Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program may receive a plurality of string data. The program may determine an embedding for each string data in the plurality of string data. The program may cluster the embeddings into groups of embeddings. The program may determine a plurality of labels for the plurality of string data based on the groups of embeddings. The program may use the plurality of labels and the plurality of string data to train a classifier model. The program may provide a particular string data as an input to the trained classifier model, wherein the classifier model is configured to determine, based on the particular string data, a classification for the particular string data.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives an image of a document, the document comprising a set of text. The program further provides the set of text to a machine learning model configured to determine, based on the set of text, a plurality of probabilities for a plurality of defined types of documents. Based on the plurality of probabilities for the plurality of defined types of documents, the program also determines a type of the document from the plurality of defined types of documents.

Abstract: Systems and methods are provided for training a machine learning model to use comments entered by a user submitting an expense to determine a correct expense type. The trained machine learning model is used to predict an expense type by analyzing submitted text comments corresponding to a submitted expense. The expense can be flagged if a mismatch is determined between the expense type of the submitted expense and the predicted expense type, or the submitted expense can be automatically updated to the predicted expense type.

Abstract: The present disclosure involves systems, software, and computer implemented methods for transaction auditing. One example method includes training at least one machine learning model to determine features that can be used to determine whether an image is an authentic image of a document or an automatically generated document image, using a training set of authentic images and a training set of automatically generated document images. A request to classify an image as either an authentic image of a document or an automatically generated document image is received. The machine learning model(s) are used to classify the image as either an authentic image of a document or an automatically generated document image, based on features included in the image that are identified by the machine learning model(s). A classification of the image is provided. The machine learning model(s) are updated based on the image and the classification of the image.

Abstract: The present disclosure involves systems, software, and computer implemented methods for transaction auditing. One example method includes receiving a request to authenticate a document image. The image is preprocessed to prepare the image for line orientation analysis. The preprocessed image is analyzed to determine lines in the preprocessed image. The determined lines are automatically analyzed by performing line orientation test(s) on the determined lines to generate line orientation test result(s) for the preprocessed image. The line orientation test result(s) are evaluated to determine whether the image is authentic. In response to determining that at least one line orientation test result matches a predefined condition corresponding to an unauthentic document, a determination is made that the image is not authentic.

Abstract: The present disclosure involves systems, software, and computer implemented methods for transaction auditing. One example method includes training at least one machine learning model to determine features that can be used to determine whether an image is an authentic image of a document or an automatically generated document image, using a training set of authentic images and a training set of automatically generated document images. A request to classify an image as either an authentic image of a document or an automatically generated document image is received. The machine learning model(s) are used to classify the image as either an authentic image of a document or an automatically generated document image, based on features included in the image that are identified by the machine learning model(s). A classification of the image is provided. The machine learning model(s) are updated based on the image and the classification of the image.

Abstract: The present disclosure involves systems, software, and computer implemented methods for transaction auditing. One example method includes receiving a request to authenticate a document image. The image is preprocessed to prepare the image for line orientation analysis. The preprocessed image is analyzed to determine lines in the preprocessed image. The determined lines are automatically analyzed by performing line orientation test(s) on the determined lines to generate line orientation test result(s) for the preprocessed image. The line orientation test result(s) are evaluated to determine whether the image is authentic. In response to determining that at least one line orientation test result matches a predefined condition corresponding to an unauthentic document, a determination is made that the image is not authentic.