Balancing act: Tackling organized retail fraud on e-commerce platforms with imbalanced learning text models

• Text-based ML approach with synonym replacement achieves top fraud detection results. • Random forest classifier with Fasttext signal extraction achieves optimal performance. • ML-based fraud detection outperforms rule-based methods and scales effectively. • Cybercrime and ORC fraud surge due to increased digital activity during COVID-19. As online shopping expands rapidly, so does the prevalence of fraud, resulting in significant losses for retailers. According to the 2020 National Retail Federation (NRF) report, organized retail crime costs retailers nearly 800, 000 per billion in sales, with an expected global annual increase of over fourteen percent. This paper introduces a text-based fraud detection framework to mitigate these losses efficiently. The framework comprises four key components: text preprocessing, representation, knowledge extraction via machine learning algorithms, and model evaluation. By integrating data augmentation techniques, the framework enhances classifier performance in detecting fraud. The proposed method, employing a combination of FastText and Random Forest classifiers, achieves an impressive F1 score of 0. 833 and AUC score of 0. 99 on an augmented dataset, surpassing conventional keyword-based models. Informed by best practices in fraud detection, this scalable framework promises a solution to combat the escalating fraud associated with the exponential growth of online shopping.