09 - 10 November 2022

AI in Finance Summit AI in Finance Summit schedule

Toronto AI Summit



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  • 08:00

    COFFEE & REGISTRATION

  • 09:00

    WELCOME NOTE

  • 09:15
    Olga Tsubiks

    Building Reliable AI Products in Banking

    Olga Tsubiks - Director, Strategic Analytics and Data Science - RBC

  • 09:45

    Deep Learning Tools & Anti-Money Laundering

  • 10:10
    Serena McDonnell

    The Role of Alternative Data in Investing

    Serena McDonnell - Senior Data Scientist - Delphia

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    Applying alternative data to quantitative equity strategies has high potential and unique challenges. In this talk, we will use Delphia's machine learning driven long-short equity market neutral strategy as context to discuss the following: - Case studies to highlight the advantages of alternative data in investing in general. - The promise of alternative data in quantitative equity strategies. - The challenges in working with alternative data in Delphia's strategy.

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  • 10:40

    MORNING NETWORKING BREAK

  • 11:10

    Panel Discussion: Machine Learning in Wealth Management

  • 11:50
    Alexey Rubtsov

    AI/ML Regulation: A Model Risk Management Perspective

    Alexey Rubtsov - Assistant Professor - Ryerson University

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    The last decade has witnessed a large-scale adoption of Artificial Intelligence and Machine Learning (AI/ML) models in finance. Although there are many benefits that AI/ML can bring to financial services (e.g., higher accuracy, automation), it could also introduce new and amplify existing risks. In this respect, financial regulators around the world are currently working on regulatory requirements that AI/ML models should satisfy when applied by financial institutions. In this presentation we discuss some most recent developments on AI/ML model risk management.

  • 12:20
    Armando Ordorica

    Risk Algorithms, Trust, and Digital Identity - What Does Your Online Behavior Say About You?

    Armando Ordorica - Senior Data Scientist - Risk and Trust - Pinterest

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    With a growing number of sensors and data collected about individuals, the resolution of risk scoring models has become increasingly crisp. While traditional data fields such as income, educational attainment, and civil status are still used, alternative data sources are on the rise. The list of emerging startups that scrape and sell alternative data sources continues to grow. Maybe the size of your screen, your phone provider, or your email domain are not very strong predictors of risk when evaluated independently, but together can sculpt a digital persona with a very specific risk profile.

    What data sources are companies using to evaluate the risk of individuals or even networks of individuals? How do we feel about data from health sensors (smartwatches, etc) to be used for risk profiling? Is this a good thing or a bad thing? Where do we draw the line?

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  • 12:50

    LUNCH

  • 14:00
    Natalia Bailey

    ML in Credit Underwriting

    Natalia Bailey - Research Manager - FinRegLab

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    FinRegLab is currently undergoing empirical research together with Stanford University to evaluate the implications of using machine learning with the use of models that take into account requirements of U.S. law (fair lending and adverse action), and general robustness issues. The empirical research is getting to answer questions around the explainability of credit underwriting models.

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  • 14:30
    Meisam Soltani-Koopa

    Using Reinforcement Learning to Maximize Customer Profitability and CLV at Financial Institutions

    Meisam Soltani-Koopa - Manager Performance & Insight PISCO - Scotiabank

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    Customer Lifetime Value, CLV, is a popular measure to understand the future profitability of customers

    to allocate resources in more efficient ways to keep the company alive during difficult economic

    situations. We use machine learning tools to predict the expected revenue from each customer during

    one year of his/her relationship with the institution as the CLV of the customer. The approach is

    implemented on two datasets from two international financial institutions. Different feature engineering techniques were applied to improve the prediction power of the model. We used two stage or three stage prediction models. In the second phase, we train a reinforcement learning algorithm based on the history of marketing activities and the CLV as the state of customers to determine the optimum marketing action for customers in each state to maximize their profitability.

  • 15:00

    AFTERNOON NETWORKING BREAK

  • 15:45
    Wafiq Syed

    Click! Grow Customer Lifetime Value with AI

    Wafiq Syed - Analytical Lead, Finance - Google

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  • 16:10
    Isaac De Souza

    Monitoring Financial Risk with AI

    Isaac De Souza - Artificial Intelligence & Emerging Technology Risk Officer - BMO Financial Group

  • 16:40
    Eric Charton

    Online Banking, Deep Learning & NLP

    Eric Charton - Senior AI Director - National Bank of Canada

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    Using Deep Learning with Word Embeddings to improve Customer Satisfaction

    Understanding customer satisfaction in retail banking requires exploring and comprehending multiple sources of feedback, such as emails, social networks reviews, web feedback, bot interactions, as well as speech-to-text transcripts collected from call centers. Since such a vast amount of textual data can be difficult to leverage with traditional text mining techniques, deep learning and word embeddings can be used to automatically classify and label feedback, and then deeply analyze and understand their content. In this communication we explain how we leverage all those AI techniques to get an in-depth understanding of the opinions and needs of National Bank’s retail customers. We also show how we improve the performance levels of those AI tools using in-house algorithms and data resources to improve the overall capacity of natural language understanding.

