PREDICTING FINANCIAL OUTCOME

• US 20140101024A1
• Filed: 10/04/2012
• Published: 04/10/2014
• Est. Priority Date: 10/04/2012
• Status: Abandoned Application

• Current Assignee: SAP SE
• Original Assignee: SAP AG (SAP SE)
• Inventors: Li, Wen-Syan, Luwang, Tianyu Raymond
• Application Number:
  US13/644,814
• Publication Number:
  US 20140101024A1
• Time in Patent Office: Days
• US Class Current: 705/38
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof

Abstract

The embodiments provide a system for predicting financial outcome of an order. The system includes a discriminant model training module configured to receive historical orders from a data source and to generate discriminant model parameters based on the historical orders, a discriminant model engine configured to receive at least one order to be analyzed and to calculate a probability for each of a plurality of outcomes for the at least one order to be analyzed based on the discriminant model parameters, and a strategy comparison module configured to calculate an expected business value for at least one strategy based on, in part, the probabilities for the plurality of outcomes to evaluate a risk associated with the at least one order.

Claims

1. A system for predicting financial outcome of an order, the system comprising:

at least one processor;

a non-transitory computer-readable storage medium including instructions executable by the at least one processor, the instructions configured to implement,

a discriminant model training module configured to receive historical orders from a data source, and generate discriminant model parameters based on the historical orders;

a discriminant model engine configured to receive at least one order to be analyzed, and calculate a probability for each of a plurality of outcomes for the at least one order to be analyzed based on the discriminant model parameters; and

a strategy comparison module configured to calculate an expected business value for at least one strategy based on, in part, the probabilities for the plurality of outcomes to evaluate a risk associated with the at least one order.

2. The system of claim 1, wherein the discriminant model training module configured to generate discriminant model parameters based on the historical orders includes:

computing new discriminant model parameters during each iteration based on discriminant model parameters of a previous iteration and a change to the discriminant model parameters from the previous iteration.

3. The system of claim 2, wherein,

the discriminant model training module is configured to calculate the change to the discriminant model parameters from the previous iteration includes calculating a negative log likelihood based on, in part, the historical orders, and calculating the change to the discriminant model parameters based on, in part, a function of a gradient of the negative log likelihood,

the discriminant model training module is configured to output the new discriminant model parameters to the discriminant model engine when a current iteration exceeds a maximum iteration or a change in the negative log likelihood from the previous iteration is less than a threshold value.

4. The system of claim 1, wherein the discriminant model engine configured to calculate a probability for each of a plurality of outcomes for the at least one order to be analyzed based on the discriminant model parameters includes:

calculating a probability that the order to be analyzed belongs to each of the plurality of outcomes based on a function corresponding to each outcome supplied with the discriminant model parameters and a feature vector representing one or more attributes of the at least one order to be analyzed.

5. The system of claim 1, wherein the strategy comparison module configured to calculate an expected business value for at least one strategy based on, in part, the probabilities for the plurality of outcomes includes:

calculating an expected business value for each outcome based on a projected business value and the probability associated with a respective outcome; and

calculating the expected business value for the at least one strategy based on the calculated expected business value for each outcome.

6. The system of claim 1, wherein the expected business value includes an expected revenue.

7. The system of claim 1, wherein the strategy comparison module is configured to provide the expected business value in conjunction with the at least one strategy for display.

8. The system of claim 1, wherein the strategy comparison module is configured to provide, with respect to each outcome for the at least one strategy, a respective projected business value, a respective probability, and a respective expected business value for display, the respective expected business value being provided based on the respective projected business value and the respective probability.

9. The system of claim 1, wherein each historical order includes a first feature vector representing one or more attributes or features of the historical order, and an outcome vector representing a final outcome of a respective historical order, and the at least one order to be analyzed includes a second feature vector representing one or more attributes or features of the at least one order to be analyzed.

10. The system of claim 9, wherein the first feature vector and the second feature vector include customer-related features including financial status and contribution of revenue, and order attributes including amount and type of products for a respective order.

11. The system of claim 1, wherein the at least one strategy includes acceptance of the order and rejection of the order.

12. The system of claim 1, wherein the plurality of outcomes includes on-time payment, overdue payment, and default payment.

13. The system of claim 1, wherein the at least one order to be analyzed includes a product order for at least one product for which a purchase amount of the at least one product exceeds a credit limit associated with a customer.

14. A non-transitory computer-readable medium storing instructions that when executed cause one or more processors to predict financial outcome of an order, the instructions comprising instructions to:

receive historical orders from a data source;

generate discriminant model parameters based on the historical orders;

receive at least one order to be analyzed;

calculate a probability for each of a plurality of outcomes for the at least one order to be analyzed based on the discriminant model parameters; and

calculate an expected business value for at least one strategy based on, in part, the probabilities for the plurality of outcomes to evaluate a risk associated with the at least one order.

15. The non-transitory computer-readable medium of claim 14, wherein the instructions to generate discriminant model parameters based on the historical orders includes instructions to:

compute new discriminant model parameters during each iteration based on discriminant model parameters of a previous iteration and a change to the discriminant model parameters from the previous iteration.

16. The non-transitory computer-readable medium of claim 15, wherein the instructions include instructions to:

calculate the change to the discriminant model parameters from the previous iteration including calculating a negative log likelihood based on, in part, the historical orders, and calculating the change to the discriminant model parameters based on, in part, a function of a gradient of the negative log likelihood; and

output the new discriminant model parameters when a current iteration exceeds a maximum iteration or a change in the negative log likelihood from the previous iteration is less than a threshold value.

17. The non-transitory computer-readable medium of claim 14, wherein the instructions to calculate a probability for each of a plurality of outcomes for the at least one order to be analyzed based on the discriminant model parameters includes instructions to:

calculate a probability that the order to be analyzed belongs to each of the plurality of outcomes based on a function corresponding to each outcome supplied with the discriminant model parameters and a feature vector representing one or more attributes of the at least one order to be analyzed.

18. The non-transitory computer-readable medium of claim 14, wherein the instructions to calculate an expected business value for at least one strategy based on, in part, the probabilities for the plurality of outcomes to evaluate a risk associated with the at least one orders includes instructions to:

calculate an expected business value for each outcome based on a projected business value and the probability associated with a respective outcome; and

calculate the expected business value for the at least one strategy based on the calculated expected business value for each outcome.

19. The non-transitory computer-readable medium of claim 14, wherein the expected business value includes an expected revenue.

20. A method for predicting financial outcome of an order performed by at least one processor, the method comprising:

receiving, including the at least one processor, historical orders from a data source;

generating, including the at least one processor, discriminant model parameters based on the historical orders;

receiving, including the at least one processor, at least one order to be analyzed;

calculating, including the at least one processor, a probability for each of a plurality of outcomes for the at least one order to be analyzed based on the discriminant model parameters; and

calculating, including the at least one processor, an expected business value for at least one strategy based on, in part, the probabilities for the plurality of outcomes to evaluate a risk associated with the at least one order.