The field of artificial intelligence is traditionally divided into two broad paradigms. On one hand there are symbolic, formal, procedural, deterministic, and/or rule-based methods that often rely on a set of atomic elements and rules operating on those elements. Sometimes they are as complex as a comprehensive reasoning system. It is relatively effortless to provide a few manual instructions to these systems, however, these instructions (i.e., rules) are labor-intensive and become unfeasibly time-consuming as the complexity of the system grows beyond a certain point.

Two overarching approaches to allocate the aggregate risk capital stand out nowadays. These are the top-down approach that entails that the allocation exercise is imposed by the corporate centre, and the bottom-up approach that implies that the allocation of the aggregate risk to business units is informed by these units. Briefly, the top-down allocations start with the aggregate risk capital that is then replenished among business units according to the views of the centre, thus limiting the inputs from the business units.

Like many of their global compatriots, Canadian banks have embraced digital transformation, which enables their account holders to access and manage their accounts and investments online, allowing personalized service. This reality includes the needs of internal stakeholders as well as clients. Planning and implementation of Customer Relations Management and digital sales systems require new ways of working that in turn implicate policies and procedures. Internal culture is impacted as banks strengthen their talent acquisition in AI, Machine learning and data analytics.

This project is designed to assess both natural variability and the future change of forest productivity and natural disaster risks that are related to climate. These areas are important to study as climatic change is projected to impact northern latitudes more strongly and disasters, such as floods, droughts, and fires, are predicted to increasingly impact human populations and infrastructure. To assess these components, a combination of satellite remote sensing, in-situ and UAV data will be utilized in conjunction with large ensemble modelling.

The wide adoption and development of wireless sensing technologies for the monitoring and autonomous identification of financial activities have affected financial institutions in the past decade. However, wider utilization of RFID technologies in the banking sector has introduced challenges regarding the security and privacy of sensitive financial data. The proposed innovations and technological developments will revolutionize the banking sector by increasing efficiency, decreasing cost and provide secure and privacy sensitive financial transactions.