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Abstract
The proliferation of digital transactions and the increasing sophistication of financial fraud necessitated robust detection mechanisms by 2020. Real-time data management (RTDM) emerged as a critical technological approach, offering dynamic, on-the-fly analysis of financial transactions to thwart fraudulent behavior. This study investigates how RTDM influenced fraud detection efficiency in financial institutions during the year 2020. The research employs a comparative methodology that examines institutional case studies, data management technologies, and fraud detection metrics. It contrasts real-time techniques against traditional batch processing methods to evaluate performance in terms of accuracy, latency, and operational scalability. Findings reveal that RTDM significantly improved fraud detection accuracy and response time, reducing financial losses while enhancing customer trust. The integration of AI and streaming analytics played a vital role in achieving these outcomes. However, implementation challenges such as cost, technical expertise, and data integration complexities limited widespread adoption. This paper contributes to the understanding of RTDM’s value in the financial domain and outlines future directions for enhancing its adoption and efficacy.
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