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

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 AIbased 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.