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  • 08:00am


  • 09:00am


  • 09:15am
    Olga Tsubiks

    Building Reliable AI Products in Banking

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


    Whether you are part of a small fintech company or a top-tier financial institution, this practical session shows data scientists, data engineers, and business leaders how to build reliable AI products within your organization. You will gain insight into everything from how to do model monitoring to how to prepare your team for model failures.

    The advancements of AI offer financial services organizations the potential to increase revenue at a lower cost by engaging and serving customers in radically new ways. Opportunities come with challenges, and AI reliability can mean the difference between success and failure. Whether you want to build AI products that can lead your bank to deeper customer relationships, expand market share, or increase revenue, you'll learn how to perform everyday ML tasks while keeping the bigger picture in mind.

    Olga will show you how to create reliable and efficient AI systems:
    • What AI reliability is
    • Where it belongs in the AI product development process
    • AI reliability principles
    • Common reliability problems and solutions

  • 09:45am
    Henry Ehrenberg

    Putting NLP to Work in Financial Services

    Henry Ehrenberg - Co-founder - Snorkel AI


    As Financial Services increasingly embrace digitization, NLP presents many opportunities for efficiency gains and automation across the entirety of a bank’s operations. However, a lot of these efforts to develop and operate NLP applications have been bottlenecked by the data not being AI ready. Join Henry Ehrenberg, to learn how Snorkel AI helps Financial Services companies solve their data challenges and discuss a few NLP use cases this has unlocked.

  • 10:10am

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

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


    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?

  • 10:40am
    David Foxit

    Presentation by iDox.ai

    David Perrett - Business Development Manager - iDox.ai

  • 10:50am


  • 11:20am

    PANEL DISCUSSION: Emerging Technology Adoption Challenges for Financial Institutions

  • Jason Madge-1


    Jason Madge - Strategic Account Executive - Snorkel AI

  • Issac-1


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

  • Elizabeth Hunker


    Elizabeth Hunker - Senior Director of Innovation - Northwestern Mutual

  • Kate Goldman Headshot


    Kate Goldman - Senior Policy Associate - Elliptic

  • 12:00pm

    AI/ML Regulation: A Model Risk Management Perspective

    Alexey Rubtsov - Assistant Professor - Ryerson University


    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.

    Alexey Rubtsov is an Assistant Professor of Mathematical Finance at Ryerson University, a Senior Research Associate at the Global Risk Institute and an Academic Advisor at Borealis AI. His areas of focus are Systemic Risk, FinTech, and Asset Allocation. His academic research was published in such journals as the Operations Research, Journal of Banking and Finance, Journal of Economic Dynamics and Control, Annals of Finance, among others. He holds a PhD in Operations Research and an MSc in Financial Mathematics from North Carolina State University.


  • 12:30pm
    Serena McDonnell-3

    The Role of Alternative Data in Investing

    Serena McDonnell - Lead Data Scientist - Delphia


    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


  • 1:00pm


  • 2:00pm
    Eric Lanoix

    ML in Credit Underwriting

    Eric Lanoix - Vice-President - Quantitative Risk Analytics - Coast Capital Savings

    In spite of some well-publicized early missteps, Machine Learning (ML) in credit underwriting is here to stay. Significant gains in approval speed, operational efficiency, and reduced credit losses can be realized while limiting operational and reputational risk. This presentation discusses aspects of credit underwriting where ML shows the most promise, as well as potential pitfalls. In particular, I promote inherent and continuous explainability as a winning strategy (instead of post-hoc techniques like LIME or SHAP). I also discuss model stability and fairness and propose strategies for quickly gaining acceptance of ML credit underwriting from internal stakeholders and regulators.
  • 2:30pm
    Meisam Soltani-Koopa

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

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


    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.

  • 3:00pm


  • 3:40pm

    Cloud-Native and Data-Centric Systems Design for Fast Moving Enterprises

    Zia Babar - Director, Cloud Engineering - PwC Canada


    Enterprises are increasingly under pressure to continually evolve and respond to fast-moving consumer trends, and adopt emerging digital technologies for business execution. It is no longer sufficient for them to design and set up their technological infrastructure once, ans assume that it would suffice for extended periods of time. By leveraging cloud computing and modern data infrastructure, enterprises can rapidly design and modify solutions to deal with evolving business and technological requirements. For this, we propose and discuss cloud-native and data-centric architectural design patterns that enterprise can effectively utilize to respond to fast-moving internal and external factors.


  • 4:10pm

    Managing AI Risks for Financial Institutions

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


    Artificial Intelligence (AI) is one of the most powerful technologies being adopted today and will significantly impact the financial industry and society at large. In this session we will demystify the “black box” of AI, discuss the novel risks it brings, how regulators are reacting, and what can be done to ensure we safely and securely use AI.

  • 4:40pm
    Eric Charton

    Using a Deep Learning Bot Model to Answer Customers Emails Automatically

    Eric Charton - Senior AI Director - National Bank of Canada


    Handling email messages from customers can be an important part of the workload of a customer support service. Answering emails automatically using information retrieval or machine learning and classification techniques are solutions that can be deployed to handle this task with more or less success. In this presentation we will first explore the technological solutions proposed in the past to answer automatically to emails and their specific challenges. Then we will introduce our novel solution : a deep learning bot model initially designed to handle chatbots intentions, that we reused to automatically answer to emails messages in real life operations context.

    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.

  • 5:10pm


  • 5:20pm