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Based on what they learned in the initial review, the research team conducted a multi-part data analysis of all submitted applications (without using any student personal information). In the first review, the focus was on one college that provided a large number of bad applications between June 1, 2016 - August 15, 2017. The second review looked at all other colleges who provided examples of bad applications in the same time period; and the third pull looked at all remaining colleges' submitted application data. It was important to compare the bad applications to good applications in order to start detecting trends and patterns in the fraudulent "formula". After reviewing all three data pulls, even without including personal identifiable information, we learned a great deal.
The majority of bad applications identified were submitted in under 3 minutes, with the majority of those being submitted in under 2.5 minutes. This That information alone told us that robots are likely involved, submitting applications quickly using keyboard strokes; .
Of the applications identified as frauds, other patterns were prevalent:
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Spam Pilot Project One of the recommendations from the research study was to organize a small pilot of colleges that can work with our support engineers and provide feedback throughout the research and development efforts. The pilot colleges will also collaborate on best practices and other workflow changes that can be shared back with the other colleges.enhancements to help prevent fraud applications from getting back to the colleges through their download system to prevent bad data from getting to the colleges and continuously re-training the prediction service model. |
Development Project
One of the recommendations from the research develop a spam filter web service that would prevent these the bad applications from getting back to the colleges through their download system to prevent bad data from getting to the colleges and continuously re-training the prediction service model.
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