Insurance claim rejection
Identifying fraudulent or incomplete claims
What is insurance claim rejection?
Claim management accounts for a large sum of costs for insurance companies. Insurance claim rejection models deliver an overview of claims that may be fraudulent or incomplete and need to be rejected. With this model in place, insurance companies can lower legal costs and become familiar with common claim issues.
3 facts about the model
Claim rejection as a standard model is a fast track to AI model implementation.
The model uses data from filed claims, customers, and services to predict which to reject.
The model enables you to run more accurate operations.
2021.AI offers claim rejection by using a supervised learning algorithm. The mathematical model is trained on a data set, describing customers, services, and claims, together with a label (supervised) that classifies the case as a rejection or not. The model stores the customer profile and claim filings associated with a high risk of claim rejection, along with the descriptive variables most likely to classify the cases.
The business outcome
Using the Grace Standard Model for claim rejection, the company gets an overview of the likelihood that a filed claim is at risk to be fraud, incomplete, etc. and needs to be rejected. With this information, the company can focus on problematic claims and lower its legal costs. Furthermore, the company gets insights into common issues and tricks used in filed claims, and with this information, it starts to change customer targeting and improve the service.
Interested in taking AI into production?
If you think this model would be relevant for your organization go-ahead and book a demo! We would love to help you get started. Otherwise, you can download our Grace Enterprise AI Platform brochure for more information.