Automated computer-based prediction of rejections of requisitions

1. A computer-implemented method comprising:

receiving at a computer system and digitally storing local statistical information and global statistical information for electronic digital past request data representing past requests of client devices that were submitted via a computer system and that were reviewed in an approval chain that comprises a series of approval computers, the local statistical information being associated with past requests of client devices of a particular enterprise and the global statistical information being associated with past requests of other enterprises;

the computer system receiving, from a client device, electronic digital requisition data representing a request;

generating, in real time in response to the request, and based on the request and the local statistical information and global statistical information for electronic digital past request data representing past requests, a probability value indicating a likelihood that the request will be rejected if the request is submitted to the computer system for processing in the approval chain;

causing the client device to display a graphical user interface that includes the request and a message that includes the probability value;

generating the probability value by executing a neural network using request data values of the request;

training the neural network by assigning one or more weight values to connections between neurons of the neural network; providing the past request data to the neural network as training data; repeating the training, until output generated by the neural network indicates, within an error value not exceeding a threshold value, that processing the past request data by the neural network results in the neural network predicting an approval of the past request data.

2. The computer-implemented method of claim 1 , the neural network comprising a Fast Artificial Neural Network (FANN).

3. The computer-implemented method of claim 1 , the training data being obtained from requisition forms that were submitted and approved or rejected previously, history logs maintained for previously reviewed requisitions, profiles of users, profiles of approval chains, profiles of suppliers, inventory logs of suppliers.

4. The computer-implemented method of claim 1 , the neural network comprising an input layer configured with neurons to receive, from the requisition data, at least total price, quantity, first supplier, second supplier, delivery date, and percentage of contract.

5. The computer-implemented method of claim 1 , the request comprising any of an invoice, expense report, personal request, employee suggestion submission.

6. The computer-implemented method of claim 1 , the request comprising a purchase requisition and the past request data for each of the past requests comprising a plurality of different requisition data values identifying at least a vendor, one or more products that the vendor is to supply, and one or more metadata values from among order date, date required, shipper, freight terms, shipping address.

7. The computer-implemented method of claim 1 , further comprising retrieving from storage and causing displaying an explanation of why the request data indicates a high probability that a particular request will be rejected if submitted.

8. The computer-implemented method of claim 1 , the local statistical information and global statistical information comprising any of a count of requested items in a request, availability of items in a request, whether requested items were approved in the past, whether requested items meet a quality threshold, whether a supplier of requested items is local, whether fulfilling similar requests for similar items was successful in the past.

9. The computer-implemented method of claim 1 , further comprising parsing justification content of a justification in the request to identify keywords and testing whether the keywords have been also used in requests that were approved in the past.

10. The computer-implemented method of claim 1 , further comprising determining suggestions for rewriting the request by changing supplier from whom items are requested, changing a requested delivery date of requested items, changing a quantity of requested items.

11. The computer-implemented method of claim 1 , further comprising causing to display the probability value in a computer display screen logically near or on the request data or a portion of the request data.

12. The computer-implemented method of claim 1 , further comprising causing to display the message specifying the likelihood that the request will be approved.

13. The computer-implemented method of claim 1 , the request comprising a purchase requisition, the method further comprising generating the probability value by determining a K nearest requisition-neighbors to the request and determining the probability value as a percentage of the K nearest requisition-neighbors that were rejected.

14. A non-transitory computer-readable storage medium storing one or more sequences of instructions which, when executed by one or more processors, cause the one or more processors to perform:

receiving at a computer system and digitally storing local statistical information and global statistical information for electronic digital past request data representing past requests of client devices that were submitted via a computer system and that were reviewed in an approval chain that comprises a series of approval computers, the local statistical information being associated with past requests of client devices of a particular enterprise and the global statistical information being associated with past requests of other enterprises;

the computer system receiving, from a client device, electronic digital requisition data representing a request;

generating, in real time in response to the request, and based on the request and the local statistical information and global statistical information for electronic digital past request data representing past requests, a probability value indicating a likelihood that the request will be rejected if the request is submitted to the computer system for processing in the approval chain;

causing the client device to display a graphical user interface that includes the request and a message that includes the probability value;

generating the probability value by executing a trained neural network using request data values of the request;

training the neural network by assigning one or more weight values to connections between neurons of the neural network; providing the past request data to the neural network as training data; repeating the training, until output generated by the neural network indicates, within an error value not exceeding a threshold value, that processing the past request data by the neural network results in the neural network predicting an approval of the past request data.

15. The non-transitory computer-readable storage medium of claim 14 , the neural network comprising a Fast Artificial Neural Network (FANN).

16. The non-transitory computer-readable storage medium of claim 14 , the training data being obtained from requisition forms that were submitted and approved or rejected previously, history logs maintained for previously reviewed requisitions, profiles of users, profiles of approval chains, profiles of suppliers, inventory logs of suppliers.

17. The computer-implemented method of claim 1 , the local statistical information and global statistical information comprising any of a count of requested items in a request, availability of items in a request, whether requested items were approved in the past, whether requested items meet a quality threshold, whether a supplier of requested items is local, whether fulfilling similar requests for similar items was successful in the past.

18. The computer-implemented method of claim 1 , the request comprising a purchase requisition, the method further comprising generating the probability value by determining a K nearest requisition-neighbors to the request and determining the probability value as a percentage of the K nearest requisition-neighbors that were rejected.