Insurance quote automation

Case study

Insurance quote automation

With the commoditization of insurance, the competition among insurance brokerage firms compels them to fast track their business processes. Traditional Insurance broking companies take a few days to generate quotes, as it involves the gathering of data, entering it into disparate systems, validating, and then generating quotes.
Manually performing these tasks is highly time consuming and can lead to inconsistencies and errors in the data. To improve market share and grow, it becomes essential to ensure fast and accurate quote delivery.


Our client is a $6 billion, international insurance intermediary firm that specializes in insurance/reinsurance broking, MGA services, and insurance underwriting. They wanted to optimize their insurance quote creation process for multiple divisions to deliver the best possible insurance quotes to their clients.


  • Manual data entry resulted in inconsistent data capture.
  • Manual extraction of data from emails and loading of extracted data into the client’s Agency Management System (AMS) was highly prone to human error.


Earlier, quote creation involved manually extracting relevant information from emails and attachments and entering into the AMS. The quote was then generated in AMS, which had to be communicated to their customers.

Imaginea offered to implement an RPA solution, with OCR (Optical Character Recognition) capabilities to automate the process. Our clients have specific email IDs for various divisions, in which they receive request for insurance quotes. RPA bots were given access to mailboxes to process and extract the data using UiPath’s OCR engine, and populate relevant fields in the AMS.

The bots use keywords to identify relevant emails from the subject line and read the quote-related information from the email body and attachments, and extract/load the data into the AMS. The diagram below illustrates how data is extracted and processed through our solution:

Tech stack

How our solution helped

80% reduction in Turnaround Time, 90% automation of the quote creation process. 50% reduction in FTE.

Overall approach

We decided to use reusable worklets for automation. These worklets are tiny independent testable workflows that can be easily integrated into any process. The worklets were used to replace manual intervention in the identified processor lines of the quote generation process. We defined the ‘why, what and how’ of the processes that can be automated as follows:

  • The first step was to identify the process and determine the RPA goal. Here, the goal was to reduce the turnaround time for the quote generation process.
  • Identify the steps in the process that can be automated. There were two steps where the data was being entered manually from a source to the destination. Hence those steps were identified to be automated.


  • Reduced quote generation time from days to minutes.
  • Automation has reduced human error substantially to < 2%.

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