Insurance Industry is often overwhelmed with high volumes of repetitive business. Ridden heavily with manual processes and regulatory and compliance requirements, this vertical probably benefits the most from RPA. While some of insurance companies already make use of partial automation — in order to scan paper documents or to speed up data entry, for example — greater automation capabilities are offered by robotic process automation (RPA).Underwriting RPA

With the help of machine learning algorithms, deep learning models, clustering and segmentation, complex event processing, API’s, the following underwriting tasks can be achieved:

  • Update case data and determine customer risk profile after each action taken by the underwriting system e.g. case referral, additional document request etc.
  • Determine the most appropriate next best action based on the case data updated by the previous action and reassess customer risk profile
  • Determine the indicative time required by the system to take the final decision based on the historical analysis.

Some of the common challenges faced by underwriters that can be solved by RPA include:

  • Manually ingesting data from applications submitted in different formats: Formats include ACORD PDF and proprietary XLS. This sometimes includes data entry into the underwriting system (UWS) in addition to data verification. This is where RPA flies high with no requirement to change existing system and attaining near error free results.
  • Having multiple channels for receiving applications: There are agencies that prefer to submit applications in hardcopy paper form while others use faxes or emails as their preferred channel of submission. Often, all the submitted applications are not brought into a common queue resulting in improper prioritization of application processing i.e. higher value applications are processed after lower value ones. Again RPA comes to the rescue. With cognitive BOTS, step 1 in the process will be structuring the data using OCR and step2 will involve machine learning.
  • Inability to pre-score applications: This is essential to figure out which applications are ready for underwriting in terms of complete documentation, or which are low risk, high value applications to be taken up before others. This is important, because sometimes, an underwriter may spend hours before figuring out that the application is not complete, while there might be a complete high-value application, waiting for their attention. RPA can enable pre-scoring quickly and efficiently as the BOTS are really much faster than humans at data collation. Once collation is done, a simple rule based sort will help identify high value applications.
  • Requirements management: This happens when underwriters end up finding missing or incomplete requirements during the entire underwriting process. This results in a situation where they have put in significant amount of effort, but the application cannot proceed due to missing or incomplete information. Further, the agent/ broker may take their own time to provide the missing or incomplete information. BOTS can streamline this process as discussed above and post /track additional requirement requests as a part of an elaborate workflow. This
  • Business as usual (BAU) activities: Last but not the least, there are too many BAU activities at hand, for underwriters to find time to improve customer experience by improving the underwriting processes or systems. In case of RPA these requirements are either completely done away with or done much faster.