Sap Se

Anna Fishbein

Lev Sigal

Anton Ioffe

Ran Bittmann

Iryna Butselan

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.

Classifying documents based on machine learning

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.

Expense-type audit machine learning modeling system

Some embodiments provide a non-transitory machine-readable medium that stores a program. The program queries a database for a subset of a plurality of records in the database. Each record in the plurality of records includes a value for a first field and a second value for a second field. The program further normalizes the first value of the first field of each record in the subset of the plurality of records. The program also divides the subset of the plurality of records into a plurality of groups of records based on the second values of the second field. The program further generates a function for predicting a type of a particular record based on the value of the field of the particular record.

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Hadera, Israel

Anna Fishbein