More specifically, from SSQ’s corporate data, the research team aims to create the most accurate, fair and equitable predictive models for customer needs in terms of insurance products and for certain aspects of their behaviour, such as the likelihood that a customer will not renew a particular insurance policy.
The team also aims to set up accurate and fair fraud detectors capable of detecting fraud at an early stage and detecting new types of fraud.
To achieve these goals, the research team will need to adapt existing machine learning algorithms in innovative ways and design new ones, such that they can use and combine different data sources during learning, some of which are sequential in nature. Additionally, we will need to find ways to enforce fairness in machine learning algorithms, so that the predictors generated by these algorithms will not indirectly use sensitive attributes (such as race, ethnicity, religion, etc.) so as to make the algorithms perform unevenly among different groups of individuals.