Supply chain fraud prediction with machine learning and artificial intelligence

Abstract

As businesses undergo digital transformation, supply chain fraud poses an increasing threat, necessitating more sophisticated detection and prevention methods. This paper explores the application of machine learning (ML) and artificial intelligence (AI) in detecting and preventing supply chain fraud. The research design involves analyzing a dataset of supply chain operations and employing various ML algorithms to detect consumer-based fraud within the supply chain, which occurs when consumers partake in deceptive practices during the order process of e-commerce transactions. We analyzed 180,000 transactions from an international company recorded between 2015 and 2018. This study emphasises the necessity of human oversight in interpreting the results generated by these technologies. The implications of supply chain fraud on financial stability, legal standing, and reputation are discussed, along with the potential for ML technology to identify irregularities indicative of fraud. Descriptive findings highlight the prevalence of fraudulent transactions in specific payment types. The AI sequential and the CatBoost classifiers were the top-performing algorithms across all performance metrics. The top features to detect unusual orders are delivery status, payment type, and late delivery risks. The discussion emphasises the promising predictive capabilities of the ML and AI models and their implications for detecting supply chain fraud.