In recent years, many machine learning methods have been developed for predictive analytics and automated decision making. However, the lack of explanation resulted in both practical and ethical issues. In this project, we will employ and advance interpretable machine learning methods for various predictive analytics tasks in employee benefit insurance. The proposed methods can be used by the partner organization to improve transparency and hence trust in a wide range of applications that involve predictive analytics.
Blockchain technologies are increasingly becoming integral parts of information systems in domains that exhibit an increased need for resilience and can make no assumption of trust between parties. However, properly adopting blockchain in an information system design remains difficult, unsystematic and requires thorough understanding of the technology. In this project we explore ways by which traditional model-driven architecting and design techniques can be augmented to support incorporation of blockchain components.
As part of reforms to the regulations governing Canadian banks, new rules governing the capital to be set aside for market risk have been proposed, termed the Fundamental Review of the Trading Book (FRTB). With the new rules, some Canadian banks will move to calculating capital requirements using a regulatory Standardized Approach. The goal of the research is to analyze the drivers of the FRTB capital charges and contrast these against the drivers of current regulatory capital, and both against theoretical ideals for economic capital requirements.
Climate change is one of the greatest challenge society has ever faced, with increasingly severe consequences for humanity. Climate change also creates risks to both the safety and soundness of the individual firms and to the stability of the financial system.
In Canada and around the world, investors hire financial advisors and dealers to manage, monitor, and guide their investment choices purchased from a financial dealer. Dealers and advisors are obligated by regulations--introduced in 2009 by the Ontario Securities Commission (OSC)--to ensure that their investment products and recommendations are "suitable".
Users on the League platform have access to a number of health and wellness benefits including massage, physiotherapy, personal trainers and a variety of other programs; however, not all of them fully utilize them to maximize their wellbeing. Utilizing the health and program utilization data we want to develop robust personalized predictions that will suggest to individuals, programs that they are eligible for and would benefit their health.
In this self-contained project we will investigate how machine learning can be applied to help provide personalized financial advice. Machine learning is a term that designates types of artificial intelligence that rely on learning behaviors from data or experience. Specifically, the goal of this work is to apply machine learning to Servus Credit Union’s Noble Purpose “Shaping Member Financial Fitness” to provide personalized recommendations to individual members who have set specific financial goals.
Sustainable investment is an expending sector of the mainstream financial market, yet there are few studies evaluating the trends, opportunities, impacts and knowledge gaps as they relate to Canadian investors. Understanding the environmental, social, and governance (ESG) issues related to business operations and investment are critical to understanding trends that are driving this shift towards sustainability in financial markets.
Cyber insurance is a relatively new and growing insurance product that provides companies with compensation following cybersecurity incidents involving data breaches, business interruption, digital asset loss and/or cyber extortion. The ever-changing nature of cyber technology combined with the lack of a large history of cyber insurance claims makes it challenging for insurance companies to rapidly assess risk and determine appropriate premiums for all of their cyber insurance clients, especially for small-to-medium sized enterprises.