Towards Explainability of Machine Learning Models in Insurance Pricing

03/24/2020
by   Kevin Kuo, et al.
0

Machine learning methods have garnered increasing interest among actuaries in recent years. However, their adoption by practitioners has been limited, partly due to the lack of transparency of these methods, as compared to generalized linear models. In this paper, we discuss the need for model interpretability in property casualty insurance ratemaking, propose a framework for explaining models, and present a case study to illustrate the framework.

READ FULL TEXT VIEW PDF
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

09/19/2019

InterpretML: A Unified Framework for Machine Learning Interpretability

InterpretML is an open-source Python package which exposes machine learn...
07/12/2021

Quantifying Explainability in NLP and Analyzing Algorithms for Performance-Explainability Tradeoff

The healthcare domain is one of the most exciting application areas for ...
05/11/2018

Machine Learning for Public Administration Research, with Application to Organizational Reputation

Machine learning methods have gained a great deal of popularity in recen...
07/02/2020

Addressing the interpretability problem for deep learning using many valued quantum logic

Deep learning models are widely used for various industrial and scientif...
04/09/2021

Individual Explanations in Machine Learning Models: A Survey for Practitioners

In recent years, the use of sophisticated statistical models that influe...
05/13/2021

Explainable Machine Learning for Fraud Detection

The application of machine learning to support the processing of large d...
10/01/2018

Utilizing a Transparency-driven Environment toward Trusted Automatic Genre Classification: A Case Study in Journalism History

With the growing abundance of unlabeled data in real-world tasks, resear...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.