Methodology for automatic extraction of red flags in public procurement

Procurement fraud brings severe economic and social damage around the World. Losses in revenue reach the magnitude of trillions of dollars. Control bodies around the World expend great efforts in an attempt to reduce such waste. The audit work involves the analysis of procurement by specialists. The high number of processes analyzed and the time required for fraud analysis come in an inefficient and low fraud detection rate. Several computational models have been proposed in recent years to automate the detection and prediction of procurement fraud. However, most of these models depend on human intervention to extract red flags that the machine should consider in detecting fraud. We propose the use of BERT with NLP techniques for the automatic extraction of red flags used in detecting fraud in procurement. Experimental results show that pre-trained contextualized language models are competitive with other methods.