Behavior-Based Fraud Detection and Performance Transparency in Peer Marketplaces

This article analyzes the specifics of behavior-based fraud detection processes and performance transparency in peer-to-peer marketplaces.The need for the study is driven by the fact that peer-to-peer (P2P) marketplaces, as a key element of the modern digital economy, face the fundamental problem of information asymmetry, which leads to increased fraud and unstable service quality, undermining user trust.The article presents an agent-based management framework, developed and implemented on a large online travel marketplace, that is powered by machine learning.The system uses agents' behavioral data to classify risks in real time through logistic regression and implements radical performance transparency mechanisms to create an effective feedback loop.The implementation of the system has led to significant improvements, namely, a reduction in the number of payment disputes and fraud-related complaints.The article contributes to the theory and practice of platform governance by demonstrating a scalable model for enhancing integrity, fairness, and trust on P2P platforms.