Effective Climate Governance for Canadian Credit Unions: Cooperating for a Greener Future

The goal is to develop a best practice guide for Canadian credit unions, focusing on the legal duties of Canadian directors and officers in respect of managing climate change, best practices for corporate boards, reporting on the latest global research on best climate governance practices for credit unions.

Advanced Quantitative Behavioral Models for Asset-Liability, Interest Rate Risk, and Liquidity Management in Deposit-Taking Financial Institutions

Cashflow uncertainty due to customer behaviors poses special challenges to a bank’s ability to accurately forecast its future cashflows, and therefore makes its funding and risk management difficult. In the proposed research, we plan to use cutting-edge machine learning techniques to study the behaviors of bank depositors and borrowers in Canada using an extensive proprietary data sample of the Partner Organization (i.e., EQ Bank).

The Innovation North Compass: A Made-In-Canada Approach to Innovation

Traditionally, firms approach their innovation agenda focused on profits. Conventional innovation processes, such as stage-gate and design approaches, can create harm for the world by ignoring the wider ecological, social, and economic systems in which a firm is embedded. There is a better way. This project will develop an innovation tool that creates prosperity and well-being for everyone by applying systems thinking to the innovation process. This requires creativity, teamwork, the inclusion of diverse stakeholders, and a commitment to a better future for all.

Using VHF telemetry to inform nest-site-selection of western painted turtle (Chrysemys picta bellii) in the Kootenay region of British Columbia

The purpose of this project is to study the behaviour of western painted turtles during the nesting season. These species are currently in decline in British Columbia due to urbanization and a loss of nesting habitat. In recent years, the construction of man-made nesting beaches has been widely used in an effort to help this species.

An Application of Machine Learning to Mortgage Prepayment Modeling

The business partner is interested in expanding its understanding of prepayment. Specifically the goal is to predict prepayment risk for mortgages as a function of mortgagors’ characteristics (including data from previous interactions with the bank), and the local economy. In recent years, with the improvement in efficient computing and data storage, the relying on a wide range of mortgagors’ characteristics to predict prepayment risk has become more prevalent in the industry.

Fundamental Review of the Trading Book: Explainable Equity Volatility Models with Event Risk

The Fundamental Review of the Trading Book is a set of regulations set by the Basel committee, which is expected to be implemented by banks in Canada by late 2023. According to these regulations, in order to maintain stability in the banking system, banks need to post extra capital against the so-called non-modellable risk factors. As this extra capital could significantly increase the total market risk capital requirements for a bank, reducing the weight of these non-modellable risk factors can greatly increase the bank’s profitability.

Reasoning and Abstraction in NLP with Explicit Semantic Structures

Computers that can understand and communicate in human languages would benefit a wide range of application domains, from finance, e-commerce, legal to health care. In recent years, deep learning has dramatically accelerated natural language processing research by allowing models to learn statistical patterns from massive amounts of data. However, current models are still weak in terms of their reasoning and abstraction ability. This shortcoming limits their robustness when facing natural environment changes or adversarial attacks.

Research of Enhanced Analytical Applications for Investment Funds and Portfolios; Development and Design of Statistical Risk Models and Dashboards

Anchor Pacific Financial Risk Labs (“AP Fin Labs”) in partnership with SFU and MITACS, seeks to research, develop, and design an Investment Portfolio Analytics Data Engine and Graphic User Interface (the “Project”) for commercial delivery as an enterprise offering for wealth management firms and their investment advisors, as well as other investment firms and asset owners.

Situation awareness in complex medical decision-making

Medical errors continue to occur in the Canadian healthcare system, with errors not only leading to patient harm but also to costly litigation. Some of the costliest litigation relates to low frequency, but high impact, events. The aim of this research will be to design, and assess the feasibility of, interventions that can lead to fewer costly medical errors by improving the situation awareness of medical personnel. We will first examine the behavioural traits of the people who are cited in medical litigation cases.

Operationalizing Bayesian multi-state models and financial institution resources for business firm life cycle modelling

Like most living organisms, the life cycle of a business can be divided into distinct, complex phases. These life stages are determined by various internal and external factors such as financial resource availability, managerial ability, and market conditions. The ability to model firm life stages would help financial institutions (FIs) such as ATB identify and meet the time-specific needs and challenges of their business clients. However, properly analyzing the wealth of data collected by FIs is difficult.