An American bank holding company automates and updates pricing information for 56 US regions, reducing processing time and eliminating human error.
SITUATION
Our client’s automotive finance team oversees maintaining and optimizing the prices and interest rates of automobile loans for 56 US regions and they are planning on expanding that number. With this expansion, the team is faced with a task requiring extensive manual processes. The team was originally tasked with manually updating pricing information across the 56 regions into the client’s national pricing origination system. This process used to take an employee two hours per region, meaning that for a full-price optimization, it would take 112 hours or 14 days, with a large window for human error that could directly impact the consumer.
Our Solution Improved processing time by 78% with 0% error rate.SOLUTION
Our team implemented a dynamic approach to the development of the solution getting involved in each phase of the development cycle (requirements, design, development, testing, and implementation). To accomplish this vision, the team designed a BluePrism solution composed of three artifacts: a Zena job, a queue bot, and a worker bot. These artifacts work harmoniously in sync to create a smooth robotic process. The Zena artifact checks for region files inside of a secure location every hour, and if there are files to process, it sends a signal to the queue bot to start working. The queue bot extracts the data of each region file and validates that everything is in order, then adds the files to a queue so the worker bot can process them. The worker bot goes into the client’s pricing origination system, compares the data on the file against the system, performs the updates inside the system, confirms that the update is there, and notifies the line of business that the process is complete. The team also designed a checkpoint/recoverability system that allows the bot to recover from any system error and restart the execution from the section of the process where the error occurred.
RESULT
Apex’s nearshore team in Mexico took the requirement and transformed it into a simple but complete vision for the robotic process. The process they designed was able to reduce the file processing time by 78%, with the entire scope of 56 regions being processed in 4.6 hours. The process also eliminates human error in copying data. Previously the business avoided pricing updates unless it was considered urgent; now, they do it once a month.