Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3467 2018 2018 University of Tasmania, Hobart city, Australia Botirjon Botirjon Tashkent State University of Economics, 100066, Tashkent city, Islam Karimov st. 49, Uzbekistan Murodjon Sultanov University of Liverpool, Liverpool city, UK Jasurbek Nematullaev This paper aims at presenting an overview of the most popular outlier detection methods that can be used in the retail sector to solve such important problems as fraud, inventory issues, and untypical customer behavior. The techniques discussed in this paper include the conventional statistical methods such as Z-score, Mahalanobis Distance, and Elliptic Envelope and the advanced machine learning methods such as Local Outlier Factor (LOF), Isolation Forest, and DBSCAN. Each method is discussed in detail and the advantages and disadvantages of each are evaluated in relation to different retail scenarios. The primary contribution of this study is the new approach to use Artificial Neural Networks (ANN) for tuning contamination parameters in the Elliptic Envelope model, which makes the anomaly detection more accurate and efficient. Furthermore, the study also depicts the application of min-max scaling for normalizing the features where it helps in reducing the effect of outliers and thus improves the model performance. The results show that the integration of the statistical and machine learning methods is very useful for the real-time detection of anomalies particularly in the ever-changing environment of the retail industry. This research presents a practical insight and new methodological approaches that may be useful for researchers and practitioners who develop outlier detection systems. The outcomes of this study have the potential of enhancing data fusion quality, workflow, and decision-making in the context of retailing. 2025 2025 269 287 10.54216/FPA.180119 https://www.americaspg.com/articleinfo/3/show/3467