Skan AI performed a deep analysis to gain insights into key operational metrics at the claims processing center and identify opportunities for improvement. One area of focus was turnaround times (TATs) for claims processing, which were found to be higher than desired. By analyzing the root causes, Skan AI identified that complex claims often bounced back and forth between different teams trying to determine the appropriate resolution, leading to delays.
Skan AI also looked at the number of reworked claims and the average handling time. Here we found that claims had to be reworked due to errors, adding unnecessary time to the process.
Additionally, Skan AI identified several opportunities for automation in the claims handling process itself. By implementing robotic process automation for repetitive tasks, capacity and productivity could be improved. The center was also only running at 51.8% capacity currently, so there was room to handle more claims volume without adding staff.
Finally, Skan AI found that adjusters were relying heavily on Google Docs, which was adding 5% to the overall claims cycle time. By reducing this dependency and utilizing more streamlined claims management tools, overall process efficiency could be improved by 4%.