Developed a High Frequency Insurance Claim system derived from the base system to automate back-office business processes. Business processes that were successfully automated include: Claim Entry, Document Request and Reception, Claim Analysis and Payment Generation.

Developed a P&C program comprised of Predictive Modeling, Rating Administration, and Actuarial workbench. Strategically supported architectural direction, determining impacts to tooling, programming model changes, application development, and frameworks.

In a multinational finance and insurance corporation, claims documents are uploaded with searchable metadata to document management systems manually. RPA  integrated with OCR solution extracts the required metadata from claims documents and uploads the documents to the document management system without human intervention. This touchless end to end automated process helps the company reduce human dependency, reduce overall turn around time, provides better searchable metadata for the documents reducing the possibility of human errors.

Developed a new “Rate Capping” system to mitigate renewal premium increases and decreases caused by rate revisions and program changes. This system also enabled the ability to respond to State demands for rate capping being in place.

A Global insurance company generates premium invoices every month. It receives an excel file with a list of policies for premium collected from all channels through email. Each excel has information about customer policy and payment details. RPA solution automates reading email, excel rows, verifies it against mainframe and invoicing system.  The solution finds missing records that are manually corrected by the central processing team and send back to the RPA solution for processing. RPA solution updates all information in a web-based invoicing system to generate and send excel back to the original channels with the required status. This reduces human dependency & errors, repurpose human efforts for more critical tasks, increases accuracy, improves overall quality & performance of the process and improve SLAs by 40% 

Automated document meta-data tagging for uploaded documents in DMS using Pega Robotics for a major Insurance client by integrating OCR and document management system which helps in reducing human errors by 90% and man hours by 80% which results in improving turnaround time and quality of system.

In a multinational finance and insurance corporation, small and simple claims were processed by experience people, medium and complex claims were kept idle and these claims go into legal litigation, complex claims were assigned to freshers which had some errors and for which company bare legal litigation. RPA solution automates all claims which are less than certain $ amount by following the required steps, the customer enters very minimum information for medium claims, and Adjuster takes this info and goto policy handling system and passes the claim to the fraud detection system, complex claims are directly assigned to experienced Adjuster. The solution integrates with the Machine Learning algorithm to create ratings based on the complexity of the claims. This drastically reduces the company claim processing SLAs and cases go to legal litigation saving more than 50% of the cost. 

Automated report generation reduces human error arising out of manual, repetitive, and time-consuming AOD/EOD activities. Important time-sensitive information captured from diverse sources then converted to a standard format and transmitted on an hourly basis, 24 X 7. This helps the company remove the manual processes, wasted employees’ time, and disrupted their work.