DATA ANALYTICS MODELS FOR LOAN TREATMENT
First Claim
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.
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Accused Products
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.
227 Citations
33 Claims
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1. A system for data analytics in loan treatment, the system comprising:
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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. - View Dependent Claims (2)
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3. A system for data analytics in loan treatment, the system comprising:
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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, the real estate market information including a time on market for a region associated with the specific property, the property information including a property value, equity, and open lean amount; a processor configured to extract metrics including a willingness, a capacity and a stress from the credit information of a borrower of the loan, and configured to apply a model, the model comprising a machine-learned classifier configured to output a loan treatment recommendation for the borrower of the loan, the processor configured to apply the model as a function of the willingness, the capacity, the stress, the time on market, the property value, the equity, the open lean amount, a combined loan-to-value ratio, the loan information, and the real estate market information; and a display configured to output the loan treatment recommendation. - View Dependent Claims (4)
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5. A system for data analytics in loan treatment, the system comprising:
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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; 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 a display configured to output the loan treatment recommendation. - View Dependent Claims (6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. 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:
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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. - View Dependent Claims (16, 17, 18, 19, 20)
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21. A method for data analytics in loan processing, the method comprising:
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generating, with a computer, a user interface; receiving, with a user input of the computer, an indication of a property value, a loan term, a loan modification, or combinations thereof, the indication received as part of the user interface; calculating, with the computer, a net present value as a function of a machine-trained model, the received indication being input as a feature of the machine-trained model; repeating the receiving and calculating for different indications for a same loan of a same property; displaying on a display the net present value for each of the different indications. - View Dependent Claims (22, 23, 24)
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25. 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:
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applying a machine trained model to borrower and property data for a distressed loan; calculating net present value as a function of an output of the application of the machine trained model, the net present value calculated for each of a plurality of loan treatments; and outputting the net present value of at least one of the loan treatments. - View Dependent Claims (26, 27, 28, 29)
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30. A method for data analytics in loan processing, the method comprising:
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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. - View Dependent Claims (31, 32)
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33. 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:
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determining at least twenty borrower metrics from credit report information for first and second borrowers, the borrower metrics including distress, capacity, and willingness metrics; determining at least eight property metrics about a property associated with first and second loans for the first and second borrowers, respectively, the property metrics including property value, equity, and combined loan-to-value ratio metrics; classifying the first borrower into a first cluster of a plurality of clusters based on the at least twenty borrower metrics and the at least eight property metrics for the first borrower, the metrics for the classifying including the distress, capacity, willingness, property value, equity, and combined loan-to-value ratio metrics for the first borrower; classifying the second borrower into a second cluster of a plurality of clusters based on the at least twenty borrower metrics and the at least eight property metrics for the second borrower, the metrics for the classifying including the distress, capacity, willingness, property value, equity, and combined loan-to-value ratio metrics for the second borrower; wherein the first cluster corresponds to a greater default rate than the second cluster with the equity of the first borrower being higher than the equity of the second borrower; and recommending foreclosure for the first borrower and a different loan treatment for the second borrower based on the classification into the first and second clusters, respectively.
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Specification