The retail ecosystem has evolved from simple Kirana stores to large omni-channel retail systems. The advent of omni-channel retailing has seen an increased count of fraudsters who can abuse the gap between the online and offline channels to perform different types of fraudulent activities. Fraud can be related to payment, account take over, refund and cancellation abuse, collusion of customers with associates. Waste can be due to over-production, sub-optimal pricing or discounts, damaged products, throwaways, availability issues, packaging waste. The paper tries to identify the need for better research on improvement in fraud detection systems for sparse data, improvement of the customer experience and reduction of returns. The paper also discusses the need for adoption of ML models through better interpretability. The data analysis is performed on omni-channel retail transactions over a period of a year across multiple countries. Machine Learning methods of supervised and unsupervised learning can be used to highlight the outlier cases to focus upon, and then identify the root causes of such losses. Then causal discovery models can be used to identify root causes and provide prescriptive recommendations. The paper provides a comprehensive evaluation of the challenges in omni-channel retail ecosystem and the proposed machine learning tools which can help resolve the issue at large. The implementation of ML techniques in retail total loss management can lead to multimillion- dollar savings through better fraud detection and waste management. Performance of supervised models improves to close to 98% over the course of months by using feedback loop.


Retail loss, fraud management, waste reduction, Machine Learning, anomaly detection, classification, causal discovery, computer vision


Article updated to add co-author's affiliation.

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