Richard Hickling

The solution used automated data categorization to identify the document type, then automated data capture to pull the details. The extracted data was passed to the client's system to update. The project resulted in the removal of any manual intervention and delivered benefits equating to over 12 FTE in savings.

In order to remove the mailvan network that connected over 1,300 branches and eight service centers, we had to implement an electronic document and records management system covering over 100 different processes. This included an exception workflow for when the submitter didn't know the document type. This workflow would use automated document categorization to identify the document type and submit to the correct workflow. It also used machine learning to learn about documents that it didn't recognize and manual intervention was required.

When a PPI complaint is received from the customer, a handler will review it against a defined set of rules in order to see if a payout criterion is automatically triggered. If not, the handler will then be required to log into a number of current and legacy systems to pull the case together. The solution used robotic process automation (RPA) to perform all this functionality and the results presented the completed case to the handler for review.

Each year the client posted a form to 750,000 customers confirming any international tax status. The returns were manually reviewed for the circa 1% who did have international tax. For that 1%, the international tax details were manually updated on the system. In this solution, where the customers provided an email address they were sent a link to a smart form to capture the data, which included customer verification. For those who did not complete after receiving the email or did not have an email contact, the paper form was sent but the returns were processed using automated data capture. This removed the requirement for handlers to manually update the customer records.