Implementation of Quantum Machine Learning for Fraud Detection in Online Transactions at Ibrahim Badamasi Babangida University, Niger State
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Background of the Study
The digitalization of university services has increased the reliance on online transactions for various academic and administrative functions, including fee payments, tuition processing, and other financial activities. However, this increase in online activities has also led to a rise in fraudulent transactions, with universities being targeted by cybercriminals seeking to exploit weaknesses in financial systems (Raghu et al., 2024). Traditional machine learning algorithms have been widely adopted for fraud detection in financial transactions, but they face limitations in processing speed, accuracy, and scalability. Quantum machine learning (QML), which integrates quantum computing into machine learning models, offers a promising alternative, capable of analyzing vast datasets more efficiently and improving the detection of anomalous patterns that signify fraud.
At Ibrahim Badamasi Babangida University, Niger State, the implementation of quantum machine learning techniques could potentially enhance the university’s ability to detect fraudulent activities in online financial transactions. This research will explore how quantum machine learning can be utilized to identify fraudulent activities in real time, offering an advanced solution to a pressing issue in the academic sector.
Statement of the Problem
With an increase in the volume of online transactions, universities such as Ibrahim Badamasi Babangida University have become prime targets for cyber fraud. Existing fraud detection systems, typically based on classical machine learning models, often struggle with real-time processing of large transaction datasets and may produce false positives or miss fraudulent activities altogether. There is a need for more effective fraud detection systems that can handle larger volumes of transaction data with improved accuracy and efficiency. Quantum machine learning has the potential to address these limitations, but its feasibility and effectiveness in the university’s online financial transaction environment remain untested.
Objectives of the Study
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To design and implement a quantum machine learning model for fraud detection in online transactions at Ibrahim Badamasi Babangida University.
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To evaluate the performance of the quantum machine learning model in comparison to traditional fraud detection methods.
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To assess the feasibility and challenges of integrating quantum machine learning into the university’s financial systems.
Research Questions
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How can quantum machine learning techniques be implemented to detect fraud in online transactions at Ibrahim Badamasi Babangida University?
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What are the benefits of using quantum machine learning for fraud detection compared to traditional methods?
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What challenges are involved in integrating quantum machine learning into the university's existing financial systems?
Significance of the Study
This study will contribute to improving the security of online financial transactions at Ibrahim Badamasi Babangida University by providing a more effective fraud detection mechanism. By adopting quantum machine learning, the university can potentially reduce the risk of fraudulent activities and ensure the integrity of its financial operations. The research will also offer valuable insights into the practical application of quantum technologies in the field of financial security within academic institutions.
Scope and Limitations of the Study
The study will focus on the implementation of quantum machine learning for fraud detection in online transactions specifically at Ibrahim Badamasi Babangida University, Niger State. The scope of the study will be limited to evaluating the model’s performance in the context of financial transactions and will not explore its use in other areas of the university's operations.
Definitions of Terms
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Quantum Machine Learning (QML): The integration of quantum computing techniques with classical machine learning models to enhance data processing capabilities, particularly for large datasets.
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Fraud Detection: The process of identifying and preventing fraudulent activities in financial transactions through the analysis of transaction patterns.
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Online Transactions: Electronic exchanges of money or data between individuals or institutions over the internet, typically involving payment systems, tuition fees, or administrative transactions.
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