    Key Takeaways: • Industrial applications • State of the art classification • Understanding of DL embedding limits

    Eric Charton holds a Master in machine learning applied to voice recognition, and a Ph.D. in machine learning applied to Information extraction and natural language generation. He worked as scientist and research project coordinator in academic context in Europe (University of Avignon) and North America (CRIM, École Polytechnique de Montréal) before becoming head of search engine research and development at Yellow Pages Canada. Since March 2018, he is Senior AI Director at National Bank of Canada.

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  • 17:10

    CLOSING NOTE

  • 17:20

    NETWORKING RECEPTION

  • 08:00

    COFFEE & REGISTRATION

  • 09:00

    WELCOME NOTE

  • 09:15
    Hamid Arian

    Asset Management and AI

    Hamid Arian - Associate Director, Research and Financial Engineering - Equitable Bank

  • 09:45

    AI Models Impact on KYC Requirements

  • 10:10

    The Legal Framework for Artificial Intelligence and Machine Learning

  • Chetan Phull

    Presenter

    Chetan Phull - Associate | Data Privacy, Cybersecurity, and Digital Law - Deloitte Legal

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    Chetan Phull is a technology and data management lawyer at Deloitte Legal Canada in Ontario. He is in his tenth year of practice and holds two IAPP certifications (CIPP/C/US). He regularly advises on regulatory and contractual aspects of product development, integrated digital services, and cyber incident response. Chetan's subject matter focus is in privacy, artificial intelligence, blockchain, SaaS/PaaS, and IT service contracts. His publications include BIG DATA LAW IN CANADA, and numerous compilations and articles on virtual asset regulation. Chetan is admitted to the Bars of Ontario, New York State, and Massachusetts. He is a former law clerk of the Nova Scotia Court of Appeal, with law degrees from Queen’s University and University College London.

  • Helene Deschamps Marquis

    Presenter

    Helene Deschamps Marquis - Partner, Data Privacy and Cyber Security Law - Deloitte Legal

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    Hélène is a partner and the National Leader of the Data Privacy and Cyber Security Law practice at Deloitte Legal Canada LLP. Hélène and her team provide data breach coaching services and incident response support to clients across Canada on some of the most significant cyber incidents in the country. A dynamic thought-leader, she also advises Canada’s leading institutions and their respective boards of directors on significant digital risks, including cybersecurity matters and data management. Hélène’s integrated, multidisciplinary approach has gained her recognition as an expert in her field by major cybersecurity rankings, including IDC MarketScape and Forrester Wave. Moreover, she is a frequent speaker in North America and Europe on cyber security and data privacy. In addition, Hélène is recognized by various legal rankings for her expertise in data privacy, cybersecurity and digital law. Currently, Hélène is on the board of ITechLaw where she copresides their I-WIN (Women’s International Network) and she is also the vice-president for their American conferences. Previously, Hélène was Chair of the ITechLaw World Technology Conference in Boston in May 2019.

  • 10:40

    MORNING NETWORKING BREAK

  • 11:10

    Discussion Group: The ROI of AI in Finance

  • Ioannis Bakagiannis

    Facilitator

    Ioannis Bakagiannis - Director of Machine Learning, Marketing Science - RBC

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  • 11:50
    Talieh Tabatabaei

    Operationalizing Responsible AI in FIs

    Talieh Tabatabaei - Senior Manager, AI/ML Model Validation - TD

  • 12:30

    LUNCH

  • 13:35

    NLP in Fraud Prevention

  • 14:00
    Mary Jane Dykeman

    Closing Panel Discussion: Bill C-27 Potential Impact on AI and Implications of the EU AI Act

    Mary Jane Dykeman - Managing Partner - INQ Law

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    Mary Jane Dykeman is a managing partner at INQ Law. In addition to data law, she is a long-standing health lawyer. Her data practice focuses on privacy, artificial intelligence (AI), cyber preparedness and response, and data governance. She regularly advises on use and disclosure of identifiable and de-identified data. Mary Jane applies a strategic, risk and innovation lens to data and emerging technologies. She helps clients identify the data they hold, understand how to use it within the law, and how to innovate responsibly to improve patient care and health system efficiencies. In her health law practice, Mary Jane focuses on clinical and enterprise risk, privacy and information management, health research, governance and more. She currently acts as VP Legal, Chief Legal/Risk to the Centre for Addiction and Mental Health, home of the Krembil Centre for Neuroinformatics, and was instrumental in the development of Ontario’s health privacy legislation.

    Mary Jane regularly consults on large data initiatives and use of data for health research, quality, and health system planning. Her consulting work extends to modernizing privacy legislation and digital societies, and she works with Boards, CEOs and CISOs, as well as innovation teams on the emerging risks, trends and opportunities in data. Mary Jane regularly speaks on AI, cyber risk and how to better engage and build trust with clients and customers whose data is at play. She is also a frequent speaker and writer on health law and data law. Mary Jane is co-founder of Canari AI, an AI risk impact solution.

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  • 14:40

    CLOSING NOTE

  • 14:50

    END OF SUMMIT

Toronto AI Summit

Toronto AI Summit

09 - 10 November 2022

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