Data analytics models for loan treatment

• US 8,775,300 B2
• Filed: 04/10/2013
• Issued: 07/08/2014
• Est. Priority Date: 04/30/2010
• Status: Active Grant

• Current Assignee: Corelogic Solutions LLC (CoreLogic Incorporated)
• Original Assignee: Corelogic Solutions LLC (CoreLogic Incorporated)
• Inventors: Showalter, Thomas
• Primary Examiner(s): Holly, John H
• Application Number:
  US13/860,504
• Publication Number:
  US 20130297489A1
• Time in Patent Office: 454 Days
• Field of Search: 705/38, 705/1.1, 705/36.R, 705/35, 705/40, 705/26.1, 705/30, 705/44, 705/313, 705/306, 717/106, 273/278
• US Class Current: 705/38
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

Data analytics are provided in loan treatment. Various sources of data may be used to optimize or predict value for a loan. Using machine-learning and/or statistical analysis, loans or treatment best suited for a particular borrower may be determined. Due to the large amounts of data available, borrower behavior may be learned from previous behavior of others and mapped to a predictive model. Machine-learning indicates the most relevant factors in loan treatment, providing a matrix for predicting loan value or treatment success. A given borrower may be classified into one of many classes of borrower based on credit information, property information, desired loan information, real estate market information, and/or other data. Tens, hundreds, or even thousands of variables may be used to predict the optimum treatment.

Claims

1. A system for data analytics in loan treatment, the system comprising:

an input configured to receive credit report information for a person associated with a loan, property information for a specific property associated with the loan, loan information for the loan, and real estate market information for a region including the specific property, wherein the real estate market information includes a time on market for a region associated with the specific property, and wherein the property information includes a property value, equity, or open lien amount;

a processor configured to apply a model, the model comprising a machine-learned classifier configured to output a loan treatment recommendation for a borrower of the loan, the processor configured to apply the model as a function of the credit report information, the property information, the loan information, and the real estate market information; and

an output configured to output the loan treatment recommendation.

2. The system of claim 1, wherein options for the loan treatment recommendation comprise modification of the loan, foreclosure of the loan, short sale, or forbearance, the model configured to output one of the options as a function of the credit report information, the property information, the loan information, and the real estate market information.

3. The system of claim 1, wherein the credit report information, the property information, the loan information, and the real estate market information comprise metrics, and wherein at least 90% of the metrics used by the model are from the credit report information.

4. The system of claim 1, wherein the credit report information comprises metrics, and wherein the processor is configured to determine distress, capacity to pay, and willingness to pay factors from the metrics, the model using the distress, capacity to pay, and willingness to pay factors as input feature vectors.

5. The system of claim 1, wherein the model comprises a cluster classifier configured to classify the borrower as belonging to one of a plurality of clusters based on the credit report information and the property information.

6. The system of claim 5, wherein the plurality of clusters comprise at least ten clusters, wherein the model comprises at least two separate loan treatment classifiers for each of the clusters, the at least two separate loan treatment classifiers outputting a net present value of the property as a function of the loan information and the real estate information, the loan treatment recommendation output by the model comprising the loan treatment recommendation associated with the highest net present value of the property output by the at least two separate loan treatment classifiers for the cluster to which the borrower belongs.

7. The system of claim 1, wherein the input is configured to receive a value for a loan retention variable, the processor configured to apply the model as a function of the value of the loan retention variable, the loan treatment recommendation being a function of the value.

8. The system of claim 1, wherein the processor is configured to calculate a plurality of credit factors for each of a plurality of types of debt from the credit report information, the model applying the credit factors as input vectors.

9. The system of claim 1, further comprising a user selector, the user selector configured to indicate selection of one or more values of variables for the loan information, the loan treatment recommendation being a function of the one or more values such that a user interacts with the model to receive different outputs.

10. The system of claim 1, wherein the input comprises a network interface configured to receive the credit report information.

11. The system of claim 1, wherein the output comprises a display configured to output the loan treatment recommendation.

12. A non-transitory computer readable storage medium comprising instructions executable by a computer system that includes a data processor and is connected to at least one data repository, the instructions comprising:

accessing, by the computer system from the at least one data repository through a first communication channel, credit report information for a person associated with a loan, property information for a specific property associated with the loan, loan information for the loan, and real estate market information for a region including the specific property, wherein the real estate market information includes a time on market for a region associated with the specific property, and wherein the property information includes a property value, equity, or open lien amount;

applying, by the data processor of the computer system, a model, the model comprising a machine-learned classifier configured to output a loan treatment recommendation for a borrower of the loan, the data processor configured to apply the model as a function of the credit report information, the property information, the loan information, and the real estate market information; and

outputting, by the data processor of the computer system and through a second communication channel, the loan treatment recommendation.

13. The non-transitory computer readable storage medium of claim 12, wherein options for the loan treatment recommendation comprise modification of the loan, foreclosure of the loan, short sale, or forbearance, wherein applying the model comprises outputting one of the options as a function of the credit report information, the property information, the loan information, and the real estate market information.

14. The non-transitory computer readable storage medium of claim 12, wherein the credit report information, the property information, the loan information, and the real estate market information comprise metrics, wherein at least 90% of the metrics used by the model are from the credit report information.

15. The non-transitory computer readable storage medium of claim 12, wherein the credit report information comprises metrics, and wherein the instructions further comprise determining distress, capacity to pay, and willingness to pay factors from the metrics, the model using the distress, capacity to pay, and willingness to pay factors as input feature vectors.

16. The non-transitory computer readable storage medium of claim 12, wherein the applying comprises classifying, with a cluster classifier of the model, the borrower as belonging to one of a plurality of clusters based on the credit report information and the property information.

17. A computer-implemented method comprising:

accessing, by a computer system from at least one data repository through a first communication channel, credit report information for a person associated with a loan, property information for a specific property associated with the loan, loan information for the loan, and real estate market information for a region including the specific property, wherein the real estate market information includes a time on market for a region associated with the specific property, and wherein the property information includes a property value, equity, or open lien amount;

applying, by a data processor of the computer system, a model, the model comprising a machine-learned classifier configured to output a loan treatment recommendation for a borrower of the loan, the data processor configured to apply the model as a function of the credit report information, the property information, the loan information, and the real estate market information; and

outputting through a second communication channel the loan treatment recommendation.

18. The computer-implemented method of claim 17, wherein options for the loan treatment recommendation comprise modification of the loan, foreclosure of the loan, short sale, or forbearance, wherein applying the model comprises outputting one of the options as a function of the credit report information, the property information, the loan information, and the real estate market information.

19. The computer-implemented method of claim 17, wherein the credit report information, the property information, the loan information, and the real estate market information comprise metrics, wherein at least 90% of the metrics used by the model being from the credit report information.

20. The computer-implemented method of claim 17, wherein the credit report information comprises metrics, and wherein the computer-implemented method further comprises determining distress, capacity to pay, and willingness to pay factors from the metrics, the model using the distress, capacity to pay, and willingness to pay factors as input feature vectors.