Finance and Insurance January 15, 2021


Ryo Takei, PhD. – IHS Markit

Ryo Takei is currently a Financial Engineer at IHS Markit, where he specializes in software solutions for derivative risk management. He has spent nearly a decade in the financial risk industry, including derivative risk and hedge fund investment fields.

Prior to joining the financial industry, he was a post doctorial researcher at UC Berkeley in the Electrical Engineering and Computing Science department, where he worked on optimal control and differential game theory applications. He is a graduate of Simon Fraser University (BSc and MSc, Mathematics), and UCLA (PhD, Applied Mathematics).

My Life as a Quant

The talk will be non-technical and will consist of two parts. In the first part I outline how financial engineering industry has evolved, from the view of a former academic in a quantitative field, and how it compares to other quantitative industries in the past and present. I will also explain how the 2008 financial crisis affected the derivative risk management industry and where quantitative finance in general is heading in the near future.

In the second part, I will describe my own journey into the industry, as a former academic with no prior knowledge in finance. I will share my thoughts regarding how to approach career decisions and how to position oneself in a changing labor market.

Bahar Sateli, PhD. – PwC Canada

Bahar Sateli is a Manager and Senior Data Scientist at PwC AI & Analytics practice in Montreal. She has more than a decade of experience in advanced software engineering practices and building large-scale and adaptive solutions. With a doctorate in computer science from Concordia University, she holds an in-depth knowledge of development and operationalization of various AI solutions. In addition to her data science role, she is specialized in building explainable, robust AI models, and helping businesses understand the ethical implications of their use. Bahar is the co-founder of Knowlet Networks, a Montreal-based startup creating AI-powered personal research assistants that aid researchers in scientific tasks. Recently, she was recognized as one of the ‘Top 30 Influential Women Advancing AI in 2019’ and ‘Women in AI to Watch’ by Forbes.

Responsible AI in Financial Services

Artificial Intelligence (AI) is a critical disruptor for the corporate world which could contribute up to US$15.7 trillion to the global economy by 2030. Financial services, like insurance, wealth management and banking, are among the sectors where AI would have a major impact by enabling them to target opportunities and pinpoint threats. For example, through intelligent automation financial services can free up time of their workforce for more value-added tasks, and enhance quality of their services through real-time business intelligence and personalization.

However, as the use of algorithmic and ML/AI systems within financial services becomes prevalent, it gets increasingly important for businesses within such a highly-regulated domain to ensure they are able to clearly articulate how decisions are reached (“explainability”) and the extent of human involvement (“human agency”) in the AI systems’ operations. But when it comes to embedding and adopting ‘responsible’ and ‘ethical’ AI practices, it is clear that there will be no ‘one size fits all’ answer, since the risks associated with the use of AI solutions will differ depending on the context in which they are deployed. Hence, getting the balance right will not be easy and businesses need to be equipped with robust governance frameworks to address those key themes associated with AI in financial services, while demonstrating ongoing governance and regulatory compliance. In this talk, I will explain why establishing appropriate accountability for development and use of AI solutions is essential in demonstrating responsible oversight and preventing the crystallisation of risks.

Bevan Ferreira – Okanagan College

Bevan Ferreira began his career in post-secondary education in BC, after completing an MSc. In pure mathematics. After several years spent nurturing students in the interior of BC to pursue studies in mathematics and statistics, he decided to return to university, and instead of pursuing his PhD, he chose instead to branch out into industrial and applied mathematics. After completing the MSc program in Financial Mathematics at McMaster University, in Hamilton Ontario, he began working in downtown Toronto as a ‘Bay Street quant’. He began his career with TD Bank Group, where he specialized in development work for quantitative models for counterparty risk management for the Commodity Derivatives business line, as well as development work in Operational Risk quantification for the Basel II Advanced Measurement approaches framework. Bevan was fortunate to then be able to move into the field of regulatory capital modeling oversight, at Canada’s Banking, Pension and Insurance Regulator, OSFI. There he was responsible for overseeing the accuracy, stability, and suitability of bank regulatory capital modeling for Credit, Counterparty, Market and Operational Risk measurement and capital aggregation. After later work with Deloitte, in the Financial Engineering and Modeling team, he moved over to MUFG/Bank of Tokyo, where he was responsible for the risk and capital management framework for Canadian Branch Operations. He has since returned to his first passion – teaching mathematics and statistics to future professionals – joining Okanagan College in 2018, where he has played a sgifnicant role in the preparation, design and delivery of the Okanagan College program in Data and Analytics.

What makes a good quantitative practitioner?

Many young science graduates enter the field of mathematical finance, trading and risk, only to struggle with the divide between academic “for funding and/or GPA” studies, and the “for profit and payroll” requirements of executives and team leads in their organization. Many will struggle also with making the transition from “perfect” (or “correct” in a peer-reviewed sense) scientific results, to saleable, scalable, profitable and/or executable outcomes. A number of the challenges they face can be traced directly to the explosive growth in data-driven decision-making, the steadily-rising footprint of modeling and quantitative methodologies in finance, risk management and accounting, and rapidly increasing regulatory demands for structure and accountability in the data and modeling space. To further complicate things, too many employers lament the lack of availability of high-performing quantitative talent in vetting, validation, development, model audit roles, specifically, and in the industry generally. I look forward to exploring steps we, as educators, can take to address the growing challenge graduates face in making the transition from school to the workspace.  

Simona Gandrabur, PhD. – National Bank

Simona Gandrabur has been working in the general field of AI for close to 20 years, most notably in areas related to processing of human languages – such as automatic speech recognition, natural language understanding, machine translation and conversational reasoning. Her experience ranges from many years in research, in the development of smart assistant applications, to defining and executing the strategy of AI-based offers.

After Mathematics at the Bucharest University, she studied in Computer Science at the Université de Montréal, where she obtained her PhD, followed by a post-doc in Machine Translation at the RALI NLP Research Laboratory. Before joining the National Bank in 2018, she worked at several start-ups and then for 10 years at Nuance Communications, a leading Hi-Tech provider of speech recognition and conversational solutions. She has multiple patents and publications in top tier scientific conferences and journals and is regularly invited as guest speakers in scientific, business and educational events.

In her current role, she is leveraging both her extensive AI background and her strategic vision and combining this with outstanding communication skills to bridge the gap between the scientists and the business divisions. In her current role at the National Bank, she is directing the AI and data driven transformation of the Wealth Management business unit, by identifying and leading the most promising innovation opportunities.

Challenges and opportunities for AI in Finance

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