AI Fraud & Review Manipulation Detection

Guarding the Marketplace: AI-Driven Detection of Return Fraud and Review Manipulation in E-commerce 6,000–8,000 words, formatted for a peer-reviewed journal in marketing/e-commerce or applied AI. Objective: Produce an original paper examining how AI and machine learning can detect two major e-commerce trust-and-safety threats: return fraud (e.g., wardrobing, empty-box returns, serial returners, receipt fraud) and review manipulation (e.g., fake reviews, paid review rings, bot-generated ratings). The paper must position trust-and-safety as a distinct, underexplored angle relative to the authors’ prior work on personalization, search advertising, and CLV prediction — referencing those only as adjacent background. Required sections: • Abstract (200–250 words) with 5–6 keywords • Introduction: scale and cost of return fraud and fake reviews to retailers and consumer trust • Literature review (15–25 recent sources, 2023–2026 preferred), covering fraud detection ML NLP for fake-review detection, and anomaly detection • Taxonomy of return fraud and review manipulation types • Technical framework: relevant AI/ML approaches — supervised classifiers (Random Forest, Gradient Boosting), anomaly detection, graph-based methods for detecting coordinated review rings, and NLP/LLM-based fake-review detection • Analysis of features and signals used for detection (behavioral, transactional, linguistic, network) • Evaluation considerations: precision/recall trade-offs, false positives and customer experience impact, adversarial evasion • Ethical and practical implications: consumer fairness, false accusations, privacy, and regulatory context • Limitations and future research • References in the target journal’s citation style Methodology: Conceptual/analytical review with a proposed detection framework. Optionally include an illustrative case study or a small simulated dataset demonstrating one detection approach (e.g., a fake-review classifier or a returns-anomaly model). Note clearly whether any primary data or simulation is expected; otherwise treat as a conceptual review with a proposed framework. Tone & style: Academic, evidence-based, neutral. Original prose only — no plagiarism, no AI-detectable boilerplate, all claims cited. Include an ORCID placeholder for each author. Deliverables: Editable Word document, a plagiarism/originality report, and a reference list with working links/DOIs.

Project ID: 40511698