AI-Powered Fraud Detection Systems in Digital Payment Platforms: A Comparative Study

With the fast increase in digital payment platforms, the aspect of financial transactions has changed by providing financial transactions speed, convenience, and accessibility worldwide. Nonetheless, there has been an enormous growth in fraudulent cases due to this growth and disclosure is threatening to consumer confidence and financial stability. The rule-based systems available in the market that detect fraud using known patterns are effective but are not efficient in detecting new and advanced patterns of frauds. In that regard, AI-based system of fraud detection has proved to be a solid solution, as it is able to both detect anomalies and forecast fraudulent activity with the help of machine learning and deep learning principles and apply them real-time. This paper has also critically compared the conventional rule-based frameworks alongside AI-based fraud detection systems in the digital payment frameworks. It also compares various models with their most vital performance measures that consists of accuracy, precision, recall, scalability and adaptability. The findings show that the AI-driven systems will significantly transcend the traditional systems in detecting complex and hitherto unheard-of fraudulent times, in addition to increasing the capacity to counter fraud in real-time. However, such aspects as data privacy, model interpretation, or high costs of intensive calculations is also one of the most critical factors. The study may be perceived as a supplement to the current literature on the subject of evaluating the system to detect a fraud since the paper is well organized and includes feasible suggestions to financial institute and digital payment companies that are interested in enhancing their security framework with the inclusion of AI.