There are numerous financial goals that most Canadians face. Retirement, funding post high school education, managing debt, purchasing appropriate amounts of insurance and saving for lump sum purchases. Each of these goals has various accounts and savings vehicles associated with them. The research projects we are proposing will help Canadians define their own financial situation, focus on their goals in the optimal order, and best utilize savings vehicles and government benefits to best meet their goals. Glencairn Financial Inc.
This project evaluates the impact of customer service on customer retention and churn. In the first phase, we build a statistical model to examine drivers of customer loyalty. In the second phase, we work with customer service to evaluate the effectiveness of new customer service strategies. This project will enable the company to better predict customer retention and churn by using appropriate metrics. In addition, the company can understand the impact of alternative customer service strategies on customer loyalty and can choose the most effective one for implementation.
This research project aims to evaluate whether members of minority groups or women face higher barriers to access credit in the small and medium-sized enterprises credit market. The intern will analyze loan-level data provided by the business partner to evaluate whether these biases are detectable in the portfolio of SME loans of the business partner. Discrimination in credit allocation prevents efficient credit allocation, besides being demeaning for the individual subject to discrimination.
Financial indicators of an individual firm may be in the form of time series, vectors, or even richer data, such as text or images. The purpose of this work is to explore and develop methods for dealing with such data, and in particular perform the clustering/classification of such data into similar groups. In the project the intern will develop the tools that will allow to determine whether a client should be issued a loan or not.
Artificial Intelligence (AI) has attracted significant attention in both industry and academia recently. On one hand, people are feeling excited about seeing the breakthroughs that AI has made. On the other hand, they are also worried that these advanced AI technologies will only be mastered by a very small number of organizations in the future. Therefore, there is a strong need to democratize AI (i.e., make AI accessible for everyone).There are three kinds of resources that AI requires: Algorithm, Computation, and Data.
Nuera has been acquiring customers and data for the past 3 years and is launching an internal initiative to mine the data to find insurance claims trends. The objective of this initiative will consist of analysis and reporting on our data sets to find trends that lead to claims frequency and severity, in an effort to reduce claims costs, and consumer insurance pricing as a result. We will also be identifying customer behaviors and how those behaviors contribute to insurance purchasing and claims.
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 this joint collaboration with BNS, we will develop a behavioural risk model to predict the likelihood of future risk of breaking the promise to pay debt for customers who only hold deposit products with BNS. The model will be utilized to support business operations such as credit card and loan pre-approvals. That is to say, if you are a customer who only have chequing, saving and/or investment accounts with BNS and plan to buy a car, you will be scored in this model for the car loan pre-approval.
This proposal deals with the pricing and risk management considerations of a property and casualty (P&C) insurance company. These considerations are within the context of a new accounting standard called IFRS 17, in which liabilities in insurance contracts will be measured prior to and during the exposure periods. We propose an implementable and accurate methodology, which is also compliant with the new standard in generating risk measures and margin adjustments.
We seek to replace or enhance the traditional underwriting approach (namely identification of insureds via a pre-defined fixed set of risk criteria) with one based on a set of dynamic protocols that are responsive to human behavioral factors for continual health improvement. We seek to provide a live and interactive in-market research dataset that can be used to explore the benefit of and improve data-driven approaches (namely artificial intelligence or AI) for immediate use in life & health insurance product development and actuarial risk assessment.