The challenge
A leading financial services company aimed to streamline the process of preparing lawyer bidding instructions for the sale of foreclosure properties. Their goal was to enhance accuracy, standardisation, and risk reduction within their Foreclosure team. To achieve this, they partnered with the Avec team to implement Robotic Process Automation (RPA) to automate manual tasks and ensure consistent enforcement of business rules. The client also sought to improve accuracy to reduce costly human errors that occurred during the process and decrease the occurrence of ‘rushed’ cases by preparing relevant documents much earlier.
The solution
To address the client’s needs, the Avec team mapped the entire process with input from subject-matter experts and defined applicable cases and business rules supporting each process. They optimised automation potential by re-engineering aspects of the process, including the development of a form to facilitate assisted automation. Key processes were automated at a keystroke level, and the process automation was validated with process owners. This was followed by configuration testing, verification testing, and user acceptance testing with subject-matter experts. Upon approval, the solution was transitioned to production (deployed/BAU).
The result
The implemented solution resulted in:
- Significant efficiency improvements and cost savings for the client.
- The Avec team created a repository of reusable objects for future automation projects and realised benefits such as a reduction of 1.5 Full-Time Equivalent (FTE) positions and the elimination of costly errors during the process (previously ~$500k per annum).
- There was no change or operational impact on existing systems.
- The automation covered 14 different target applications and resulted in the development of a robust, reusable, and scalable Blue Prism RPA solution.
- The client also experienced FTE reduction, increased revenue due to less time spent correcting cases, and achieved a 0% error rate, completely removing human errors.