Effectiveness of AI-Based Fraud Detection in Online Pharmacies

Saurabh Bhowmik

Independent Researcher

West Bengal, India

Abstract

The evolution of digital healthcare platforms and e-commerce has led to the exponential rise of online pharmacies, transforming the pharmaceutical landscape by offering enhanced accessibility and convenience to consumers. However, this growth has been paralleled by increasing incidences of fraud, including counterfeit medication distribution, unauthorized dispensing, and identity theft. Artificial Intelligence (AI), even in its foundational forms prior to August 2013, has demonstrated promising capabilities in fraud detection through techniques such as decision trees, naïve Bayes classification, k-nearest neighbors (k-NN), and rule-based inference systems. This manuscript investigates the efficacy of AI-based fraud detection methods deployed in online pharmacies using historical machine learning algorithms available. Emphasis is placed on the classification accuracy, reduction in false positives, and improvement in response time for fraud investigation. The study also explores challenges related to data quality, model bias, and interpretability of AI decisions in regulatory contexts. Findings reveal that legacy AI techniques, despite their limitations in deep representation learning, significantly contributed to mitigating online pharmaceutical fraud when integrated into structured fraud detection pipelines.

Keywords

AI-based fraud detection, online pharmacies, machine learning, rule-based systems, pattern recognition, classification algorithms, supervised learning, counterfeit detection

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