Computational Analysis of Insurance Complaints: GEICO Case Study

TL;DR

This paper presents a computational topic modeling approach to analyze thousands of online insurance complaints, revealing key issues in customer service, coverage, legal, and payment categories for GEICO.

Contribution

The study introduces a novel application of topic modeling to efficiently analyze large-scale online complaints in the insurance sector.

Findings

Identified 30 major complaint topics across four categories.

Demonstrated the effectiveness of computational analysis for large review datasets.

Provided insights into customer concerns for insurance companies.

Abstract

The online environment has provided a great opportunity for insurance policyholders to share their complaints with respect to different services. These complaints can reveal valuable information for insurance companies who seek to improve their services; however, analyzing a huge number of online complaints is a complicated task for human and must involve computational methods to create an efficient process. This research proposes a computational approach to characterize the major topics of a large number of online complaints. Our approach is based on using the topic modeling approach to disclose the latent semantic of complaints. The proposed approach deployed on thousands of GEICO negative reviews. Analyzing 1,371 GEICO complaints indicates that there are 30 major complains in four categories: (1) customer service, (2) insurance coverage, paperwork, policy, and reports, (3) legal issues, and (4) costs, estimates, and payments. This research approach can be used in other applications to explore a large number of reviews.