Abstract: A device may be configured to obtain text from a document. The device may perform embedding to obtain a data structure indicating probabilities associated with characters included in the text and apply a first convolution to the data structure to obtain different representations of the characters included in the text. In addition, the device may apply parallel convolution to the different representations to obtain multiple sets of character representations, subsample the multiple sets of character representations, and pool the subsampled multiple sets of character representations into a merged data structure. The device may provide the merged data structure to a fully connected layer, of a convolutional neural network, to produce data representing features of the text; and provide the data representing features of the text to an inference layer, of the convolutional neural network, that provides data indicating a classification for the text.

Abstract: This document describes systems, methods, devices, and other techniques for detecting procurement fraud in one or more procurement processes. In some implementations, a computing device receives input data representing one or more procurement processes, processes the received input data to generate a respective risk score for each procurement process, each risk score representing a likelihood that the respective procurement process is fraudulent, comprising processing the received input data using (i) one or more predetermined rules and scenarios, and (ii) atypical patterns data mined through unsupervised learning mechanisms, and provides, based on the generated risk score, output data indicating procurement processes that are likely to be fraudulent.