Cross-Cloud Generative AI Framework for AML, KYC, and Claims Fraud Detection

Abstract

Financial​‍​‌‍​‍‌​‍​‌‍​‍‌ institutions and insurance companies are struggling more and more to spot money laundering cases, identity fraud scams, and fake insurance claims. The existing rule-based systems that detect only know how to react because they have significant shortcomings when adaptive criminal methodologies confront them. Static threshold parameters fail to capture evolving transactional patterns. Conventional machine learning implementations operate within isolated data environments. Semantic reasoning capabilities remain absent from current detection frameworks. A Cross-Cloud Generative AI Framework addresses architectural gaps through integration of large language models, retrieval-augmented generation mechanisms, and multi-agent reasoning systems. The framework establishes unified data ingestion pipelines across distributed cloud storage environments. Domain-tuned language models process heterogeneous data types, including transaction records, identity documents, and claims artifacts. Retrieval-augmented generation grounds analytical outputs in verifiable documentation, reducing hallucination risks. Specialized agents rapidly communicate in different domains of anti-money laundering, identity verification, and claims fraud. Cross-cloud orchestration can distribute inference workloads depending on computational requirements as well as data residency regulations. Zero-trust security principles safeguard sensitive financial information through continuous authentication and micro-segmentation.  An explainability layer generates transparent reasoning paths satisfying regulatory examination requirements. The framework establishes a scalable foundation for next-generation financial crime prevention infrastructure.