AI-POWERED FRAUD DETECTION & PREVENTION IN FINANCIAL INSTITUTIONS
| dc.contributor.author | BOAS SINGOGO | |
| dc.date.accessioned | 2025-12-03T08:53:38Z | |
| dc.date.issued | 2025-06-27 | |
| dc.description | DISSERTATIONS | |
| dc.description.abstract | This research project investigates the development and implementation of AI-Powered Fraud Detection and Prevention in financial institutions. By using artificial intelligence and machine learning techniques like supervised learning, the proposed model aims to improve the accuracy, efficiency, and real-time capability of fraud detection. The study addresses key challenges faced by traditional methods such as high positive rates and failure to adapt to changing fraud tactics by developing a scalable and resource-efficient system. As emphasis is put on addressing data imbalance, ethical compliance, and user-friendly interfaces, this work contributes to improving risk management, reducing financial loses and increasing customer trust. The research findings show the potential of AI to revolutionize fraud detection, offering robust solutions aligned with regulatory standards and industry best practices | |
| dc.description.sponsorship | ZCAS UNIVERSITY | |
| dc.identifier.citation | HARVARD REFRENCING | |
| dc.identifier.uri | http://dspace.zcas.edu.zm/handle/123456789/169 | |
| dc.language.iso | en_US | |
| dc.subject | Fraud Detection | |
| dc.subject | Artificial-Intelligence | |
| dc.subject | Supervised learning | |
| dc.subject | Machine-Learning | |
| dc.subject | Deep-Learning | |
| dc.subject | Natural-Language- Processing | |
| dc.subject | Risk management. | |
| dc.title | AI-POWERED FRAUD DETECTION & PREVENTION IN FINANCIAL INSTITUTIONS | |
| dc.type | Thesis |
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