Events / Upcoming

    • 30 Nov 2020
    • 12:30 PM - 2:00 PM (EST)
    • Zoom Webinar
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    Abstract:

    Using a randomized experiment in the auto lending industry, we provide causal evidence of higher loan profitability with algorithmic machine underwriting, relative to human underwriting. Machine-underwritten loans generate 10.2% higher loan-level profit than human-underwritten loans in a sample of 140,000 randomly assigned applications. Comparing loans to otherwise identical borrowers, the loans underwritten by machines not only have higher interest rates, but also realize a 6.8% lower incidence of default, relative to loans underwritten by humans. The performance gap is more pronounced with more complex loans and at discrete cutoffs. These results are consistent with findings on the human's limited capacity for analyzing complex problems and with agency conflicts in the underwriting process.

    Bio:

    Mark Jansen is an Assistant Professor of Finance. His primary research and teaching interests are in entrepreneurial finance, household finance, and corporate finance. Prior to joining the University of Utah, Dr. Jansen worked in private equity as managing director at Holland Park Capital and was responsible for strategy and investor relations. In this capacity, Dr. Jansen was a member of the Young President’s Organization. Prior to this Dr. Jansen worked in management consulting and in the chemical Industry. He received a Ph.D. in finance from the University of Texas at Austin, an M.B.A. from London Business School and a dual B.S. in management science and mechanical engineering from the Massachusetts Institute of Technology. 

    • 02 Dec 2020
    • 9:00 AM (EST)
    • Zoom Panel
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    Financial Engineers Give a Personal View of Their Careers in Quantitative Finance


    A Series of Panel Discussions for Students Interested in a Career in Quantitative Finance

    Virtual Event

    Join us remotely as a panel of financial professionals give a personal view of their careers in Quantitative Finance. This year’s event presents a focus on the careers of women in quantitative finance. Following the panel discussion and questions, attendees will have the opportunity for further interaction with panelists in breakout rooms.

    Hosted by:

    The Fields Institute


    Panelists

    Alyson Bailey-Flynn, Scotiabank

    Jeanine Kwong, Manulife

    Samaneh Samavi, Office of Supervision of Financial Institutions

    Jennifer Page, Toronto-Dominion Bank

    Moderator

    Carol Alexander, University of Sussex and University of Peking


    Registration is Free!

    Sponsored by:

    Schedule

    9:00 to 9:10 Welcome and Introductions

    9:10 to 10:00 Panel Discussion and Questions

    10:00 to 10:30 Discussions with Panelists in Breakout Rooms

    All times are in the morning, EST.

    Panelists Information

    Alyson Bailey-Flynn (University of Toronto, Master of Mathematical Finance, 1999) Vice President, Enterprise Risk, Scotiabank - Bio coming soon

    Jeanine Kwong is the Global Head of Investment Risk Oversight at Manulife Investment Management. She has over 15 years of industry experience with current responsibilities include leading the development of a robust investment risk governance framework, spearheading large-scale risk analytics infrastructure transformation project, and enabling independent oversight of market, liquidity and counterparty risks across Manulife’s Global Wealth and Asset Management businesses servicing retail, institutional and retirement clients. Prior to her current role, she oversaw Manulife’s General Accounts’ Public Equity investment risks. She spent 7 years at ING Life Insurance as Head of Financial Engineering for their Japan closed-block variable annuity hedging. Jeanine holds a Master’s degree in Mathematics/Statistics (Quantitative Finance) and a Bachelor degree in Mathematics/Business Administration from the University of Waterloo. She also holds the professional designations of Professional Risk Manager (PRM) and Certified Management Accountant (CMA).

