Clinical logistics has more than 20 years of experience in providing clinical samples to some of the largest pharmaceutical companies for the clinical studies. These samples are mainly used in clinical studies for research and development of new drugs. Thus the quality and timely provision of sample is of utmost importance. Currently the operation of clinical sample collection and management is performed manually. This makes the operation error prone and limits its scalability.
Counterproductive Work Behaviours (CWB) refer to negative behaviours in the workplace that hinder organizational effectiveness. CWB ranges from violence and harassment to stealing and drug consumption on the job. Despite its importance and prevalence, little attention has been paid to leader CWB, as the focus has been mainly on employee CWB. Leader CWB, could be largely attributed to negative aspects of individuals’ personality. Therefore, the first objective is to discover what types of personality based CWB are more prevalent in leaders.
In this joint collaboration with Scotiabank we hope to solve a commonly faced problem by large financial institutions. It is to detect errors in financial datasets. This could be due to typing errors made by a human or a computer glitch that causes an incorrect value to be stored. To identify these errors, we plan to build an error detection system. It will model how financial variables change in relation to other variables. This will help us identify groups of variables that move, through time, in a similar manner. With this knowledge we will then be able to spot errors in the data.
PACICC role is to compensate policyholders in scenarios where a P&C insurer can no longer provide compensation while overseeing the health of the P&C industry in Canada. The proposed project aims to improve PACICC’s ability to identity companies at risk of insolvency and improve strategies to minimize dead weight loss when insolvency is imminent.
Machine learning (ML) is a method of training a computer to learn from data and predict future outcomes based on existing patterns in the data. This project aims to utilize various ML methods as new and potentially better analytics and predictive tools in the area of credit risk management for ATB. Given that data quality and flows change over time, a new framework built on Google Cloud Platform to update the machine learning models will also be developed.
Electronic exchanges are venues that provide immediacy for those who need to find a counterparty to their trades. Orders of various types arrive in the market at ever increasing speeds, and in this era of high-frequency trading (HFT), institutional investors are often disadvantaged because of their high-latency relative to faster traders.
Risk aggregation is omnipresent in insurance applications. A recent example, borrowed from the modern regulatory accords, is the determination of the aggregate economic capital and its consequent allocation to risk drivers. A more traditional illustration of the importance of risk aggregation in insurance is the celebrated collective risk theory that dates back to the early years of the 20th century. This project will assist Sun Life Financial to build and implement an efficient quantitative framework to approximate the aggregate risk of its portfolio.
Asset allocation – the decision of how to divide a portfolio among the major asset classes such as cash, stocks and bonds – is a key determinant of portfolio performance. Because financial markets go through periods of strong and weak economies, the performance of an asset class varies with shifting economic conditions. These regime shifts pose a challenge to the asset allocation decision because they impact the portfolio’s return and risk.
This research focuses on three substantive areas (1) curriculum and instruction in early childhood education, (2) international relationships and partnerships in international business and (3) business management in knowledge transfers related to the challenges and possibilities of how early childhood education engages the various national settings in the global context. This study will identify the tensions between the goals of Canadian and Chinese partners. These tensions will be explored using a specific Canadian curriculum applied to a Chinese kindergarten.
The Research Group at CANNEX (formerly known as the QWeMA Group) develops solutions for the financial and insurance industry of North America. Our analytics play an important role in determining the value proposition of investment products. Our solutions help the financial community and public through their financial advisors to be able to make informed decisions. We work at the intersection of finance, mathematics, actuarial science, and computer science.