AI-Based Bot Detection for Dynamic Pricing Engine in Retail and Logistics Platforms
DOI:
https://doi.org/10.58425/ajt.v5i1.475Keywords:
Bot detection, dynamic pricing, explainable AI, agentic AI, graph-based modeling, machine learning ensembles, retail analyticsAbstract
Aim: Dynamic-pricing services rely on good demand indicators, but the retail and logistics sites continue to be affected by price bots. These automated agents interact with traffic volumes and corrupt the input on price optimization. This study aims to develop and evaluate a hybrid artificial intelligence–based bot-detection framework that identifies automated sessions in retail and logistics platforms and provides explainable traffic-quality signals for dynamic pricing systems.
Methods: The study adopts a system-based experimental design that integrates graph-based modeling, sequence learning, Agentic AI-generated synthetic traces, and ensemble classification. Data were drawn from anonymized web logs, honeypot captures, and multi-agent adversarial simulations. Models are tested in terms of precision, recall, AUC, and stability of the pricing engine.
Results: The proposed pipeline achieved high detection performance (AUC ≈ 0.94) and showed that graph metrics and timing distribution characteristics were the most prominent predictors of automated behavior.
Conclusion: The use of AI-based synthetic traces enhanced the model’s strength, aligning with BotChase and other research studies.
Recommendation: This study suggests that a hybrid AI/ML-based bot detection approach can enhance the reliability of demand signals used in dynamic pricing systems, providing a viable alternative to traditional binary blocking methods. This helps significantly in preventing pricing distortion and enhancing market resilience.
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Copyright (c) 2026 Rohit Grover
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