Modelling the Dependence between Loss Frequency and Loss Rate

Lending to various companies and individuals is a core business of banks. This lending activity comes with credit risk, namely the risk that some borrowers default and fail to make required payments. Estimating credit risk accurately is important for banks’ risk management. In this project, we analyze and model the dependence between loss frequency and loss rate of defaulting customers. The reason for the dependence comes from the underlying economic cycle: in an economic downturn, losses occur both more frequently and more severely than in an economic boom.

Non-convex learning with stochastic algorithms

In recent years, deep learning has led to unprecedented advances in a wide range of applications including natural language processing, reinforcement learning, and speech recognition. Despite the abundance of empirical evidence highlighting the success of neural networks, the theoretical properties of deep learning remain poorly understood and have been a subject of active investigation. One foundational aspect of deep learning that has garnered great intrigue in recent years is the generalization behavior of neural networks, that is, the ability of a neural network to perform on unseen data.

Enabling Purchase of Residential Homes at Scale

Properly buys and sells homes directly from consumers. For our business to be successful, we must be able to predict two things when making a home purchasing decision: ? The price it would sell for on the open market ? How much time it will spend on the market to sell at that price These two variables are correlated: price can affect time-on-market, and time-on-market can affect price. There are many other factors at play as well. In the broader market, these complex real estate decisions are largely made using human judgement, based on experience and expertise.

Probabilistic Transitive Closure of Fuzzy Cognitive Maps: Algorithm Enhancement and Application to Work Integrated Learning

Shopify has a well-developed partnership with universities for a work-integrated Bachelor of Computer Science degree. It is in their best interest to see their student interns successfully transition to the work place. In the application part of this study, we will use a fuzzy cognitive map (that is, a special type of a graph) to represent expert knowledge on determinants of success and well-being of a student intern, Moreover, a relatively new mathematical model – transitive closure – will be applied to analyze this data and compute a set of guidelines for better learning outcomes.

Evaluate effect of chemical compounds on plants using statistics and machine learning

To understand the effects of various substances on plants in terms of yield and disease severity (phytopathology), we need to evaluate both statistical significance and biological relevance when conducting biological experiments. Biological relevance refers to the nature and size of biological changes or differences seen in studies that would be considered relevant, while Statistically significance is the likelihood that a relationship between two or more variables is caused by something other than chance.

Statistical and Physiological Beat Modelling of Seismocardiogram Signal - Year 2

"Seismocardiogram (SCG) is a signal that is captured by placing an accelerometer on the human chest. This signal captures very important timing information such as opening and closing of the heart valves. In addition to these timing information, the non-invasive nature of this signal makes it an attractive solution for remote monitoring of patients with heart conditions.
The morphology of SCG signal changes depending on different types of heart conditions and diseases. A mathematical model represents the morphology of a signal in terms of certain parameters.

A behavioural risk model for deposit only customers

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.

Setting risk margin for claims and premium liabilities in accordance with IFRS 17

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.

Accessible data platform for dynamic experience study of lifestyle underwriting

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.

Generalization in Deep Learning

In recent years, deep learning has led to unprecedented advances in a wide range of applications including natural language processing, reinforcement learning, and speech recognition. Despite the abundance of empirical evidence highlighting the success of neural networks, the theoretical properties of deep learning remain poorly understood and have been a subject of active investigation. One foundational aspect of deep learning that has garnered great intrigue in recent years is the generalization behavior of neural networks, that is, the ability of a neural network to perform on unseen data.

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