Method and system for predicting the probability of regulatory compliance approval
Methods and systems for automatically predicting the probability of regulatory compliance approval based on data contained in a data structure. A data structure can be configured to include data collated and collected from one or more regulators (e.g., regulatory agencies) and one or more value chain participants. Such data is inclusive of data indicative of actual approval-request results of applications for regulatory approval by one or more regulators and variable weights assigned to different data elements. A prediction can be then made as to the probability that an application for regulatory approval by a value chain participant will be approved by a regulator based on the data collated and collected and contained in the data structure. This approach predicts the probability that an application for regulatory approval by a value chain participant will be approved and also predicts what is missing for a regulatory approval application.
1. A method for automatically predicting a probability of regulatory compliance approval based on data contained in a data structure, comprising:
configuring said data structure comprising first data collated and collected from a plurality of regulators and a plurality of value chain participants, said data structure provided by an RCS (Regulatory Compliance System) that collates the first data and restricts disclosure in a value chain by localizing reporting to an individual value chain participant among the plurality of value chain participants and direct suppliers, said first data inclusive of second data indicative of actual approval-request results of applications for regulatory approval and other data including assigned weights and weighted data, the RCS further operable to use a distributed ledger to ensure that responses from the plurality of regulator are securely recorded and reconstructable;
predicting with a RCAP (Regulatory Compliance Approval Prediction) module comprising a machine learning engine, predictive data including a probability that an application for regulatory approval by at least one regulatory agency of a value chain participant among said plurality of value chain participants will be approved by a regulator of the at least one regulatory agency among said plurality of regulators based on said first data collated and collected from said plurality of regulators and contained in said data structure including said second data indicative of actual approval-request results of applications for regulatory approval and the other data including the assigned weights and the weighted data, the predictive data including an outcome determination and a probability score indicative of a confidence of the predictive data for the at least one regulatory agency, wherein the machine learning engine further comprises a dimensional analysis and prediction engine that provides dimensional history and correlations and wherein analysis by the RCAP module is granular and the RCAP module communicates with the RCS; and
compiling the predictive data for improvements in predictive accuracy, wherein the compiling of the predictive data comprises assigning the weights to the machine learning engine based on an accuracy of at least one predictive classification algorithm in predicting an actual outcome with respect to the actual approval-request results of applications for regulatory approval.
2. The method of claim 1 further comprising:
predicting potential items that may be missing in said application that are potentially needed by said regulator for said regulatory approval of said application; and
providing a summary of said potential items that may be missing in said application that are potentially needed by said regulator for said regulatory approval of said application.
3. The method of claim 1 further comprising:
improving an accuracy of said predicting said probability that said application by said value chain participant for said regulatory approval will be approved by analyzing results of said predicting said probability over an interval of time.
4. The method of claim 1 , wherein the machine learning engine comprises the at least one predictive classification algorithm.
5. The method of claim 1 wherein said machine learning engine functions as a high volume correlation engine that as said high volume correlation engine takes in new data, said high volume correlation engine constantly updates a value of each data element in said data structure in determining a predicted outcome.
6. The method of claim 5 wherein a predicted outcome of said high volume correlation engine comprises at least one of the following predicted outcomes: 1) approved by said regulator but not by another regulator among said plurality of regulators, and 2) undetermined.
7. The method of claim 5 wherein predicted outcome is based on specific regulatory criteria and published regulatory criteria and further based on decision-making criteria comprising a history of decision-making by said plurality regulators against each vendor approved or denied by the plurality of regulators.
8. A system for automatically predicting a probability of regulatory compliance approval based on data contained in a data structure, said system comprising:
at least one processor; and
a non-transitory computer-usable medium embodying computer program code, said non-transitory computer-usable medium configured to communicates with said at least one processor, said computer program code comprising instructions executable by said at least one processor and configured for:
configuring said data structure comprising first data collated and collected from a plurality of regulators and a plurality of value chain participants, said data structure provided by an RCS (Regulatory Compliance System) that collates the first data and restricts disclosure in a value chain by localizing reporting to an individual value chain participant among the plurality of value chain participants and direct suppliers, said first data inclusive of second data indicative of actual approval-request results of applications for regulatory approval and other data including assigned weights and weighted data, the RCS further operable to use a distributed ledger to ensure that responses from the plurality of regulator are securely recorded and reconstructable;
predicting with a RCAP (Regulatory Compliance Approval Prediction) module comprising a machine learning engine, predictive data including a probability that an application for regulatory approval by at least one regulatory agency of a value chain participant among said plurality of value chain participants will be approved by a regulator of the at least one regulatory agency among said plurality of regulators based on said first data collated and collected from said plurality of regulators and contained in said data structure including said second data indicative of actual approval-request results of applications for regulatory approval and the other data including the assigned weights and the weighted data, the predictive data including an outcome determination and a probability score indicative of a confidence of the predictive data for the at least one regulatory agency, wherein the machine learning engine further comprises a dimensional analysis and prediction engine that provides dimensional history and correlations and wherein analysis by the RCAP module is granular and the RCAP module communicates with the RCS; and
compiling the predictive data for improvements in predictive accuracy, wherein the compiling of the predictive data comprises assigning the weights to the machine learning engine based on an accuracy of at least one predictive classification algorithm in predicting an actual outcome with respect to the actual approval-request results of applications for regulatory approval.
9. The system of claim 8 wherein said instructions are further configured for:
predicting potential items that may be missing in said application that are potentially needed by said regulator for said regulatory approval of said application; and
providing a summary of said potential items that may be missing in said application that are potentially needed by said regulator for said regulatory approval of said application.
10. The system of claim 8 wherein said instructions are further configured for:
improving an accuracy of said predicting said probability that said application will be approved by analyzing results of said predicting said probability over an interval of time.
11. The system of claim 8 wherein said instructions are further configured for predicting said probability that said application by said value chain participant for regulatory approval will be approved, utilizing a classification algorithm.
12. The system of claim 8 wherein said machine learning engine comprises a high volume correlation engine that as said high volume correlation engine takes in new data, said high volume correlation engine constantly updates a value of each data element in said data structure in determining a predicted outcome.
13. The system of claim 12 wherein a predicted outcome of said high volume correlation engine comprises at least one of the following predicted outcomes: 1) approved by said regulator but not by another regulator among said plurality of regulators; and 2) undetermined.
14. The system of claim 13 wherein predicted outcome is based on specific regulatory criteria and published regulatory criteria.