We examine consumer-profiling services offered by third-parties with a novel neural network structure and built-in interpretability useful to insurance companies for risk prediction and risk pricing. We test the effectiveness of the proposed methods on public datasets such as Kaggle and UCI, as well as on suitable real-world data from our industrial collaborators.
This project aims to develop methodologies and business solutions that address the problem of how to monetize behavioral data collected across different business scenarios, such as social media, e-commerce, etc., to support new business areas. One focus is how much business value can be added to insurance services using this approach.
The financial service industry is going through a paradigm shift from mass production to personalization. The immediate challenge facing service providers is that it asks for a much higher level of understanding of consumer behavior to survive in a business environment that’s no longer product-centric. When financial services become data-driven, the services provided are naturally customized. It means a service provider must understand customer behavior well enough before it knows who its customers are and how to identify them. One way to reach this level of understanding is through consumer-profiling services. And for the financial industry, machine-friendly labels interpretable by humans are essential when implementing financial models.