The project aims to use state-of-the-art machine learning techniques to perform model validation. In particular, the intern will validate outcomes from risk assessment models for loan portfolios. The results will be employed to further the efficiency of ATB’s internal stress testing models. The benefit for ATB financial will be the possibility to detect subsamples for […]

The Embedding Project is a public-benefit research project that relies on strong social science research methods to bring together a global network of business sustainability change agents and harness their collective knowledge to develop rigorous and practical guidance that benefits everyone. This internship will offer an MBA student the opportunity to gain experience in both […]

This project is the first step to providing Thinking North’s Purple Squirrel recruitment platform to go beyond traditional matching with a novel, data-backed holistic candidate matching process. To provide a robust system, Thinking North is collaborating with Seneca’s School of Software Design and Data Science to use advanced artificial intelligence and gamification techniques to combine […]

This project targets development of applied methods and practical solutions to risk management problems where only partial observation of a system is possible. Such settings are commonplace in financial and other context but can be challenging to address due to a limited number of production-grade ready-to-use solutions. The scientific component of the project employs approaches […]

In this research project, we will partner with the Financial Services Regulatory Authority of Ontario (FSRA) to enhance its default prediction model for private companies administering pension plans in Ontario. Our goal is to enhance the current model’s timeliness in predicting default of private companies by addressing the lack of publicly accessible information from these […]

Borealis AI has access to a huge amount of financial data related to the stock market and is interested in leveraging recent developments in machine learning to better understand this data. Some potential questions emerging from this data are: (1) Given the closing price of a stock in the recent months, can we predict the […]

In Finance, the use of Automated Trading Systems (ATS) on markets is growing every year and the trades generated by an algorithm now account for the majority of orders that arrive at stock exchanges. Historically, these systems were based on advanced statistical methods and signal processing able to extract trading signals from financial data. However, […]

Quick Response Research has long allowed social, behavioural and economic science researchers to collect and integrate valuable first-response data in time-sensitive environments. This type of research is conducted during or shortly after an extreme event and allows social science researchers to collect perishable data that wouldn’t be accessible otherwise. While quick response research has been […]

A geospatial query is a question where the concept of location is necessary for formulating the answer. Furthermore, we are not simply interested in spatial relationships, but also with the ways in which people can possibly move through space given the goals that they want to achieve. We therefore want to predict the behaviour of […]

This project is about implementing a technique called Agent-Based modelling (ABM) so it can work better in real-world application. Particularly, it aims to help policy makers to do more adaptive decisions when the whole economics environment changes. For example, how to set the federal interest rate after COVID-19 panic? This model could simulate how all […]

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. According to FRTB, banks need to post extra capital against non-modellable risk factors, which could account for 30% of […]

In AI safety, compliance ensures that a model adheres to operational specifications at runtime to avoid adverse events for the end user. This proposal looks at obtaining model compliance in two ways: (i) applying corrective measures to a non-compliant Machine Learning (ML) model and (ii) ensuring compliance throughout the model’s training process. We aim to […]