The insurance sector is evolving, with customers now holding more sway than before. Companies must re-think how they provide their services to customers, incorporating greater flexibility to accommodate changes in information and plans.

Although Mid-term Adjustments (MTA) in insurance plans have become commonplace, the process generates significant paperwork. The challenge is further exacerbated when the process is manual and involves numerous change requests.

Thus, automation in insurance has become critical. Implementing an automated and scalable workflow system that can extract and contextualize data using intelligent algorithms will help insurers improve efficiency, accuracy and Turnaround Time (TAT).

It processed over 100,000 MTAs annually, necessitating a colossal volume of administrative work, complex workflow management, coordination and multiple applications. The process involved manual information updating, laborious underwriting procedures and change communication with customers.

While an extended TAT negatively impacted customer experience, manual data extraction led to inefficiencies, errors and steep operational costs.

WNS Analytics (WNS’ data, analytics and AI practice) collaborated with client stakeholders to identify workflow management as the primary challenge. To address this, we proposed an AI / Machine Learning (ML) solution to digitize and expedite the MTA process.