Wolters Kluwer Elm Solutions, Inc.

Jeetu Gupta

Sandeep Sacheti

Abhishek Mittal

Florence Merceron

Anand K Ramteke

Sharon Horozaniecki

A description of a machine learning (ML) model is received, with the ML model including multiple features such as an unlikely combination feature, which corresponds to a first attribute to be located in an invoice and a second attribute to be located the invoice concurrently with the first attribute. Training data is received, including (i) invoice data with multiple invoices, each including the first attribute and the second attribute, and respective values of the first attribute and the second attribute, and (ii) validity data including indications of which of the invoices are valid and which of the invoices are invalid. The ML model is trained using the training data using the ML model. The training includes applying the values of the attributes to the unlikely combination feature. The ML model is applied to an invoice to be validated to determine a probability that the invoice is invalid.

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Jeetu Gupta