Interpretable Machine Learning for Predictive Analytics in Employee Benefits Insurance

In recent years, many machine learning methods have been developed for predictive analytics and automated decision making. However, the lack of explanation resulted in both practical and ethical issues. In this project, we will employ and advance interpretable machine learning methods for various predictive analytics tasks in employee benefit insurance. The proposed methods can be used by the partner organization to improve transparency and hence trust in a wide range of applications that involve predictive analytics.

Faculty Supervisor:

Majid Komeili

Student:

Partner:

Global IQX

Discipline:

Computer science

Sector:

Finance and Insurance

University:

Carleton University

Program:

Accelerate

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