SAS Canada Steve Holder is responsible for creating and driving SAS solution strategy in the market.  A key part of this is providing thought leadership for the SAS Analytics, Big Data and Cloud portfolios.  A Canadian analytics evangelist Steve has seen first-hand how the use of analytics and data can help customers solve business problems; make the best decisions possible and unearth new opportunities.   Steve’s passion is making technology make sense for everyone regardless of their technical skillset.

With many years of software industry experience, spanning: management, solutions architecture, presales and sales, Steve brings alignment and execution across SAS’ customers, sales teams, and partners.

Prior to joining SAS in 2014 Steve was Director of solution strategy at SAP and Regional Director at IBM. Steve received his Bachelor of Arts from McMaster University and lives in Toronto with his wife and two daughters.  During his spare time he enjoys traveling, cycling, skiing and spending time outdoors.

Steve tweets at @holdersmTO and can be emailed at

Dr. Wayne Thompson
Chief Data Scientist, SAS

Wayne Thompson is a globally renowned presenter, teacher, practitioner and innovator in the fields of data mining and machine learning. He has worked alongside the world's biggest and most challenging organizations to help them harness analytics to build high performing organizations. Over the course of his 20-year tenure at SAS he has been credited with bringing to market landmark SAS analytics technologies (SAS Text Miner, SAS Credit Scoring for Enterprise Miner, SAS Model Manager, SAS Rapid Predictive Modeler, SAS Scoring Accelerator for Teradata, SAS High Performance Analytics (Data Mining) SAS Analytics Accelerator for Teradata, SAS Visual Statistics). Current focus initiatives include easy to use self-service cognitive computing tools for business analysts, deep learning, and self-service machine learning APIs. 

Wayne received his Ph.D. and M.S from the University of Tennessee in 1992 and 1987, respectively. During his PhD program, he was also a visiting scientist at the Institut Superieur d'Agriculture de Lille, Lille, France.

Dr. Eugene Wen
VP, Group Advanced Analytics, Manulife

Dr. Eugene Wen is the Vice President, Group Advanced Analytics, at Manulife. He is responsible for supporting group functions with advanced analytics, build a Center of Expertise (CoE) in advanced analytics, provide thought leadership, create governance and policy frameworks for analytics function and provide research and development capabilities to businesses across the company.

Prior to joining Manulife, Eugene served as the Vice President and Chief Statistician at the Workplace Safety & Insurance Board (WSIB). He was trained in both clinical medicine and public health.

Dr. Pierre Montagnier,
Director, Customer Analytics & Modeling, Bank of Montreal

Within BMO Enterprise Customer Analytics team, Pierre Montagnier leads a team of data scientists and modellers responsible for partnering with Marketing, Brand, Digital & Channels Marketing, and lines of business to develop data-driven customer insights and marketing models that drive marketing strategies and plans to support sales efforts and ensure a positive customer experience across all channels. Prior to joining BMO Financial group, Pierre led analytics teams at other financial institutions and in luxury retail at Holt Renfrew.  

Pierre is a CFA charter holder. He graduated from Ecole Centrale de Nantes in France, received a M.Sc. (Eng) in Manufacturing Engineering & Management from the University of Birmingham (UK) and a Ph.D. in Mechanical Engineering from McGill University.


Dr. Lovell Hodge, PhD
Vice President, North American Fraud Analytics Financial Crimes & Fraud Management Group, TD Bank Group

Lovell Hodge is Vice President of North American Fraud Analytics (NAFA), in the Financial Crimes & Fraud Management group at TD Bank.  North American Fraud Analytics (NAFA) applies technology, artificial intelligence and other advanced analytics to deliver best in class fraud and financial crime management and prevention capabilities.

Lovell joined TD in 2005 and has more than 20 years of information technology expertise in developing large scale database systems, machine intelligence and expert systems for the insurance, medical, manufacturing and more recently the financial industries.  His past experience includes leading teams to deliver strategic initiatives and emerging technologies from concept to implementation.

Prior to joining TD Lovell lectured at the University of Waterloo where he taught artificial intelligence and published 13 papers on computational intelligence, multi-agent theory and artificial neural networks   in world renowned publications such as Systems Man and Cybernetics. Lovell's expertise includes many areas of artificial intelligence including computer vision, artificial neural networks, multi- agent systems, deep learning as well as genetic algorithms and fuzzy logic. Lovell currently has a patent pending in computer vision.

Lovell holds a Bachelor of Computer Science degree from Ryerson University, a Master of Computing and Information science from the University of Guelph and a Ph.D. in Artificial Intelligence from the University of Waterloo. At University of Waterloo, his areas of research included Artificial Neural Networks, Intelligent Multi-agent systems and learning algorithms.


The St. Andrew’s Club

150 King St. W., 27th Floor 

Additional Resources

SAS Visual Data Mining & Machine Learning Overview
An intuitive programming environment. Innovative algorithms. Fast, in-memory processing. SAS Visual Data Mining and Machine Learning shatters barriers related to data volume and variety, limited analytical depth and computational bottlenecks. That means greater productivity – and faster, deeper insight.

Machine Learning: What it is and why it matters
Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.

The Evolution of Analytics: Opportunities and Challenges for Machine Learning in Business
Analytics is now an expected part of the bottom line. The irony is that as more companies become adept at analytics, it becomes less of a competitive advantage. Businesses are now being forced to look deeper into their data to increase efficiency and competitiveness.

Enter machine learning. Recent advances have led to increased interest in adopting this technology as part of a larger, more comprehensive analytics strategy. But incorporating modern machine learning techniques into production data infrastructures is not easy.

Read this report to learn more about modern applications for machine learning, including recommendation systems, streaming analytics, deep learning and cognitive computing. And learn from the experiences of two companies that have successfully navigated both organizational and technological challenges to adopt machine learning and embark on their own analytics evolution.

Statistics and Machine Learning at Scale
Machine learning uses algorithms to build analytical models, helping computers “learn” from data. It can now be applied to huge quantities of data to create exciting new applications such as driverless cars.

This Conclusions Paper, based on a presentation at the Analytics 2014 conference, introduces key machine learning concepts and describes new SAS solutions that allow data scientists to perform machine learning at scale. In the presentation, Viseca Card Services shares its experiences using machine learning to differentiate a new customer loyalty program

Machine Learning with SAS Enterprise Miner
This paper illustrates how a SAS team of modelers used SAS Enterprise Miner and 2009 KDD Cup competition data to create a highly accurate model for predicting churn. They applied several data preparation, feature creation and dimension reduction techniques to prepare the data for modeling. They then used several machine learning approaches, including an open source model that could be incorporated into SAS Enterprise Miner. The models were accessed using the assigned validation criteria. Learn how they approached the problem and which model was declared the “winner.”

An Overview of SAS Viya

SAS Viya is an open, cloud-ready, in-memory architecture that delivers everything you need for fast, accurate analytical results – all of the time. With its fluid, scalable and fault-tolerant processing environment, this resilient architecture addresses the complex analytical chal­lenges of today with the ability to effortlessly scale into the future. SAS Viya provides:

  • A modern, cloud-ready analytics architecture from the analytics market leader.
  • A single, open and governed analytics environment with a standardized code base that can incorporate both SAS and other programming languages.
  • A uniquely comprehensive and scalable platform for both public and private cloud implementations.

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Please contact Andrew Bowden


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