Data analytics models for loan treatment
View Patent ↗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.
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 property; and
a processor configured to apply:
a cluster model comprising an unsupervised machine-learned classifier configured to classifying a borrower of the loan and the specific property into one of a plurality of borrower-property clusters, each of the borrower-property clusters being a function of both the credit report information and the property information;
a treatment model comprising a supervised machine-learned classifier configured to output a loan treatment recommendation for the borrower of the loan, the processor configured to apply the treatment model as a function of the one of the borrower-property clusters, the property information, the loan information, and the real estate market information, the loan treatment recommendation selected from a plurality of possible treatments as one of the possible treatments with a greater value over a period of time, the possible treatments including loan modification and short sale.
2. The system of claim 1 further comprising a display configured to output the loan treatment recommendation.
3. In a computer readable storage medium having stored therein data representing instructions executable by a programmed processor for data analytics in loan processing, the instructions comprising:
modeling borrower loan behavior from credit values for a borrower;
modeling property behavior from property characteristics for a property associated with the borrower;
classifying the borrower into one of a plurality of clusters based on the modeled borrower loan behavior and the modeled property behavior; and
outputting information based on the one cluster to which the borrower is classified.
4. The computer readable storage medium of claim 3 wherein modeling borrower loan behavior comprises calculating a distress factor, a capacity factor, and a willingness to pay factor from the credit values, the distress, capacity, and willingness factors each comprising one or more metrics, the metrics input as a feature vector for the classifying.
5. The computer readable storage medium of claim 4 wherein the distress factor comprises an indication of debt servicing distress of the borrower and is calculated as a function of trade lines for revolving credit, auto credit, finance company credit, or combinations thereof.
6. The computer readable storage medium of claim 3 wherein modeling the property behavior comprises calculating a property value factor, a combined loan to Value factor, and an appreciation factor from the property characteristics and real estate market information, the property value, combined loan to value, and appreciation factors each comprising one or more metrics, the metrics input as a feature vector for the classifying.
7. The computer readable storage medium of claim 3 further comprising:
determining a percent chance of default as a function of the one cluster.
8. The computer readable storage medium of claim 3 further comprising:
outputting a loan treatment recommendation as a function of the classification of the borrower into the one cluster, the loan treatment recommendation comprising one of loan modification, short sale, and foreclosure, the loan modification output where the borrower loan behavior is modeled as able and willing to execute, the short sale output where the property behavior is modeled as able to support short sale, and otherwise outputting foreclosure.
9. A method for data analytics in loan processing, the method comprising:
receiving, with a computer, a portfolio of loans;
extracting, with the computer, borrower and property information for the loans of the portfolio;
applying, with the computer, a machined-trained model estimating survivability, for each of the loans of the portfolio, of a plurality of loan treatments;
selecting, with the computer, loan treatments for the loans as a function of the survivability;
calculating, with the computer, a cost for the portfolio as a function of the selected loan treatments; and
outputting the cost and the selected loan treatments.
10. The method of claim 9 wherein calculating the cost comprises calculating a net present value for the selected loan treatments.
11. The method of claim 9 wherein selecting comprises selecting as a function of a number or percentage of one of the loan treatments available for the portfolio.