AI-POWERED PAYMENT FRAUD SIGNATURE GENERATION AND CONTINUOUS RETRAINING METHODS

Abstract

The trend of payment fraud is not only persistent but also very fast, as criminals are adopting more and more advanced techniques, which are already beyond the detection capabilities of the traditional rule-based systems. The present research introduces a detailed framework for the generation of AI-driven fraud signatures that are constantly retrained and updated according to the real-time detection and occurrence of new fraud patterns. The role of breakthrough machine learning models is shown, which would not only identify fraud signatures within the transaction data but also automatically create detection rules and keep themselves updated continuously without any human intervention. By probing into fraud detection issues in the different digital payment ecosystems, we reveal that the use of automated signature generation gives 67% faster detection time than manual rule creation and also detection rates are increased by 34%. The inclusion of a flexible retraining strategy prevents model performance from becoming ineffective over a longer period of time, which would typically happen within 3-6 months of the initial deployment. In this paper, we offer the world practical framework for the implementation of adaptive fraud detection systems that provide a good mix of accuracy, speedy response, and operational efficiency. The results are of great significance to banks, payment processors, and online retailers who deal with millions or even billions of transactions every day. This research shows that the merging of automated pattern recognition with regular model updates yields the formation of strong fraud defense systems that are able to change as the threats change.

DOI: 10.46121/pspc.52.4.7