International Journal of Sustainable Infrastructure for Cities and Societies

Enhancing Payment Security Through AI-Driven Anomaly Detection and Predictive Analytics

Shobhit Agrawal

Sr. Software Engineer - Meta (Facebook)

https://orcid.org/0009-0000-4957-5575

Keywords: Anomaly Detection, Payment Security, Predictive Analytics, AI-based Systems, Algorithm Selection, Fraud Detection

Abstract

Developing robust and adaptive AI-based systems for anomaly detection and predictive analytics is a significant challenge. They require careful architectural design and the selection of appropriate algorithms. This research presents a though examination of system architectures and algorithmic approaches for implementing AI-driven anomaly detection and predictive analytics. The study focuses on two primary methodologies: a density and distance-based architecture and a model-based architecture. The density and distance-based architecture utilizes algorithms such as Isolation Forest, Local Outlier Factor (LOF), and DBSCAN to identify outliers based on the proximity and density of data points. In contrast, the model-based architecture employs predictive models, including techniques like Autoencoders, Support Vector Machines, Random Cut Forest, and Gaussian Mixture Models, to detect deviations from expected normal behavior. The research provides an in-depth analysis of the key features, advantages, and limitations of the various algorithms within each category. It explores how these approaches handle factors like data dimensionality, computational efficiency, and robustness to outliers and noise. Additionally, the research discusses the use of predictive analytics techniques, such as statistical models, instance-based learning, and ensemble methods, for applications like fraud detection and forecasting. The work highlights the trade-offs that must be considered when selecting the appropriate anomaly detection and predictive modeling approaches. Factors such as interpretability, handling of complex patterns, and susceptibility to overfitting are examined, offering insights for researchers working to develop AI-driven payment security systems for various infrastructure and societal applications.