FAKE SCHOLARSHIPS DETECTION SYSTEM

ABSTRACT
In the digital age, the widespread availability of online scholarship platforms has significantly improved access
to educational opportunities for students across the globe. However, this rapid growth has also led to a parallel
increase in fraudulent scholarship schemes that exploit students by presenting fake or misleading information.
These scams often target financially vulnerable students by promising guaranteed funding, quick approvals, or
high-value rewards, while secretly aiming to collect money or sensitive personal data. As a result, students face
financial loss, identity theft risks, and psychological distress, which ultimately undermines trust in legitimate
scholarship programs. To address this critical issue, the proposed Fake Scholarship Detection System introduces
an intelligent and automated solution that leverages Machine Learning (ML) and Natural Language Processing
(NLP) techniques to identify and classify scholarship opportunities as genuine or fraudulent. The system analyzes
various features such as textual content, keyword patterns, grammatical structure, website authenticity, and
metadata including domain age and security protocols. By integrating multiple classification algorithms such as
Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM), the system achieves
high accuracy and reliability in fraud detection. Furthermore, it generates a fraud probability score along with
explanatory outputs to enhance transparency and user trust. The system is implemented through a user-friendly web
interface, enabling real-time analysis and easy accessibility for students. Experimental evaluation demonstrates
that the proposed model effectively reduces false positives and false negatives while maintaining strong
performance across different datasets. This project not only provides a practical tool for preventing scholarship
fraud but also contributes to raising awareness about cybersecurity risks in the education sector, thereby promoting
a safer and more reliable digital environment for students seeking financial assistance.

KEYWORDS
Fake Scholarship Detection, Machine Learning, Natural Language Processing (NLP), Fraud Detection, Random
Forest, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), Text Analysis, Cybersecurity, Data
Mining, Web Security, Student Safety, Artificial Intelligence