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.

A Feature Discovery System for Data Science Across the Enterprise

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.

Creating a Sustainability Reporting Framework for Pace Zero’s Sustainability Linked Loans (SLLs) Borrowers

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.

Linear and non-linear replication factor models for Funds look-through

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.

Applications of ML/AI in Asset Management - part 2

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.

Applications of ML/AI in Asset Management - part 1

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.

Developing a standardized, commercially viable and scalable Software-as-a-Service model which can be customized for customer retention, acquisition, and monetization using predefined adaptation strategies

Apps, mobile games, cloud based services have become ubiquitous and integral to our daily lives. These apps, although free to use, can be expensive to produce and make money either through ads or in-app or program purchases. Companies developing these services have to make software that are not only easy to use and attractive, but also integrate money making attributes without affecting user experience, thereby building a loyal user base.

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