INTELLIGENT CLASSIFICATION OF FINANCIAL TRANSACTIONS USING REAL-TIME MACHINE LEARNING TECHNIQUES

Abstract

Real-time financial fraud detection has become increasingly vital for financial institutions due to the 
surge in digital transactions and the growing complexity of fraudulent activities. Traditional fraud 
detection methods, which relied heavily on rule-based systems and manual oversight, often failed to 
adapt to emerging fraud tactics, leading to high false positive rates and delayed responses. Earlier 
approaches using statistical models and threshold-based techniques proved insufficient in identifying 
sophisticated, evolving fraud patterns. The integration of machine learning has transformed this 
landscape, enabling systems to learn from historical transaction data and accurately detect subtle signs 
of fraud. The push toward AI-driven solutions is driven by the demand for rapid, automated fraud 
detection that minimizes human error and financial losses. Traditional systems struggle with 
adaptability, precision, and scalability, which limits their effectiveness. In contrast, the proposed AI
based approach utilizes machine learning algorithms such as support vector machines and decision 
trees to analyze transaction data in real time. This enhances detection speed, improves accuracy, and 
delivers a scalable, robust solution to combat fraud in today’s dynamic digital ecosystem.