Enhancing Structured Fraud Detection in Trade Operations through a Multi-Aspect Tabular Transformer-Based Architecture - Silva, Douglas B.; Silva, Nadia F. F. da; Carvalho, Sergio T. (Anais do Latin American Symposium on Digital Government (LASDiGov); 2026: Anais do XIV Latin American Symposium on Digital Government, 2026)
Structured tax fraud supported by shell companies remains a persistent challenge in goods and services trading, as valid invoices used to simulate non-existent transactions enable undue tax advantages. This paper proposes a multi-aspect shell company detection approach based on Transformer models applied to tabular data. The models capture complex dependencies between heterogeneous attributes and improve performance over traditional ensemble methods, particularly in recall, which is critical for highly imbalanced fraud detection scenarios. Results using real tax data indicate that Transformer-based tabular modeling is an effective and scalable alternative for supporting fiscal intelligence in identifying structured fraud.