Fairlytics: AI-based Intelligent Dynamic Pricing with Discount Authenticity Validation
Keywords:
Anomaly detection, Consumer trust, Discount authenticity, Dynamic pricing, E-Commerce, Ensemble learning, Fake discount detection, Machine learning, Price fairnessAbstract
E-commerce platforms increasingly rely on algorithmic pricing to remain competitive, yet two problems persist side by side: prices that fail to track real-time demand, and discount offers that mislead buyers through inflated “strike-through” reference prices, fake festival markdowns, and recycled coupon claims. This article presents Fairlytics, an AI-based intelligent system that unifies dynamic pricing with automated discount authenticity validation in a single pipeline. Fairlytics estimates a fair, demand-responsive price for a product using historical sales, competitor prices, inventory levels, and seasonal signals, and simultaneously verifies whether an advertised discount is genuine by reconstructing the product's true historical price trajectory and comparing it against the claimed original price. A hybrid ensemble of regression and gradient-boosted tree models drives the pricing engine, while a time-series anomaly detector and rule-based authenticity scorer flag manipulated discounts. The proposed architecture is modular, combining data ingestion, feature engineering, dual prediction engines, and a fairness-scoring dashboard for administrators and consumers. This work is intended to improve pricing transparency, protect consumers from deceptive discounting, and give platform operators a defensible, explainable basis for dynamic pricing decisions.
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