Insurers deal with a large number of documents in varied formats in their day-to-day operations. The traditional content extraction process has numerous manual touchpoints that make the workstream inefficient owing to frequent human error and associated delays. Moreover, existing targeted technological interventions only go so far because of frequent human reliance for complex, unstructured documents. To add to this, such a content extraction process is difficult to capitalize for wider downstream transformation initiatives.
Existing drawbacks of the traditional process need to be overcome to support a changing leadership vision based on building a digital operating model that can deliver on heightened consumer expectations and stay resilient in the future to come. As such, a modernized, AI-powered content extraction system would be a foundational step in building a strong digital core enabled by intelligent solutions that can empower key insurance operations by freeing up previously spent resources on redundant activities. Such a system will facilitate a quicker and accurate information capturing mechanism that would intelligently upgrade the incoming content stream, further aiding end-to-end automation of associated workflows.
Scope
Industry: insurance
Geography: global
Contents
In this study, we:
Describe the traditional content extraction process, its associated pitfalls in terms of existing inefficiencies, and how it acts as a deterrent to further transformation
Understand the process of modernized, AI-powered content extraction and the enabling role of intelligent automation
Lay down the operationalization of AI-powered content extraction across the insurance value chain to enable downstream efficiencies
