The increasing volume of digital transactions and the growing sophistication of fraudsters have made real-time financial fraud detection a critical concern for financial institutions. Traditional systems, which relied heavily on rule-based approaches and manual oversight, often failed to adapt to emerging fraud patterns. These limitations resulted in high false-positive rates and delayed responses. Earlier detection methods, such as statistical models and threshold-based systems, proved inadequate in identifying complex and evolving fraudulent behaviors. With the advent of machine learning, however, fraud detection has advanced significantly. AI-powered systems can now learn from historical transaction data and uncover subtle fraud indicators with improved accuracy. The primary motivation behind developing AI-based solutions is the urgent need for automated, real-time fraud detection systems capable of rapidly identifying threats and mitigating risks. These systems help reduce human error and financial losses while addressing the limitations of traditional methods— particularly their poor adaptability, high false-positive rates, and scalability challenges. The proposed AI-driven approach utilizes machine learning models such as Support Vector Machines (SVM) and Decision Trees to analyze real-time transaction data. This enables quicker and more accurate fraud detection. By processing data instantly, the system not only enhances detection speed and reduces financial loss but also provides a scalable and efficient solution for combating fraud in today’s fastpaced digital landscape
Prashanthi et al. (Thu,) studied this question.