Revolutionizing Digital Transactions with Generative AI: Harnessing Neural Networks and Machine Learning for Enhanced Payment Security and Fraud Prevention

Abstract

Counterfeiting, skimming, and illegal data breaches are growing threats to the security of digital transactions and financial operations on both personal and professional levels. These attacks result in significant material and reputational damages. As a result, fraud detection and fraud prevention techniques based on novel profiles of digital transaction data are faced with the increasingly urgent need for accurate and precise identification of fraudulent transfers. Achieving high-quality security and avoiding theft as much as possible must be priorities when controlling any existing or novel monetary system.
Neural networks are a potentially promising tool for fraud prevention tasks in the financial post-trade domain for several reasons: they can take full advantage of the vast amount of numerical time-series data generated by financial service transactions that provide insights into distinct fraudster behavior. A key role in neural network architectures is played by the capacity of trainable models to self-learn progressively to efficiently forecast or simulate financial markets. These sorts of unsupervised learning systems can match numerical inputs with data collected in qualitative surveys or macroeconomic indicators, making quantitative predictions about crash periods, leader-follower exchanges, and trust chain growth in social systems using mobile digital banking. The focus of this work is to review the functional capabilities of a wide range of neural networks and generative modeling advancements. This creates the self-learned base required on top of which low-fraud financial systems can be secured. Key elements and innovations are reviewed to offer a solid proprietary AI foundation, which is suitable for advancing the security of vast digital transaction data.