Retail Fraud Analytics Using Generative Intelligence and Java Cloud Frameworks

Abstract

Retail fraud continues escalating in sophistication and financial impact, with global losses exceeding $100 billion annually across e-commerce and brick-and-mortar channels. This research develops and validates a comprehensive fraud detection system leveraging generative intelligence techniques implemented through Java cloud frameworks. Traditional rule-based and machine learning approaches struggle with emerging fraud patterns, synthetic identity creation, and organized retail crime networks operating across multiple channels. Our framework integrates generative adversarial networks for anomaly detection, transformer-based models for transaction sequence analysis, and graph neural networks for relationship mapping, all deployed on Spring Cloud and Apache Kafka infrastructure. Through empirical validation using transaction data from three retail organizations encompassing 12 million transactions, we demonstrate 42% improvement in fraud detection rates while reducing false positives by 38% compared to conventional systems. The system identifies previously undetected fraud patterns including coordinated account takeovers, return fraud schemes, and payment manipulation tactics. Real-time processing capabilities enable intervention before fraudulent transactions complete, preventing losses rather than simply detecting them post-facto. This work contributes scalable Java-based architecture patterns for deploying generative AI in production retail environments while addressing explainability requirements for fraud investigation teams.