    Samaneh Samavi is a mathematician by training, Samaneh Samavi started her banking career after obtaining her second Master's degree in Financial Mathematics from Western University in 2010. Her quant role in Risk Management started as Model Risk Specialist in Model Validation group of Bank of Montreal where she worked mainly on wholesale credit risk models. She moved up to the Model Development team as Manager in modeling Operational Risk using advanced measurement approaches. After that, she joined TD bank's Quantitative Models Audit team. As a Senior Audit Group Manager she led key audits in the capital models and stress testing in both retail and wholesale businesses as well as testing of regulatory issues on models with exposure on both sides of the border. Having 8+ years of experience in the so called three lines of defense in the banking model risk management groups, she now works in the Model Risk Division of the Office of Superintendent of Financial Institutions where she reviews models used in the banks for regulatory purposes. In her spare time Samaneh loves to spend time in nature, practice yoga and play with her super cute cat.

    Jennifer Page (University of Waterloo, Master of Quantitative Finance, 2000), Vice President, Treasury Modelling, Toronto-Dominion Bank- Bio coming soon

    Dr. Carol Alexander, is Professor of Finance at Sussex and Co-Editor of the Journal of Banking and Finance. Carol has been back at Sussex (her Alma Mater) since 2012. She was appointed the John von Neumann Chair at TU Munich for the year 2018 and in January 2019 she became visiting professor at the Oxford campus of Peking University Business School. Prior academic appointments were as Chair of Financial Risk Management at the ICMA Centre in the Henley Business School at Reading (1999 – 2012) and lecturer in Mathematics and Economics at the University of Sussex (1985 - 1998). She holds degrees from the University of Sussex (BSc First Class, Mathematics with Experimental Psychology; PhD Algebraic Number Theory) and the London School of Economics (MSc Econometrics and Mathematical Economics). She also has an Honorary Professorship at the Academy of Economic Studies in Bucharest, Romania. Carol has also held several positions in financial institutions: Fixed Income Trader at UBS/Phillips and Drew (UK); Academic Director of Algorithmics (Canada); Director of Nikko Global Holdings and Head of Market Risk Modeling (UK); Risk Research Advisor, SAS (USA). She also acts as an expert witness and consultant in financial modelling. From 2010 – 2012 Carol was Chair of the Board of PRMIA (Professional Risk Manager's International Association). She publishes widely on a broad range of topics, including: volatility theory; option pricing and hedging; trading volatility; hedging with futures; alternative investments; random orthogonal matrix simulation; game theory and real options. She has written and edited numerous books in mathematics and finance and published extensively in top-ranked international journals. Her four-volume textbook on Market Risk Analysis (Wileys, 2008) is the definitive guide to the subject. Her latest interests focus on Blockchain and Cryptocurrencies and her recent edited book (with Douglas Cumming, FAU -- Wileys, May 2020) has over 600 pages about Corruption and Fraud in Financial Markets.

    • 14 Dec 2020
    • 12:30 PM - 2:00 PM (EST)
    • Zoom Webinar
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    Abstract: 

    Can machine learning support better governance? In the context of Brazilian municipalities, 2001-2012, we have access to detailed accounts of local budgets and audit data on the associated fiscal corruption. Using the budget variables as predictors, we train a tree-based gradient-boosted classifier to predict the presence of corruption in held-out test data. The trained model, when applied to new data, provides a prediction-based measure of corruption which can be used for new empirical analysis or to support policy responses. We validate the empirical usefulness of this measure by replicating, and extending, some previous empirical evidence on corruption issues in Brazil. We then explore how the predictions can be used to support policies toward corruption. Our policy simulations show that, relative to the status quo policy of random audits, a targeted policy guided by the machine predictions could detect more than twice as many corrupt municipalities for the same audit rate.

    Bio

    Elliott Ash is Assistant Professor of Law, Economics, and Data Science at ETH Zurich's Center for Law & Economics, Switzerland. Elliott's research and teaching focus on empirical analysis of the law and legal system using techniques from applied micro-econometrics, natural language processing, and machine learning. Prior to joining ETH, Elliott was Assistant Professor of Economics at University of Warwick, and before that a Postdoctoral Research Associate at Princeton University’s Center for the study of Democratic Politics. He received a Ph.D. in economics and J.D. from Columbia University, a B.A. in economics, government, and philosophy from University of Texas at Austin, and an LL.M. in international criminal law from University of Amsterdam. 

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