Risk aggregation is omnipresent in insurance applications. A recent example, borrowed from the modern regulatory accords, is the determination of the aggregate economic capital and its consequent allocation to risk drivers. A more traditional illustration of the importance of risk aggregation in insurance is the celebrated collective risk theory that dates back to the early years of the 20th century. This project will assist Sun Life Financial to build and implement an efficient quantitative framework to approximate the aggregate risk of its portfolio.

Theft or loss of sensitive data is a growing concern for companies who may suffer losses of consumer confidence, market valuation and intellectual property when large amounts of data are stolen. In this research project we will use an enhanced “screen and review” approach to combating exfiltration in a large data set of activity logs within a large corporate network.

Many governments and other organizations hold confidential data. Theft of that data can be extremely damaging both to the organization and to the people whose data has been stolen. Massive breaches each involving millions of people have been occurring on a regular basis in recent years. New Cyber Security tools are needed to help people determine the threats that exist and to provide active online monitoring that can detect unusual behavior as it happens.

Traditional insurance models build on the assumption of independence of risks. One of the main causes of the recent financial crisis, this assumption has facilitated the quantification of risks for decades, but it has often lead to risks' under-estimation and as a result under-pricing. Importantly, one of the prime pillars of the novel concept of Enterprise Risk Management is the requirement that insurance companies have a clear understanding of risks' interconnections within the risk portfolios. However, modeling dependence is not an easy call.

Although beta distribution models are a well-known tool for evaluating the recovery risk of credit instruments, concerns are raised regarding tractability its analysis and simulation. The project attempts to address such concerns by incorporating a mixture beta distribution model. The project will compare the efficiency of the proposed model with the commonly used beta distribution model. In addition, the intern will compare the mixture beta distribution model with the credit risk model that is currently employed by Sun Life Financial.

Although latent variable models are a well-known tool for evaluating a portfolio credit risk, concerns are raised regarding tractability of a subsequent analysis/simulation. The project attempts to address such concerns by incorporating a special class of Bernoulli mixture models. Then, efforts will be made to compare efficiency of these models with the commonly used latent variable counterparts and the benchmark model suggested by the regulator.