At a large University in Southern Ontario, the central Experiential Learning team is responsible for the review and awarding of substantial student Bursaries designed to support work-integrated learning, and prioritize support for marginalized students.

The Problem:

This team was finding that the collection of applications, review, awarding and reporting efforts were incredibly time-consuming, inefficient, and susceptible to human error. Staff would spend upwards of a few months reviewing submissions, determining eligibility, finalizing bursary recipients and reporting on the dispersed bursaries.  Senior leadership was spending significant time and resources managing and engaging with this process, at the expense of spending less time and resources on strategic level goals.

The front-line team of coordinators faced significant challenges in manually processing and reviewing bursary applications, which required cross-checking student data, eligibility criteria, and financial need assessments as well as cross-checking all previous submissions to prevent duplication in bursary awards. With hundreds of applications to evaluate each semester, the process was not only labor-intensive, but it was preventing the team from doing more strategic work.

The Solution:

Loop Analytics, a leading provider of data-driven optimization and automation solutions, implemented a tailored solution to optimize and automate the bursary review process.

Using advanced algorithms and pre-set eligibility rules, Loop Analytics automated the sorting and analysis of each bursary application. The system was designed to automatically cross-check financial need, academic standing, and other criteria, as well as notify the appropriate staff member to finalize the decision on the application, significantly speeding up the review process – ensuring consistency in decision-making and prevention of any duplication.

Further, Loop Analytics built customized dashboards team leaders and their University leadership are now able to use to access real-time reports on the status of each bursary application, as well as the overall distribution of bursaries, support for marginalized students, and providing them with better insights into the volume of applications, trends in financial need, and resource allocation.

The Results:

After implementing Loop Analytics’ solution, the University experienced significant improvements in the bursary review process:

– Leadership Time Savings: The time spent by the senior leader organizing and coordinating all of the application data and parceling it out to the appropriate coordinator took several hours a week to complete. With the tools developed by Loop Analytics and reorganizing internal resources, this process has been completely automated and removed from the senior leaders’ weekly tasks. This has allowed senior leadership to focus their time on the strategic needs of the department and spent no time in this part of the process.

– Coordinator Time Savings: The time required to process each bursary application was reduced by well over 50%. Previously, each application could take 30 minutes to review and verify; now, the process was completed in a fraction of that time, allowing the coordinators to handle more applications in less time.

– Increased Efficiency: With manual tasks automated, the coordinators could focus on providing personalized support to students, helping them navigate their financial aid options, and addressing complex cases more effectively.

– Resource Optimization: The University was able to allocate resources more effectively and reduced the need for overtime or additional staff during peak application periods. This led to both time and cost savings, as the team was able to manage a larger volume of applications without adding extra personnel.

By leveraging Loop Analytics’ automation and data optimization solutions, the University transformed its bursary review process from a resource-intensive, manual task into a streamlined, efficient operation. The result was a significant reduction in processing time, improved accuracy, and better resource allocation—all of which contributed to a better experience for both the financial aid team and the students they served.