Skip to Main Content
Insurance underwriting is characterized as an ordinal classification problem since the underwriting process consists in assigning an application to one of an ordered set of risk categories. In designing ordinal classifiers, it is important to leverage the ordering information of the target classes to improve classification performance. In this paper, we explore several strategies for designing neural network based classifiers for ordinal classification. We investigate four different designs and evaluate their classification performance using real-world data from an automated insurance underwriting application.
Date of Conference: 0-0 0