Enroly provides the first CAS, visa and arrival automation platform. It makes post-acceptance administration simple and secure, allowing universities to easily implement the solutions into their existing systems. Not only does it help improve enrolment systems, but it also automatically detects risk factors to help higher education institutions to prevent mistakes that would otherwise become deferrals, withdrawals and refusals. Enroly's range of products are used by universities, students, agents, and regional offices in more than 100 countries around the world.
Partnership with STX Next:
February 2020 - April 2020 (2 months)
Improving Enroly CAS SHIELD with Machine Learning
Enroly CAS SHIELD, one of the company's three core services, helps university staff manage applications from prospective students. By offering functionalities such as smart prioritization of large applicant volumes, student interview booking system, and automated reading and assessing of documents, the company helps improve enrolment numbers. The platform also automatically detects risk factors and assigns enrolment confidence scores to applications. STX Next collaborated with Enroly to increase efficiency and effectiveness with machine learning solutions, including by automating document processing and information extraction.
STX NEXT DELIVERED
Since Enroly's platform hosts large volumes of documents, the company was looking for ways to make their processing more efficient, free up their clients' employee time, as well as speed up the verification of documents and the application for the CAS statement.
The first challenge, therefore, was to identify the specific areas that would benefit from the introduction of machine learning, and create a workable project scope.
Once the scope was established, Enroly needed the right tools to help it process and extract the relevant information from the huge range of documents it receives from all over the world (for instance birth certificates, bank statements, and handwritten notes) in varying quality and formats (for instance scans and photos).
STX Next held consultations with Enroly to find areas that could be automated with ML solutions as well as explore potential features and prioritize them based on their business value.
Following the consultations, we created a project scope which served as a roadmap for future development of AI-based features.
The scope consisted of the application of Optical Character Recognition techniques to automatically process and verify documents submitted to the platform by students or agencies acting on their behalf. The documents were analyzed and relevant data was extracted from them. Natural Language Processing techniques were used to implement the fuzzy matching method, which helped ensure the required information has been provided.
Our team was tasked with developing features for automated extraction and processing of data from scans of various documents. The development process included implementing, testing, and comparing many ML methods to deliver the best possible end solution. I will remember our cooperation with the client as an enjoyable experience. We could always rely on great communication and fast decision making.