Apolo Takeshi Arai Batista
ABSTRACT
Social Commerce has risen and evolved in the last years due to changes either in e-commerce or social networks applications. On top of that, the number of online ads and transactions in Social Commerce has grown. This environment is attractive to either good users and bad users. The bad users cause harm to their victims by making them lose money or suffer psychological damage. Since the volume of transactions is high and the fraud occurrence is low, the manual detection is highly inefficient (too much resource required for low detection) and unscalable. The existing solutions for automatic fraud detection in Social Commerce are based on structured information available in ads such as price, product type, brand, new/used, among others. However, such solutions ignore possible fraud signs from the ads’ images that exhibit the products sold. Therefore, this work aims to evaluate if combining structured information and images available in the ads provides more effective models than the ones considering only structured information. To this end, it proposes FDSC, a method that combines information obtained from ads’ images through deep learning with structured information available in the corresponding ads, in order to detect fraud in Social Commerce. Experimental evidence shows an incremental opportunity of 7% in F-score by the adoption of FDSC.
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Index Terms
- Fraud Detection in Social Commerce: combining structured attributes and images
Index terms have been assigned to the content through auto-classification.
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