Methods and systems of predicting mortgage payment risk
View Patent ↗A method for detecting a risk of payment default comprises receiving mortgage data associated with a mortgage application, the mortgage application associated with an applicant, determining a first score for the mortgage data based at least partly on one or more models that are based on data from a plurality of historical mortgage transactions and based on historical credit information related to the applicant, and generating data indicative of a risk of payment default based at least partly on the first score.
1. A method for detecting a risk of payment defaults default, comprising:
receiving mortgage data associated with a mortgage application associated with an applicant, the mortgage data including credit information related to the applicant;
determining a score for the mortgage data based at least partly on:
a first model generated based at least partly on data related to a plurality of historical mortgage transactions in which a payment default outcome is known; and
a second model based at least partly on data related to a plurality of historical mortgage transactions associated with a plurality of entities, the second model including at least one cluster associated with historical mortgage transactions associated with at least one of the plurality of entities; and
generating data indicative of a risk of payment default based at least partly on the score,
wherein the method is performed in its entirety by a computing system that comprises one or more computing devices.
2. The method of claim 1 , wherein the mortgage data comprises at least one of mortgage application data, funded mortgage data, or bid tapes.
3. The method of claim 1 , wherein the credit information comprises information related to at least one of payment history, credit scores, employment, tenure, income, and debt.
4. The method of claim 2 , wherein the mortgage application data comprises property valuation information and geographic information.
5. The method of claim 1 , wherein the first model is based additionally on geographic default risk information.
6. The method of claim 1 , wherein the first model comprises at least one of neural network, logistic regression, linear regression, decision trees, a classification and regression tree (CART) model, a fuzzy logic technique, a support vector machine (SVM) of one or more classes, a Naïve Bayes technique, a boosting tree, a scorecard or an expert system.
7. The method of claim 1 , wherein the first model is configured to generate at least one risk indicator of payment default.
8. The method of claim 1 , wherein the entities include at least one of an account executive, a broker, a loan officer, or an appraiser.
9. The method of claim 7 , wherein generating data indicative of a risk of payment default comprises generating at least one risk indicator associated with an entity associated with the mortgage application.
10. The method of claim 7 , wherein the at least one risk indicator is related to at least one of a borrower's risk, a borrower's affordability risk, a property valuation risk, a geographic risk, or a combination thereof.
11. A system for detecting a risk of payment default, comprising:
storage configured to receive mortgage data associated with a mortgage application associated with an applicant, the mortgage data including credit information related to the applicant; and
a processor coupled with the storage, the processor configured to:
determine a score for the mortgage data based at least partly on:
a first model generated based at least partly on data related to a plurality of historical mortgage transactions in which a payment default outcome is known; and
a second model based at least partly on data related to a plurality of historical mortgage transactions associated with a plurality of entities, the second model including at least one cluster associated with historical mortgage transactions associated with at least one of the plurality of entities; and
generate data indicative of a risk of payment default based at least partly on the score.
12. The system of claim 11 , wherein the mortgage data comprises at least one of mortgage application data, funded mortgage data, or bid tapes.
13. The system of claim 11 , wherein the credit information comprises information related to at least one of payment history, credit scores, employment, tenure, income, and debt.
14. The system of claim 12 , wherein the mortgage application data comprises property valuation information and geographic information.
15. The system of claim 11 , wherein the first model is based additionally on geographic default risk information.
16. The system of claim 11 , wherein the first model comprises at least one of neural network, logistic regression, linear regression, decision trees, a classification and regression tree (CART) model, a fuzzy logic technique, a support vector machine (SVM) of one or more classes, a Naïve Bayes technique, a boosting tree, a scorecard or an expert system.
17. The system of claim 16 , wherein the first model is configured to generate at least one risk indicator of payment default.
18. The system of claim 11 , wherein the entities include at least one of an account executive, a broker, a loan officer, or an appraiser.
19. The system of claim 17 , wherein generating data indicative of a risk of early payment default comprises generating at least one risk indicator associated with an entity associated with the mortgage application.
20. The system of claim 17 , wherein the at least one risk indication is related to at least one of a borrower's risk, a borrower's affordability risk, a property valuation risk, a geographic risk, or a combination thereof.