Papers by Aleteia Araujo
Inferring about fraudulent collusion risk on Brazilian public works contracts in official texts using a Bi-LSTM approach (2020.findings-emnlp)
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Marcos Lima, Roberta Silva, Felipe Lopes de Souza Mendes, Leonardo R. de Carvalho, Aleteia Araujo, Flavio de Barros Vidal
| Challenge: | Public works procurements are a preferred field for collusion and fraud in Brazil . current methods of fraud detection use structured data to classification and usually do not involve annotated data. |
| Approach: | They propose to use public works procurements to classify risky entries using a dataset of 15,132,968 textual entries of which 1,907 are annotated. |
| Outcome: | The proposed datasets show that both bottleneck deep neural network and biLSTM are competitive compared with classical classifiers and achieve better precision (93.0% and 92.4%, respectively). |