ENHANCING RETAIL FRAUD DETECTION WITH ISOLATION FORESTS AND AUTOENCODERS: OVERCOMING DATA LIMITATIONS AND REGULATORY CHALLENGES
Keywords:
Retail Fraud Detection, Isolation Forest, Autoencoder, Feature Engineering, Regulatory ComplianceAbstract
Retail fraud results in significant financial losses for businesses worldwide, and the challenges are compounded by limited access to data and strict regulatory constraints. This article proposes a novel approach that combines isolation forests, autoencoders, and strategic feature engineering to enhance fraud detection in retail settings under these limitations. The proposed methodology leverages the strengths of isolation forests in detecting anomalies in high-dimensional data, autoencoders in learning normal transaction patterns, and feature engineering in capturing domain-specific insights. Experiments conducted on a real-world retail transaction dataset demonstrate the superior performance of the proposed approach compared to traditional anomaly detection techniques. The isolation forest and autoencoder models achieve high precision and recall, outperforming benchmark studies. The strategic feature engineering approach further enhances the models' ability to capture the intricacies of retail fraud. The proposed methodology offers a scalable and adaptable solution for retailers to combat fraud effectively while adhering to data limitations and regulatory requirements.
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