Abstract: In one aspect a computerized method for detecting anomalies in expense reports of an enterprise includes the step of implementing a semantic analysis algorithm on an expense report data submitted by an employee, wherein the expense report data is provided in a computer-readable format. The method includes the step of, with one or more machine learning algorithms, detecting an anomaly in expense report data. The method includes the step of obtaining an augmentation of the expense report data with a set of web scale data. The method includes the step of verifying receipts associated with an expense report. The method includes the step of determining that the employee or any employee has previously claimed an expense in the expense report data. The method includes the step of identifying an inappropriate expense in the expense report data.

Abstract: In one aspect a computerized method for detecting anomalies in expense reports of an enterprise includes the step of implementing a semantic analysis algorithm on an expense report data submitted by an employee, wherein the expense report data is provided in a computer-readable format. The method includes the step of, with one or more machine learning algorithms, detecting an anomaly in expense report data. The method includes the step of obtaining an augmentation of the expense report data with a set of web scale data. The method includes the step of verifying receipts associated with an expense report. The method includes the step of determining that the employee or any employee has previously claimed an expense in the expense report data. The method includes the step of identifying an inappropriate expense in the expense report data.

Abstract: In one aspect a computerized method for detecting anomalies in expense reports of an enterprise includes the step of implementing a semantic analysis algorithm on an expense report data submitted by an employee, wherein the expense report data is provided in a computer-readable format. The method includes the step of, with one or more machine learning algorithms, detecting an anomaly in expense report data. The method includes the step of obtaining an augmentation of the expense report data with a set of web scale data. The method includes the step of verifying receipts associated with an expense report. The method includes the step of determining that the employee or any employee has previously claimed an expense in the expense report data. The method includes the step of identifying an inappropriate expense in the expense report data.

Abstract: In one aspect a computerized method for detecting anomalies anomalies in expense reports of an enterprise includes the step, of implementing a semantic analysis algorithm on an expense report data submitted by an employee, wherein the expense report data is provided in a computer-readable format. The method includes the step of, with one or more machine learning algorithms, detecting an anomaly expense report data. The method includes the step of obtaining an augmentation of the expense report data with a se of web scale data. The method includes the step of verifying receipts associated with an expense report. The method includes the step of determining that the employee or any employee has previously claimed an expense in the expense report data. The method includes the step of identifying an inappropriate expense in the expense report data.