Fraud, Misrepresentation, and Transaction Security in Peer-to-Peer Trading Platforms: Detection Models and Policy Implications

Abstract

Peer-to-peer trading platforms have expanded the set of transactions that occur without traditional intermediaries, relying instead on software-mediated trust, lightweight identity signals, and platform governance. As participation grows and cross-border trading becomes routine, the same features that improve market access also increase exposure to fraud, strategic misrepresentation, and disputes with limited offline enforceability. This paper analyzes detection and security mechanisms for peer-to-peer trading under adaptive adversaries, focusing on how data-driven models interact with product design, transaction protocols, and policy constraints. A technical framework is developed that treats fraud risk as a sequential, partially observed process spanning onboarding, listing, negotiation, payment, fulfillment, and dispute resolution, with feedback loops created by enforcement actions and reputation systems. The paper examines learning objectives aligned to operational costs, including chargebacks, subsidy leakage, account recovery, manual review capacity, and user attrition, and it discusses how to manage label noise, delayed outcomes, and selection bias induced by interventions. Modeling approaches include cost-sensitive classification, semi-supervised anomaly detection, graph-based inference over user-transaction-device networks, and decision-focused thresholding with calibration and uncertainty. Transaction security is discussed as a complement to detection, emphasizing escrow-like holds, authenticated messaging, evidence capture, and settlement design that reshapes incentives. Policy implications are derived for transparency, appeals, privacy, cross-jurisdiction enforcement, and proportional sanctions, highlighting conditions under which model governance and protocol choices reduce harm without suppressing legitimate trade.