System and method of detecting and assessing multiple types of risks related to mortgage lending

• US 8,639,618 B2
• Filed: 07/02/2013
• Issued: 01/28/2014
• Est. Priority Date: 01/13/2010
• Status: Active Grant

• Current Assignee: Corelogic Solutions LLC (CoreLogic Incorporated)
• Original Assignee: Corelogic Solutions LLC (CoreLogic Incorporated)
• Inventors: Yan, Rui, Chi, Hoi-Ming, Koo, Seongjoon, Baker, James
• Primary Examiner(s): Shrestha, Bijendra K
• Application Number:
  US13/933,568
• Publication Number:
  US 20130332338A1
• Time in Patent Office: 210 Days
• Field of Search: 705/38
• US Class Current: 705/38
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 20/20 Ensemble learning
• CPC Class Codes: G06N 3/084 Backpropagation, e.g. using gradient descent
• CPC Class Codes: G06N 3/088 Non-supervised learning, e.g. competitive learning
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06N 5/048 Fuzzy inferencing
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/0185 Product, service or business identity fraud
• 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

Abstract

Embodiments include systems and methods of detecting and assessing multiple types of risks related to mortgage lending. One embodiment includes a system and method of detecting and assessing risks including fraud risks, early payment default risks, and risks related to fraudulently stated income on loan applications. One embodiment includes a computerized method that includes creating a combined risk detection model based on a plurality of risk detection models and using the combined risk detection model to evaluate loan application data and generate a combined risk score that takes into account interaction of different types of risks individually and collectively detected by the plurality of risk detection models.

Claims

1. A system for detecting and assessing lending risks, the system comprising:

a computer system comprising one or more computing devices, the computer system programmed, via executable code modules, to implement;

a combined risk detection model for detecting and assessing data indicative of a plurality of risks in loan application data, the combined risk detection model configured to receive as input a plurality of input features extracted from two or more of a plurality of risk detection models, the plurality of risk detection models comprising;

a first risk detection model configured to generate a first risk model score that is indicative of the presence of a first risk in the loan application data;

a second risk detection model configured to generate a second risk model score that is indicative of the presence of a second risk in the loan application data; and

a third risk detection model configured to generate a third risk model score that is indicative of the presence of a third risk in the loan application data, wherein the first risk, the second risk, and the third risk are different risks,

wherein the computer system is further programmed to;

determine combinability of the plurality of risk detection models based at least partly on evaluating predictive performance of scores from one or more combinations of the risk detection models against historical transactions data;

extract input features from the risk detection models that are determined to be combinable, the input features created from a mathematical combination of the scores and additional data, wherein the input features are extracted from the two or more risk detection models by mathematically combining scores from the plurality of risk detection models for input into the combined risk detection model, the input features being selected as based at least in part on a comparison of the predictive performance of the plurality of risk detection models and a selection of a modeling method used to construct the combined risk detection model; and

generate a composite risk score based at least in part on the scores from the models determined to be combinable and the extracted input features; and

a score reporting module that reports the composite risk score generated by the combined risk detection model.

2. The system of claim 1, wherein the input features are extracted from one or more of:

scores from the plurality of risk detection models or additional data related to the loan application not output by the plurality of risk detection models.

3. The system of claim 2, wherein the additional data related to the loan application not output by the plurality of risk detection models comprises a loan amount.

4. The system of claim 1, wherein the first risk detection model comprises a fraud model, and wherein the second risk detection model comprises a fraud model.

5. The system of claim 1, wherein the first risk detection model comprises a default risk model, and wherein the second risk detection model comprises a default risk model.

6. The system of claim 1, wherein the first risk detection model comprises a fraud model, wherein the second risk detection model comprises a multi-component risk model, and wherein the third risk detection model comprises a default risk model.

7. The system of claim 1, wherein a modeling method used to construct the combined risk detection model comprises one of:

linear regression, logical regression, neural networks, support vector machines, or decision trees generated using a machine learning algorithm that uses a tree-like graph to predict an outcome.

8. The system of claim 1, wherein the plurality of risk detection models are generated based at least in part on segmentation of the historical transactions data.

9. The system of claim 8, wherein the segmentation corresponds to geographic characteristics associated with the historical transactions data.

10. The system of claim 8, wherein the segmentation corresponds to a clustering analysis of the historical transactions data.

11. The system of claim 1, wherein the first risk detection model comprises an income fraud detection model that detects the presence of data indicative of fraud in income data within the loan application data, the income fraud detection model configured to generate an income fraud score indicative of fraud in the income data.

12. A computerized method of detecting and assessing mortgage lending risks, the method comprising:

receiving, on a physical computer processor, mortgage transaction data that includes loan application data and historical transactions data;

determining, on a physical computer processor, combinability of a plurality of risk detection models, the determining comprising determining predictive performance of scores from one or more combinations of the risk detection models as compared to the historical transactions data, wherein the plurality of risk detection models further comprise two or more of;

a first risk detection model configured to generate a first risk model score that is indicative of the presence of a first risk in the loan application data;

a second risk detection model configured to generate a second risk model score that is indicative of the presence of a second risk in the loan application data;

or

a third risk detection model configured to generate a third risk model score that is indicative of the presence of a third risk in the loan application data, wherein the first risk, the second risk, and the third risk are different risks;

extracting, on a physical computer processor, input features from the risk detection models that are determined to be combinable, the input features created from a mathematical combination of the scores and additional data, wherein the input features are extracted from the two or more risk detection models by mathematically combining scores from the plurality of risk detection models for input into a combined risk detection model, the input features being selected as based at least in part on a comparison of the predictive performance of the plurality of risk detection models and a selection of a modeling method used to construct the combined risk detection model;

applying, on a physical computer processor, the combined risk detection model to the mortgage transaction data to generate a composite risk score, wherein the combined risk detection model is configured to receive the extracted input features from the plurality of risk detection models determined to be combinable and mortgage transaction data not output by the risk detection models, the extracted input features further being selected as based at least in part on a modeling method used to construct the combined risk detection model; and

generating, on a physical computer processor, a report including the composite risk score generated by the combined risk detection model.

13. The method of claim 12, wherein the determining of the combinability of the plurality of risk detection models is based at least in part on the correlation of the results of applying the plurality of risk detection models to historical transaction data.

14. The method of claim 13, wherein the correlation is based at least in part on a measure of the similarity of the results among the plurality of risk detection models.

15. The method of claim 12, wherein the input features are selected by:

applying each of the plurality of risk detection models to data to generate a score for each risk detection model, the data comprising historical mortgage transaction data;

identifying an interaction among scores from the plurality of risk detection models; and

using the interaction as a basis for the selection of the input features.

16. The method of claim 12, wherein the input features are selected by:

applying each of the plurality of risk detection models to data to generate a score for each risk detection model, the data comprising historical mortgage transaction data;

performing a swap analysis on the scores from applying the plurality of risk detection models to the data; and

using the result of the swap analysis as a basis for the selection of the input features.

17. The method of claim 12, wherein the first risk detection model comprises a fraud model, and wherein the second risk detection model comprises a fraud model.

18. The method of claim 12, wherein the first risk detection model comprises a default risk model, and wherein the second risk detection model comprises a default risk model.

19. The method of claim 12, wherein the plurality of risk detection models are generated based at least in part on segmentation of the historical transactions data.