Machine learning techniques have been applied to the financial industry for some time. They have allowed large utilities and generators to better forecast their needs, and the prices they will pay, leading to a generally more efficient grid. However, very little research has been done that could benefit power marketers, who do not have a load to serve or a generating facility to manage. The application of machine learning techniques has yielded great results in the financial industry.

The objective of the project is to design a model determining capital requirements associated with property and casualty insurance business lines for an insurer that is compliant with the new IFRS 17 framework (international accounting framework). Several subcomponents of the model will be developed such as a dynamic model embedding dependence for the evolution of incurred but not reported (IBNR) claims, a risk measurement component with risk measures and an allocation framework for capital requirements across the various business branches of the insurer.

The project involves identifying changes in mineralization during the drilling of the blast holes. During drilling, an experienced driller is able, to a certain extent, to detect signals that indicate that a zone change is occurring: vibration in the cabin, rotation rate, etc.

This project will assist Sun Life Financial to build, implement and validate quantitatively sophisticated state-of-the-art models of its risk portfolio. This will result in a better quantitative and qualitative understanding of company's risk, liability and capital profile, and thus in more effective risk management decision making process.

Cryptocurrency markets exhibit highly chaotic behaviour, differing substantially from securities. This research project looks at the cryptocurrency markets for data–aiming to answer if it possible to create mathematical models which track the overall behaviour of the Cryptocurrency Market, while minimizing risks. Through this research we expect to reconcile the theory developed above with the real life cryptocurrency exchanges and coins.

Within the aerospace sector, aftermarket services account for over 50% of revenue generated by aero engine manufacturers. Central to this is the ability to inspect and repair high unit cost components. Many processes are manual but given the ever-increasing quality, cost and delivery requirements, and the safety critical nature of these rotating parts, there is a strong drive towards process automation.

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.