How do online marketplaces detect and remove listings of stolen goods?

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1. 1

   Detecting and reporting suspicious activities - AI-assisted compliance management for social e-commerce platforms

   Electronic Commerce Research (Nan et al.)Published Aug 26, 2026Checked Oct 20, 2026

   “Using an exploratory, case-study approach, we analyse publicly available Instagram posts and identify common textual and visual characteristics associated with suspicious and reportable selling activity. Building on these insights, we develop a proof-of-concept multi-modal deep neural network that jointly learns from image and text features to support automated triage of potential compliance risks. Empirical evaluation shows that the proposed multi-modal model outperforms single-modality baselines, achieving higher overall detection performance (AUC = 0.837; accuracy = 84.1%). The findings demonstrate how AI can elevate a key compliance management constraint by filtering and prioritising high-risk content for human review, while preserving managerial judgment for interpretive and reporting decisions. The study contributes to research on platform governance and compliance management by framing compliance reporting as a constrained operational workflow and illustrating how AI-assisted, human-in-the-loop systems can enhance scalability, accountability, and regulatory responsiveness in social e-commerce ecosystems.”

2. 2

   A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces.

   Scientific reports (Mutemi & Bacao)Published Aug 2, 2023Checked Oct 20, 2026

   “The issue of detecting fraud in general has been studied widely, especially in financial services, but studies focusing on organized retail crimes are extremely rare in literature. To contribute to the knowledge base in this area, we present a scalable machine learning strategy for detecting and isolating ORC listings on a prominent marketplace platform by merchants committing organized retail crimes or fraud. We employ a supervised learning approach to classify postings as fraudulent or real based on past data from buyer and seller behaviors and transactions on the platform. The proposed framework combines bespoke data preprocessing procedures, feature selection methods, and state-of-the-art class asymmetry resolution techniques to search for aligned classification algorithms capable of discriminating between fraudulent and legitimate listings in this context. Our best detection model obtains a recall score of 0.97 on the holdout set and 0.94 on the out-of-sample testing data set. We achieve these results based on a select set of 45 features out of 58.”

3. 3

   Detection of Illicit Content on Online Marketplaces using Large Language Models

   arXiv (Cornell University) (Tran et al.)Published Mar 5, 2026Checked Oct 20, 2026

   “This research investigates the efficacy of Large Language Models (LLMs), specifically Meta's Llama 3.2 and Google's Gemma 3, in detecting and classifying illicit online marketplace content using the multilingual DUTA10K dataset. Employing fine-tuning techniques such as Parameter-Efficient Fine-Tuning (PEFT) and quantization, these models were systematically benchmarked against a foundational transformer-based model (BERT) and traditional machine learning baselines (Support Vector Machines and Naive Bayes). Experimental results reveal a task-dependent advantage for LLMs. In binary classification (illicit vs. non-illicit), Llama 3.2 demonstrated performance comparable to traditional methods. However, for complex, imbalanced multi-class classification involving 40 specific illicit categories, Llama 3.2 significantly surpassed all baseline models. These findings offer substantial practical implications for enhancing online safety, equipping law enforcement agencies, e-commerce platforms, and cybersecurity specialists with more effective, scalable, and adaptive tools for illicit content detection and moderation.”

4. 4

   Vinted blocks French looters listing stolen goods for sale

   BBC NewsPublished Oct 19, 2026Checked Oct 20, 2026

   “The platform said since the weekend it had "taken immediate action to idenitify and remove listings linked to looting".”

5. 5

   E-fencing detection: mining online classified ad websites for stolen property.

   e-scholar@UOIT (University of Ontario Institute of Technology) (Carver)Published Sep 1, 2014Checked Oct 20, 2026

   “With the emergence of e-fencing, there presents a need to automate both the detection of ads selling stolen property and the reporting process for victims. This thesis presents a framework that dynamically retrieves and classifies online ads utilizing artificial intelligence (AI) to minimize amount of domain knowledge required. Evaluating these ads against existing known characteristics of theft as well as extracting new characteristics from suspicious ads. This in conjunction with a reporting system allows users to report events of theft and matches them to previously classified ads. The framework was designed such that it would be domain portable and allow for rapid adaptation to other domains. Experiments showed promising results, correctly classifying single and multiple trend datasets, displaying anomalies in price histograms, and extracting potential patterns that explain price variance. Experiments on other domains highly susceptible to scams displayed unique results contradicting some fundamental assumptions of the behavior of thieves.”

6. 6

   Consumer Protection on the Trade of Stolen Goods at Marketplace in the Perspective of Maslahah Mursalah (Case Study at Facebook Marketplace)

   Al-Iktisab Journal of Islamic Economic Law (Syafiq & Hasanah)Published Nov 8, 2024Checked Oct 20, 2026

   “This research aims to determine the implementation of forms of trading in stolen goods on the Facebook marketplace, forms of legal protection for consumers, as well as consumer protection against trading in stolen goods from the perspective of maslahah mursalah. This research uses empirical (juridical analysis) and qualitative research methods, where this research uses empirical facts and laws. The research results show that trading in stolen goods on Facebook marketplace includes various types of goods with legal risks for perpetrators and consumers. Existing legal protection, based on the Information and Electronic Transactions Law (ITE Law) and the Criminal Code (KUHP), provides the basis for taking action against perpetrators of trafficking in stolen goods and protecting consumer rights. From maslahah mursalah perspective, consumer protection emphasizes justice and public benefit, as well as avoiding detrimental transactions. This research emphasizes the importance of legal and ethical awareness in online transactions to prevent and deal with trafficking in stolen goods.”