Predictive modeling for unintended outcomes

1. A computer-implemented method comprising:

accessing an order history that, for each of one or more past orders, indicates (i) one or more order details associated with the order, and (ii) a fulfillment outcome associated with the order, wherein the order fulfillment outcome is selected from among multiple pre-defined order fulfillment outcomes including one or more intended order fulfillment outcomes and one or more unintended order fulfillment outcomes;

for each of one or more particular unintended order fulfillment outcomes, selecting one or more particular past orders that are associated with the particular unintended order fulfillment outcome;

for each of the one or more particular unintended order fulfillment outcomes, generating, using the one or more order details associated with the one or more particular past orders that are associated with the particular unintended order fulfillment outcome as training data, a predictive model that is trained to estimate, based on one or more given order details associated with a given order, a likelihood that the given order will be associated with the particular unintended order fulfillment outcome;

receiving one or more order details associated with a subsequently received order;

for each of the predictive models, providing the one or more order details as input to the predictive model;

in response to providing the one or more order details as input to the predictive model, receiving, from each of the predictive models, a likelihood that the one or more order details will be associated with the particular unintended order fulfillment outcome;

determining, for each indication of the likelihood, whether the likelihood satisfies a threshold;

for likelihoods that satisfy the threshold, identifying a remedial action that, when implemented, adjusts the likelihood to not satisfy the threshold; and

providing, for output, data indicating the remedial action.

2. The method of claim 1 , wherein the one or more order details comprise goods ordered, a quantity of each good, a total value, and a purchaser.

3. The method of claim 1 , wherein the predictive model is purchaser specific.

4. The method of claim 1 , wherein the one or more unintended order fulfillment outcomes comprise late payment, order dispute, and order cancellation.

5. The method of claim 1 , wherein the remedial action comprises dividing an order, generating a target invoice date, notifying a purchaser of an outstanding invoice, or applying a discount.

6. The method of claim 1 , comprising:

preventing an order associated with the one or more order details from processing until execution of the remedial action.

7. The method of claim 1 , wherein the remedial action is associated with an order receiving stage, a billing stage, or a collection stage.

8. A system comprising:

one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:

accessing an order history that, for each of one or more past orders, indicates (i) one or more order details associated with the order, and (ii) a fulfillment outcome associated with the order, wherein the order fulfillment outcome is selected from among multiple pre-defined order fulfillment outcomes including one or more intended order fulfillment outcomes and one or more unintended order fulfillment outcomes;

for each of one or more particular unintended order fulfillment outcomes, selecting one or more particular past orders that are associated with the particular unintended order fulfillment outcome;

for each of the one or more particular unintended order fulfillment outcomes, generating, using the one or more order details associated with the one or more particular past orders that are associated with the particular unintended order fulfillment outcome as training data, a predictive model that is trained to estimate, based on one or more given order details associated with a given order, a likelihood that the given order will be associated with the particular unintended order fulfillment outcome; and

using one or more of the predictive models to estimate a likelihood that a subsequently received order will be associated with one or more of the unintended order fulfillment outcomes.

9. The system of claim 8 , wherein the one or more order details comprise goods ordered, a quantity of each good, a total value, and a purchaser.

10. The system of claim 8 , wherein the predictive model is purchaser specific.

11. The system of claim 8 , wherein the one or more unintended order fulfillment outcomes comprise late payment, order dispute, and order cancellation.

12. The system of claim 8 , wherein the one or more intended order fulfillment outcomes comprises a timely, received payment.

13. The system of claim 8 , wherein the predictive model is generated using logistic regression, support vector machine, random forest, multiple linear regression, Bayes' theorem.

14. A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising:

accessing, for each of one or more particular unintended order fulfillment outcomes, a predictive model that is trained to estimate, based on one or more given order details associated with a given order, a likelihood that the given order will be associated with the particular unintended order fulfillment outcome;

receiving one or more order details associated with a subsequently received order;

for each of the predictive models, providing the one or more order details as input to the predictive model;

in response to providing the one or more order details as input to the predictive model, receiving, from each of the predictive models, a likelihood that the one or more order details will be associated with the particular unintended order fulfillment outcome;

determining, for each indication of the likelihood, whether the likelihood satisfies a threshold;

for likelihoods that satisfy the threshold, identifying a remedial action that, when implemented, adjusts the likelihood to not satisfy the threshold; and

providing, for output, data indicating the remedial action.

15. The medium of claim 14 , wherein the one or more order details comprise goods ordered, a quantity of each good, a total value, and a purchaser.

16. The medium of claim 14 , wherein the predictive model is purchaser specific.

17. The medium of claim 14 , wherein the one or more unintended order fulfillment outcomes comprise late payment, order dispute, and order cancellation.

18. The medium of claim 14 , wherein the remedial action comprises dividing an order, generating a target invoice date, notifying a purchaser of an outstanding invoice, or applying a discount.

19. The medium of claim 14 , wherein the operations further comprise:

preventing an order associated with the one or more order details from processing until execution of the remedial action.

20. The medium of claim 14 , wherein the remedial action is associated with an order receiving stage, a billing stage, or a collection stage.