AI-Driven Sales Anomaly Detection: Address Revenue Leakage NowAI-Driven Sales Anomaly Detection: Address Revenue Leakage Now
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kaze nesia
• Mar 18
AI-Driven Sales Anomaly Detection: Executive Intelligence Report
A machine learning investigation analyzing 2.8 million retail transactions across seven countries. The project utilizes a multi-model ensemble—including Isolation Forest, Local Outlier Factor (LOF), One-Class SVM, and LSTM Autoencoders—to identify revenue leakage, pricing inconsistencies, and operational risks within global retail operations.
Critical Anomalies: Four high-priority transactions were unanimously flagged by all four independent machine learning models, representing the most significant and actionable risks identified in the dataset. Systemic Vulnerabilities: The analysis pinpointed specific weaknesses in discount authorization processes and pricing chain integrity, particularly within the UAE market.
Data AnalysisData EngineeringData SciencepandasPythonscikit-learn
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