A system and method for using machine learning to generate a model from audited data includes a plurality of data sources, a training server having a machine learning unit, and a prediction/scoring server having a machine learning model and a data repository. The training server is coupled to receive and process information from the plurality of the resources and store it in the data repository. The training server, in particular, the machine learning unit fuses the input data and ground truth data. The machine learning unit applies machine learning to the fused input data and ground truth data to create a model. The machine learning unit then provides the model to the prediction/scoring server for use in processing new data. The prediction/scoring server uses the model to process new data and provide or take actions prescribed by the model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: receiving input data; receiving ground truth data from an audit evaluating the input data; fusing the input data and the ground truth data to create fused data; and applying machine learning to create a model from the fused data. 2. The computer-implemented method of claim 1, further comprising: receiving unprocessed data; processing the unprocessed data with the model created from the fused data to identify an action; and one or more of providing the action and performing the action. 3. The computer-implemented method of claim 1, wherein fusing the input data and the ground truth data to create the fused data comprises: identifying a common identifier; fusing the input data and the ground truth data using the common identifier; and performing data preparation on the fused data. 4. The computer-implemented method of claim 1, wherein the input data is relating to a complex processing workflow. 5. The computer-implemented method of claim 1, wherein the ground truth data is received from an auditor. 6. The computer-implemented method of claim 1, wherein the model includes one or more of a classification model, a regression model, a ranking model, a semi-supervised model, a density estimation model, a clustering model, a dimensionality reduction model, a multidimensional querying model and an ensemble model. 7. The computer-implemented method of claim 2, wherein the action includes one or more of a preventive action, generating a notification, generating qualitative insights, identifying a process from the input data for additional review, requesting more data, delaying the action, determining causation, and updating the model. 8. The computer-implemented method of claim 1, wherein the ground truth data includes one or more of validity data, qualification data, quantification data, correction data, preference data, likelihood data or similarity data. 9. A system comprising: one or more processors; and a memory including instructions that, when executed by the one or more processors, cause the system to: receive input data; receive ground truth data from an audit evaluating the input data; fuse the input data and the ground truth data to create fused data; and apply machine learning to create a model from the fused data. 10. The system of claim 9, wherein the instructions, when executed by the one or more processors, cause the system to: receive unprocessed data; process the unprocessed data with the model created from the fused data to identify an action; and one or more of provide the action and perform the action. 11. The system of claim 9, wherein to fuse the input data and the ground truth data to create the fused data, the instructions when executed by the one or more processors, cause the system to: identify a common identifier; fuse the input data and the ground truth data using the common identifier; and perform data preparation on the fused data. 12. The system of claim 9, wherein the input data is relating to a complex processing workflow. 13. The system of claim 9, wherein the ground truth data is received from an auditor. 14. The system of claim 9, wherein the model includes one or more of a classification model, a regression model, a ranking model, a semi-supervised model, a density estimation model, a clustering model, a dimensionality reduction model, a multidimensional querying model and an ensemble model. 15. The system of claim 10, wherein the action includes one or more of a preventive action, generating a notification, generating qualitative insights, identifying a process from the input data for additional review, requesting more data, delaying the action, determining causation, and updating the model. 16. The system of claim 9, wherein the ground truth data includes one or more of validity data, qualification data, quantification data, correction data, preference data, likelihood data or similarity data. 17. A computer-program product comprising a non-transitory computer usable medium including a computer readable program, wherein the computer readable program, when executed on a computer, causes the computer to perform operations comprising: receiving input data; receiving ground truth data from an audit evaluating the input data; fusing the input data and the ground truth data to create fused data; and applying machine learning to create a model from the fused data. 18. The computer program product of claim 17, wherein the operations further comprise: receiving unprocessed data; processing the unprocessed data with the model created from the fused data to identify an action; and one or more of providing the action and performing the action. 19. The computer program product of claim 17, wherein fusing the input data and the ground truth data to create the fused data includes: identifying a common identifier; and fusing the input data and the ground truth data using the common identifier; performing data preparation on the fused data. 20. The computer program product of claim 17, wherein the input data is relating to a complex processing workflow.