Authors: Oksana Anatolyevna Malysheva
Abstract: They've opened up the door to instant fund transfers and around-the-clock availability. But at the same time made us more exposed to scammers and money laundering schemes, when real-time payments became a norm. Coming racing up against the tight timeframes and limited space to go back and correct anything that's gone wrong, the old way of doing things just isn't working anymore. This paper takes a hard look at the analytical and infrastructure-related issues surrounding the detection of fraud and money laundering in real-time payment systems, where speed, accuracy and meeting regulations all need to be juggled at the same time. Well-known techniques won’t cut it anymore in the world of real-time, so the researchers here take a more applied approach, merging real-time analytics systems, cutting-edge fraud detection and money laundering models. They lay out a comprehensive blueprint for real-time transaction analysis, fine-tuning features for ultra-fast decision-making, hybrid rule-based and AI-driven systems and risk-scoring that’s tailored to the flow of instant payments. When evaluating the performance of fraud and money laundering systems in real-time, this paper looks beyond the traditional measure of accuracy and zeroes in on things like response times, scalability and false positives, which are pretty critical in the real-time world. The real contributions of this work are threefold: a crystal-clear picture of the threats facing real-time payments, a logical analytical framework that gets the balance between detection models, real-world timeframes and regulatory expectations, and some down-to-earth advice on how to run your fraud and money laundering systems in a way that is not only effective but also explainable and scalable.
