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Paper Type
Complete
Description
Financial awards, such as scholarships and bursaries, are offered by universities to financially support students, based on factors such as academic performance, financial need, and leadership potential. The financial awards process is managed by a financial awards (FA) team, which involves two processes: Awards Selection and Awards Disbursement. The Awards Selection process involves assessing various aspects of the application, such as student demographics, family background, academic and co-curricular achievements, and personal essays, and shortlisting suitable candidates for each award. The process becomes complex due to the combination of several applicants and several donors, each with different requirements. To address these challenges, this paper proposes an automated financial aid process design, which integrates rule-based techniques, text classification algorithms, and point aggregation models to generate the ranked list of candidates for each award. The model is tested based on historical data and achieves an accuracy of 84% for text classification and a precision@5 of 84.6% for the overall solution model.
Paper Number
1487
Recommended Citation
Gottipati, Swapna; Shankararaman, Venky; Rao, Komirisetti Chalapathi; and Vetrivillalan, Vennila, "Machine Learning based Financial Aid Management Process" (2023). AMCIS 2023 Proceedings. 14.
https://aisel.aisnet.org/amcis2023/sig_odis/sig_odis/14
Machine Learning based Financial Aid Management Process
Financial awards, such as scholarships and bursaries, are offered by universities to financially support students, based on factors such as academic performance, financial need, and leadership potential. The financial awards process is managed by a financial awards (FA) team, which involves two processes: Awards Selection and Awards Disbursement. The Awards Selection process involves assessing various aspects of the application, such as student demographics, family background, academic and co-curricular achievements, and personal essays, and shortlisting suitable candidates for each award. The process becomes complex due to the combination of several applicants and several donors, each with different requirements. To address these challenges, this paper proposes an automated financial aid process design, which integrates rule-based techniques, text classification algorithms, and point aggregation models to generate the ranked list of candidates for each award. The model is tested based on historical data and achieves an accuracy of 84% for text classification and a precision@5 of 84.6% for the overall solution model.
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