Detecting Channel Stuffing: An Unsupervised Learning Approach

ABSTRACT Channel stuffing has been a focal point for both management and regulators. Managing sales activities across countries and ensuring accurate recording of sales is challenging for management, especially in multinational corporations. This study develops a state-of-the-art channel stuffing detection framework from an internal perspective using statistical analysis alongside unsupervised outlier detection techniques. The primary goal of this framework is to identify unusual sales activities related to channel stuffing and ensure that sales activities are conducted per the company’s compliance policy. This framework provides management with a holistic and granular view of sales activities at the country and customer levels, indicates unusual trends and patterns of transactions, and underscores high-risk channel stuffing activities for further investigation. The framework also enhances the company’s monitoring procedures and produces tangible applications. Data Availability: Data are from private sources.