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.

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.

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.

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.

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.

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.

Existing data lake systems lack the support for storing or discovery features that could be used with different ML projects.
These limitations negatively affect the process of decision-taking. Data scientists spend most of their time finding, preparing,
and integrating relevant data sets to finish analytics tasks. Feature discovery systems are needed to ease the process of building
data science pipelines to drive significant insights efficiently, effectively and fairly.

Venture capital X (VCX) is about to launch a new product: Sustainability-Linked Loan (SLL). These new sustainability-linked financial products represent an interesting opportunity both for lenders and borrowers. To incentivize borrowers to achieve predetermined sustainability objectives, VCX offers reduced interest rates on loans; however, these SLL are contingent on borrowers meeting predetermined sustainability targets.

The Fundamental Review of the Trading Book (FRTB) is a set of regulations by the Basel committee, which is expected to be implemented by banks by 2022. The regulation targets market risk management in banking industry. The regulation targets market risk management in banking industry. According to FRTB, banks must decompose funds that can be looked through into their constituents and determines the relevant capital requirements as if the underlying position were held directly by the bank.

ML/AI is widely used and deployed in many industries. Its deployment in Asset Management industry (and
especially in Canadian pension fund sector) is significantly behind. Part of it is the fear of “black box” and what recommendation it gives. This sentiment is outdated as the recent advancements in ML/AI allow looking inside the “black box and thus focus on “white box” asset allocation recommendations.
Another reason is that asset management these days is the intersection of three disciplines: Financial
Economics, Statistics, and Computer Science.