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VOL. 10, ISSUE 3 (2025)
AI-driven fraud detection in school and college admission processes
Authors
Mahendra Vishwanath Thakare, Monika Ganpat Walave
Abstract
AI-driven fraud detection in school and
college admission processes has become increasingly essential to maintain
fairness, transparency, and integrity in academic institutions. Traditional
admission systems are vulnerable to fraudulent activities such as identity
theft, document forgery, and misrepresentation of credentials, which can
compromise the selection of deserving candidates. Leveraging artificial
intelligence, particularly machine learning and deep learning techniques, enables
the identification of anomalies, detection of forged documents, and
verification of applicant authenticity in real time. AI models trained on vast
datasets can analyze patterns, detect irregularities, and flag suspicious
applications by comparing them with historical admission records and authentic
data sources. Advanced natural language processing (NLP) techniques further
enhance fraud detection by verifying the legitimacy of personal statements and
recommendation letters. Additionally, AI-powered facial recognition and
biometric authentication help validate applicant identities, reducing the risk
of impersonation. Implementing AI-driven fraud detection systems not only
streamlines the admission process but also enhances security, reduces human biases,
and improves decisionmaking efficiency. As educational institutions
increasingly embrace digital transformation, integrating AI-based solutions
into admission processes ensures credibility and fairness, ultimately fostering
a more equitable academic environment.
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Pages:112-115
How to cite this article:
Mahendra Vishwanath Thakare, Monika Ganpat Walave "AI-driven fraud detection in school and college admission processes". International Journal of Advanced Scientific Research, Vol 10, Issue 3, 2025, Pages 112-115
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