Case Studies

Auto Insurance: Good Driver Discount Adjustment Process

A major American Auto Insurance company offers a discount for their policy holders who complete a “Cautious Driver” course. After course completion, the Insurance company receives files from three different course vendors and uses two different websites to record the results, populate driver information, look up the insurance policies, and apply the appropriate discounts. This process involves several different files and screens resulting in the full-time efforts of up to 10 employees to process over 800 new good driver discounts daily. Using RPA, this process was able to be fully automated and now involves no manual labor and provides the company a major reduction processing time overall.

Solution Steps:

  1. Bot Reads Email: Three different Cautious Driver Course vendors send an email with the list of policy holders who have completed the course successfully and have earned an auto insurance discount.
  2. Bot Processes List: The bot converts the emailed course completion lists from the vendors into a standard file type, processes the lists, and loads the policy holder names and identifiers into a queue.
  3. Bot Reads Queue: To account for high volumes, multiple bots log in to the system and pick up the queue items that were uploaded in the previous step. Each bot grabs the next available policy holder on the list and locks it to ensure each holder is only processed once.
  4. Bot Searches for Policy Number: Bot scans the file for the driver’s Policy number, Driver’s License number, issuing State, and State’s policy mapping information. After cross-referencing all of the gathered data the Bot locates the appropriate auto insurance policy number. Bot ensures the driver names and policies exactly match and checks for name similarity errors such as John Smith and John Smith Jr.
  1. Bot Manages Policy System: During this step the bot splits into two workflows, one for if a policy is listed in the internal “Insight” system and another if the policy is listed in the internal “Edge” system. This step requires two distinct workflows as “Edge” is a web-based system and “Insight” is an older website system that flows out of sequence, meaning the bot had to be built to follow alerts in order to navigate the site. Using the driver’s policy number, the Bot locates the matching policy and records the policy’s appropriate discount amount.
  2. Bot Updates System: The Bot enters the Cautious Driver course completion date in the Driver’s file and updates all of the policy and driver information internally to ensure records are up to date.
  3. Repeat Steps: The Bot continues to repeat steps 3-6 until the queue is empty and all of the Cautious Driver discounts for the day have been applied and accounted for
  4. Bot Generates Report: Once the queue is empty the bots sync and wait for the last running bot to finish. When complete, the last bot in the queue generates the report from the automated process, captures all queue items from the files, and time stamps the file so only the current queue items are reported on.

Using RPA to automate this process, the client was able to reduce the manual labor required for this task by 100% and has even seen the Cautious Driver discounts recorded and applied faster as the bot takes 33% less time to complete the job. Automations such as this one are one of the many examples of manual business tasks that, when analyzed and structured appropriately, can be automated to no longer involve human effort. With proper process discovery and process mining in place, major enterprises can more efficiently detect these automatable tasks – leading to a significant decrease in labor costs while improving employee well-being and digitally transforming their company.