Integrating Scalable Search and AI-Enhanced Anomaly Detection for Fraud Prevention in Federated Enterprise Transactions

Detecting and preventing fraud in federated enterprise settings is hard because companies must keep data private and follow rules. This article offers a framework to tackle this using search and AI to spot unusual activity. The design has four linked parts that let companies work together to find fraud without sharing private data. Using methods like homomorphic encryption, differential privacy, and federated learning, companies can profit from shared data while keeping control of their own info. The framework mixes different types of machine learning to find both known and new fraud patterns, using understandable AI to keep things open. Advanced search technology helps query lots of past records quickly, and graph neural networks analyze relationships to find tricky fraud groups. The system deals with problems like mixed data, speed needs, false alarms, and rule differences with flexible fixes that keep things running well. Tests in banks, supply chains, and online shops show the framework works well in different situations.