Computational Analysis of Insurance Complaints: GEICO Case Study
This provides a method for insurance companies to efficiently process customer feedback, though it is incremental as it applies existing topic modeling techniques to a new dataset.
The research tackled the problem of analyzing a large number of online insurance complaints by proposing a computational approach using topic modeling, resulting in the identification of 30 major complaints across four categories from 1,371 GEICO reviews.
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.