Abstract
Digital transactions have grown exponentially with the rise of online banking, e-commerce and financial technology. However, this growth has also led to an increase in fraudulent activities such as credit card fraud, phishing, account takeovers and cryptocurrency scams. Traditional rule-based fraud detection methods struggle to keep up with evolving fraud tactics, making advanced artificial intelligence (AI) and machine learning-based models essential for fraud prevention. This survey explores various types of digital transactions, common fraud detection techniques using data mining and recent research contributions in the field. The study on supervised, unsupervised and hybrid machine learning approaches discussing their advantages and limitations. Additionally, we highlight the key challenges in implementing effective fraud detection systems and examine future trends, including real-time analytics, blockchain integration, behavioural biometrics and explainable AI, to enhance the security of digital transactions. This will be helpful to the researchers for their future research direction in this area.
Keywords
Digital transactions
fraud detection
credit card fraud
cryptocurrency scams
machine learning
data mining
supervised learning and unsupervised learning
Authors
How to Cite this Article
L. Haripriya, S. Kavipriya, N. Geetha (2025).
"A STUDY ON DIGITAL TRANSACTIONS FRAUD DETECTION SYSTEM".
International Journal of Contemporary Research in Computer Science and Technology,
8(1), pp. 5-2.
