Uplift models extension for smart marketing

Insurance companies heavily fund marketing campaigns such as, for instance, customer retention or cross-sell initiatives. Uplift modeling aims at predicting the causal effect of an action such as medical treatment or a marketing campaign on a particular individual by taking into consideration the response to an action. Typically, the result of an uplift model is used to call customers for marketing some products based on important attributes of a customer.

Automated transaction classification using machine learning algorithm

The procurement process of an organization is key to understand company costs. Organizations gather large amounts of data coming from different sources (e.g. income statement, balance sheet, general ledger lines). This information is heterogeneous in nature as it is a mix of unstructured and structured data. Moreover, it needs to be cleaned and consolidated in a taxonomy to enable category management. The objective is to group like-to-like items and/or services into categories from Supply Market Analysis point of view and consider category management for the holistic spend.

Machine Learning for Improved Automated Valuation Model (II)

To estimate the market value of a real estate, a computer software program can produce the result by analyzing the location, market conditions, and other characteristics relevant to it. This is known as the 'automated valuation model (AVM)'. The previous Mitacs Accelerate project with 'Data Nerds' has conducted a series of experiments with the attributes of real estates and geographic dependencies. This project is to further develop the AVM by incorporating temporal factors. The deep learning based approaches will be employed in this project.

Exploring optimal trading rules in a high-frequency portfolio

Given a set of financial instruments with inherent characteristics at different time intervals, we are interested in finding an optimal trading rule in a high-frequency trading context. A trading rule is defined as a combination of indicators as well as an entry threshold (and potentially other trading parameters). The objective function we are trying to maximize is the profits of the strategy based on the trading rule. One impact of the non-linearity of such problems is that the gradient of the objective function is hard to estimate using a black-box approach.

Benefits of VaR for capital management process of credit unions.

PRO Financial Solutions (PRO) is looking to investigate the benefits of actively including Value at Risk (VaR) in the capital management process of credit unions. Whereas Earnings at Risk (EaR) is a well-established risk measure, VaR still lacks understanding among credit unions. In this research project, the intern will illustrate the relationship between EaR and Duration of Equity, a VaR proxy, and therefor explain VaR in terms of EaR ? a number credit unions are more familiar with.

The impact of Flood risk on the value of residential property: The case of Quebec city

Whether due to urbanization or climate changes, flood events have an impact on property value. Newly available geography data about flood risk zone have yet not been utilized to their full potential in Canada. This project aims to study the impact of the flood risk zone on the value of homes for Quebec City, and to identify other regions in Canada where similar conditions could affect housing value. The development of a strategy to expand the aim of research at the country-wide level will else be part of the project.

Group IT usage, diversity and innovation

This proposal is part of a larger research program that aims to study the relationships between IT usage for communication, diversity in social and technical knowledge, and innovation in groups. We have developed a research framework based on social network theories to suggest two different mechanisms for relating diversity and IT usage to novel idea generation and implementation. Our findings will help the partner organization to improve its capability to innovate by better forming and managing diversity within and across group in the context of innovations labs.

Implementing Factor Models in Investment Management

The internship will consist of studying, building, implementing and testing so called factors that are used to characterize the equities, commodities and currencies that the company invests in. These factors can be thought of as characteristics relating a group of securities that is important in explaining their returns and risk. My task will be first to understand the risk factors that are of particular importance to the company’s investment strategy.

Mortality rate modeling: applications to the pricing of longevity-linked financial derivative instruments and a study of the effectiveness of these hedging instruments in a pension risk management str

We are all well aware of the spectacular improvement in life-expectancy around the world since the 1990’s. While most people would agree that living longer is a good thing, it nonetheless increases the risk of having people outlive their assets so that they become forced to accept lower standards of living in old age. People with a defined benefit pension plan or people with a life annuity contract have transferred their individual “longevity risk” to their Pension Fund or to an Insurance Company.

Validation of a Machine Vision-based System for the Recognition of Indian Coins

Counting coins, with speed and accuracy, has been a challenging issue for banks and stores. People used to count coins manually before the arrival of coin counting machines. The process of counting coins manually is a very time consuming and tedious job. Moreover, mistakes are likely to occur due to various reasons such as fatigue, eye tiredness and too many coins of nearly same shape and size cause confusion in sorting and counting. Coin sorters are common in North America and can be found in most commercial banks and even some grocery stores.