Implementation of a Big Data-Based Fraud Detection System in University Admissions in Sokoto North Local Government, Sokoto State
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- NGN 5000
Background of the Study
University admissions fraud undermines the integrity of academic institutions and can lead to significant resource misallocation. In Sokoto North Local Government, Sokoto State, fraudulent practices in admissions have raised concerns regarding fairness and transparency. The implementation of a big data-based fraud detection system offers a technological solution to address these challenges. By integrating data from multiple sources such as application forms, academic records, and biometric systems, big data analytics can identify patterns and anomalies that indicate potential fraud (Abdullahi, 2023). Advanced algorithms, including anomaly detection and clustering, can process large volumes of data in real time, flagging suspicious activities for further investigation. This approach not only enhances the accuracy of fraud detection but also speeds up the verification process, thereby reducing the administrative burden and ensuring that admissions decisions are based on authentic and verified data. Moreover, a big data framework allows for continuous monitoring and improvement of the fraud detection system, as machine learning models update their predictions based on new information (Chinwe, 2024). The application of such a system is critical in maintaining public trust and ensuring that admission processes are equitable and merit-based. However, challenges such as data privacy, integration of heterogeneous data sources, and the need for robust computational infrastructure must be addressed to fully realize the benefits of this technology. This study aims to develop and implement a comprehensive big data-based fraud detection system tailored to the university admissions process, evaluating its effectiveness in detecting irregularities and preventing fraudulent admissions, while proposing solutions to overcome potential implementation challenges (Oluwaseun, 2025).
Statement of the Problem
The current university admissions process in Sokoto North Local Government is vulnerable to fraudulent activities due to its reliance on traditional, manual verification methods. These methods are often slow, error-prone, and unable to effectively identify sophisticated fraudulent practices, resulting in the admission of unqualified candidates and undermining the institution’s credibility (Ibrahim, 2023). The lack of an integrated, data-driven fraud detection system means that suspicious patterns and anomalies go undetected until after the admissions process is completed. This delay not only compromises the fairness of admissions but also creates administrative inefficiencies and increases the risk of legal challenges. Furthermore, the fragmented nature of the existing data sources makes it difficult to conduct comprehensive analyses necessary for fraud detection. While big data analytics has the potential to revolutionize this process, its application in the admissions context is limited by challenges such as data integration, privacy concerns, and the technical expertise required to develop robust detection algorithms. These challenges hinder timely intervention and allow fraudulent activities to persist, ultimately affecting the quality of the student body and institutional reputation. This study seeks to address these issues by developing a big data-based fraud detection system that leverages advanced analytics to identify and prevent admissions fraud in real time. The research will evaluate the system’s performance in terms of accuracy, speed, and scalability, and propose strategies to overcome the technical and operational barriers to its implementation.
Objectives of the Study:
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To develop a big data-based system for detecting fraudulent activities in university admissions.
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To evaluate the system’s accuracy and efficiency in identifying fraudulent records.
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To propose recommendations for integrating the system into the current admissions process.
Research Questions:
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How effective is the big data-based system in detecting admissions fraud?
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What are the key indicators of fraudulent activity in the admissions data?
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What challenges must be overcome to implement the system successfully, and how can they be addressed?
Significance of the Study
This study is significant as it presents an innovative approach to safeguarding the integrity of university admissions through a big data-based fraud detection system. The research will provide critical insights into the application of advanced analytics for enhancing transparency and fairness in admissions, thereby protecting institutional reputation and ensuring that deserving candidates are selected. The findings will serve as a model for other institutions facing similar challenges (Chinwe, 2024).
Scope and Limitations of the Study:
The study is limited to the implementation of a big data-based fraud detection system for university admissions in Sokoto North Local Government, Sokoto State, and does not extend to other administrative functions or regions.
Definitions of Terms:
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Fraud Detection System: A technological framework designed to identify and prevent fraudulent activities.
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Big Data Analytics: Techniques used to process and analyze large datasets to uncover patterns and anomalies.
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University Admissions: The process of selecting and enrolling students into academic programs.
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