Explainable Anomaly Detection in Retail Perpetual Inventory Systems Using Shap-Enhanced Isolation Forests
Year: 2025
Type: article
Abstract: The systems that rely on correct data can also be afflicted with anomalies related to crime, barcode scan problems, user error, and process difficulties that, if not detected can lead to financial losses and disruptions to supply chains. Traditional anomaly detection systems identify potentially fraudulent records but have little accountability through transparency. As such, managers have little trust in the "black-box" results generated. This study provides one way of applying current ability in isolating the best anomalies - isolation forests, one of the leading anomaly detection strategies, and SHAP (SHapley Additive exPlanations), one of the premier explainable AI. They demonstrate not only that the isolation forest aims to estimate the likelihood that a record is anomalous, they make clear the factors within the records that contribute to each violation - whether that is increased sales from promotions, negative value inventory, or promoting unusual sequences of transactions. Our datasets consist of real retail inventory records, which shows how well an isolation forest + SHAP framework performs compared to other models well known to anomaly detection. By making these models explainable, they not only become more effective, the new situates them under a decision support system given their manageria1 trustworthiness to reduce shrinkage, and provide greater operational resiliency to retail firms.
DOI: 10.14419/5csy6a64
Author: Shiva Kumar Ramavath
Institution: University of North Texas
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7117372298