P&C insurers are undergoing massive transformation to remain competitive in the face of strong headwinds in the form of regulatory and cost pressures. Changing customer expectations and the advent of digital-native players have further compelled insurers to modernize their operations and focus on innovation. They have thus begun to invest in technologies such as Robotic Process Automation (RPA) to improve operational efficiencies and reduce processing times. Increasingly, however, they have begun to realize that RPA alone fails to serve the purpose, as such solutions are best suited for rules-based tasks, which require structured data, while many insurance processes rely significantly on judgment-based decisions and unstructured data.
In this study, we discuss the role of Intelligent Document Processing (IDP) in automating data extraction from semi-structured and unstructured documents and converting the data into a structured format using Artificial Intelligence (AI). We also discuss how IDP solutions can be operationalized across various P&C insurance processes to reduce processing times and improve data accuracy.
Scope
All industries and geographies
Contents
In this research, we examine:
The current state of the P&C insurance industry
Introduction to IDP and its capabilities
IDP adoption in the insurance industry
Operationalizing IDP across P&C insurance processes
