Canada’s financial services industry faces significant challenges to remain internationally competitive in the rapidly evolving web and big data environments. Scotiabank and its global competitors have as a key priority effective use of a large and growing amount of data to optimize the design and pricing of product offerings, to communicate effectively with clients, and to mitigate risk.
In the Corporate Tax domain, professionals must review hundreds of documents in the process of filing taxes and rely on experience to identify where benefits or deductibles can be applied. This manual task is subject to human error and can result in unnecessary administrative overhead. With access to PricewaterhouseCoopers’ wealth of tax data, it is possible to develop a tool that uses historical trends to automate this process.
Consistent with industry norms, ATB Financial conducts both mandatory and discretionary stress tests of the whole institution and of its credit portfolio. This project aims to contribute to the refinement of the in-house expertise on methodologies employed to measure credits risk and the overall level of risk of the institution. These activities normally requires management to provide an estimate for ATB’s financial performance, capital and liquidity position conditional on a set of predefined scenarios.
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.