Interpretability of ML models in pricing

[kevinykuo]

It is widely accepted that modern machine learning (ML) techniques, such as gradient boosted trees and deep neural networks, have better predictive performance than traditional predictive modeling techniques such as generalized linear models (GLM.) However, one major obstacle for moving beyond GLM in pricing is the perceived lack interpretability of the ML techniques. This project will investigate techniques for model interpretability in the context of regulated environments in the US.

The deliverable of this project will include a research paper and potentially contributions to existing open source packages.

Currently recruiting

• Pricing actuary with experience in predictive modeling and filing rates in different US jurisdictions.

[kevinykuo]

Funding proposal (CAS) in progress.