An analysis of the impact of FRTB SA Market Risk Capital rules on various Capital Market trade types

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 Risk Valuation – Mapping climatology to macro-economic indicators

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.

Investor Behaviours, Canada's investment suitability regulations, and Robo-advising

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".

Designing ‘Zero credit touch’ (ZCT) pre-approved credit underwriting program for retail customers

ICICI Bank has developed various ‘Zero credit touch’ (ZCT) strategies where without any credit intervention and additional information taken from customers, credit facilities can be provided.

Recommending Benefits Utilization to Promote a Healthy Lifestyle

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.

Helping Servus Members Reach Financial Goals via Transfer Learning

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.

Addressing Knowledge Gap in Sustainable Financing and Investment for Climate Conscious Canadian Investors

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.

Relevance of security intelligence data for cyber insurance risk quantification

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.

Scaling Security Architecture

Security has become increasingly important as cyberattacks are more prominent. Despite advances in security technologies there's still a need for a new security architecture that is simply adaptable and scalable to different and evolving security requirements of various organizations. This project will study the latest technologies and solutions in the field of Cybersecurity that are being used to design scalable cybersecurity frameworks. We will also look at the new technologies and processes that are disrupting the existing Cybersecurity landscape.

Drivers of Time to Resolution, Application of LASSO Regression and Random Forest

International Financial Reporting Standards (IFRS) for loss allowances are changing, and financial institutions are proactively adapting existing methodologies and developing new ones to remain compliant. The main ingredient in the myriad of evaluations that banks are required to perform for compliance is risk assessment. The first goal of this research project is to review best practice risk models, with a special focus on modeling the evolution of default probabilities.

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