Hierarchical Classification of Financial Transactions Through Context-Fusion of Transformer-based Embeddings and Taxonomy-aware Attention Layer
Antonio J. G. Busson, Rafael Rocha, Rennan Gaio, Rafael Miceli, Ivan Pereira, Daniel de S. Moraes, Sérgio Colcher, Alvaro Veiga, Bruno Rizzi, Francisco Evangelista, Leandro Santos, Fellipe Marques, Marcos Rabaioli, Diego Feldberg, Debora Mattos, João Pasqua, Diogo Dias
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This work proposes the Two-headed DragoNet, a Transformer-based model for hierarchical multi-label classification of financial transactions. Our model is based on a stack of Transformers encoder layers that generate contextual embeddings from two short textual descriptors (merchant name and business activity), followed by a Context Fusion layer and two output heads that classify transactions according to a hierarchical two-level taxonomy (macro and micro categories). Finally, our proposed Taxonomy-aware Attention Layer corrects predictions that break categorical hierarchy rules defined in the given taxonomy. Our proposal outperforms classical machine learning methods in experiments of macro-category classification by achieving an F1-score of 93\% on a card dataset and 95% on a current account dataset.