Today’s claims processing involves a great deal of manual effort. Claims managers and adjusters often spend most of their time looking for data scattered in different systems instead of focusing on analyzing claims and delivering optimal service to customers. In addition, many existing tools for claims processing lack the ability to quickly distribute analytics and actionable insights to the relevant end users who need them most to drive informed business decisions. As a result, insurers are left with poor operational efficiency, increased cost, greater risk exposure, and low customer satisfaction scores.
An end-to-end claims analytics solution that automates data collection, performs predictive analytics and distributes insights can not only alleviate the burden of manual processing, but also provide more comprehensive understanding of each claim to support better decision-making.
In this session Neal Silbert, a well-respected insurance industry veteran, and Mark Rusch, VP of Insurance at GoodData, will discuss how an advanced analytics solution powered by machine learning and AI can:
- Enable a 360-degree view of claims that drives intelligent decision-making
- Provide deeper insights into new and existing claims to effectively identify fraud and reduce future losses
- Automatically deliver continuous improvement of the overall claims process based on a closed-loop feedback system
- Improve business agility by quickly aligning your operations with the ever changing business environment