VOTING MECHANISM AND MULTI-MODEL FEATURE SELECTION TO AID FOR LOAN RISK PREDICTION
Presented are a system, method, and apparatus for loan risk prediction. A computing device receives a plurality of loan account histories containing variables x; a plurality of algorithms then independently selects features from the loan account histories, the selected features being functions of the received variables x; the selected features are then grouped into a first data structure x f ; the computing device applies voting algorithm(s) to the selected features to create a second data structure x r ; the computing device generates a third data structure x I of interaction terms from the second data structure x r ; a fourth data structure is generated, x NL , where x NL =x r ∪x I or x∪x I ; a model executes that selects significant features from the fourth data structure x NL ; and a nonlinear model y=f(X NLR ) is generated, the nonlinear model y indicating risk associated with the plurality of loan account histories.
1 . A method for loan risk prediction comprising:
Receiving by a computing device a plurality of loan account histories X containing variables x transmitted from a database;
Utilizing by said computing device a plurality of algorithms to independently select features from said plurality of loan account histories, the selected features being functions of the received variables x;
Grouping said selected features selected from said plurality of loan account histories into a first data structure x f ;
Applying by said computing device a voting algorithm or voting algorithms to said selected features selected from said plurality of loan account histories and grouping results into a second data structure x r ; and
Generating by the computing device a third data structure x, of interaction terms from the second data structure x r .
2 . The method of claim 1 further comprising after generating by the computing device the third data structure x I , then generating by the computing device a fourth data structure x NL wherein x NL equals selectively one of x r ∪x I and x∪x I .
3 . The method of claim 2 further comprising after generating by the computing device the fourth data structure x NL then executing a model that selects significant features from the fourth data structure x NL to form a fifth data structure x NLR .
4 . The method of claim 3 wherein the fourth data structure x NL , is used to form a data structure X NL by selecting elements of X whose indices are in the fourth data structure x NL .
5 . The method of claim 3 wherein the fifth data structure X NLR , is used to form a data structure X NLR by selecting elements of X whose indices are in x NLR .
6 . The method of claim 5 further comprising generating a nonlinear model y=f(X NLR ), where f is a nonlinear function, the nonlinear model y indicating risk associated with each of said received plurality of loan account histories on a periodic basis for a time period into the future.
7 . The method of claim 1 wherein the second data structure x r is used by the computing device to form a data structure X r said data structure X r used to generate a linear model, the linear model indicating risk associated with each of said received plurality of loan account histories on a periodic basis for a time period into the future.
8 . The method of claim 7 wherein the linear model is defined by an equation, z=g(X r ).
9 . The method of claim 7 wherein the data structure X r is formed by selecting elements of X whose indices are in x r .
10 . The method of claim 1 wherein the voting algorithm or voting algorithms applied to said selected features selected from said plurality of loan account histories to create a second data structure x r perform the further steps of selectively one or more of the following a.-c.:
a. Selecting variables that appear at least r times in the first data structure x f ;
b. Selecting variables that appear r times pairwise; and
c. Selecting variables that appear r times in models that have a certain average accuracy.
11 . The method of claim 6 further comprising after generating the nonlinear model y, then using M algorithms to independently confirm features in the generated nonlinear model y.
12 . The method of claim 1 wherein said plurality of algorithms selects features from said plurality of loan account histories by operating in parallel.
13 . The method of claim 1 wherein said plurality of algorithms selects features from said plurality of loan account histories by operating sequentially.
14 . The method of claim 1 wherein said plurality of algorithm(s) comprise selectively two or more of the following: an Elastic Net Algorithm, a LASSO Algorithm, a Stepwise Regression with the MC Penalty Algorithm, and a Multivariate Adaptive Regression Splines Algorithm.
15 . The method of claim 6 wherein the generated nonlinear model y is stored in a non-transitory computer-readable storage medium for future use with test data.
16 . The method of claim 6 wherein the time period into the future is selectively one of: one week, one month, two months, six months, and one year.
17 . The method of claim 11 wherein said M algorithm(s) comprises selectively one or more of the following: an Elastic Net algorithm, a LASSO Algorithm, a Stepwise Regression with the RIC Penalty Algorithm, and a Multivariate Adaptive Regression Splines Algorithm.
18 . The method of claim 1 wherein the third data structure x I of interaction terms comprises sets of two elements and sets of three elements.
19 . A system for loan risk prediction comprising:
A computing device performing the steps of:
Receiving a plurality of loan account histories X containing variables x transmitted from a database;
Utilizing a plurality of algorithms to independently select features from said plurality of loan account histories, the selected features being functions of the received variables x;
Grouping said selected features selected from said plurality of loan account histories into a first data structure x f ;
Applying a voting algorithm or voting algorithms to said selected features selected from said plurality of loan account histories and grouping results into a second data structure x r ; and
Generating by the computing device a third data structure x I of interaction terms from the second data structure x r .
20 . The system of claim 19 further comprising after generating by the computing device the third data structure x I , then generating by the computing device a fourth data structure x NL wherein x NL equals selectively one of x r ∪x I and x∪x I .
21 . The system of claim 20 further comprising after generating by the computing device the fourth data structure x NL , then executing a model that selects significant features from the fourth data structure x NL to form a fifth data structure x NLR .
22 . The system of claim 20 wherein the fourth data structure x NL is used to form a data structure X NL by selecting elements of X whose indices are in the fourth data structure X NL .
23 . The system of claim 21 wherein the fifth data structure x NLR is used to form a data structure X NLR by selecting elements of X whose indices are in x NLR .
24 . The system of claim 23 further comprising generating a nonlinear model y=f(X NLR ), where f is a nonlinear function, the nonlinear model y indicating risk associated with each of said received plurality of loan account histories on a periodic basis for a time period into the future.
25 . The system of claim 19 wherein the second data structure x r is used to form a data structure X r , said data structure X r used to generate a linear model, the linear model indicating risk associated with each of said received plurality of loan account histories on a periodic basis for a time period into the future.
26 . The system of claim 25 wherein the data structure X r is composed by selecting elements of X whose indices are in x r .
27 . The system of claim 25 wherein the linear model is defined by an equation, z=g(X r ).
28 . The system of claim 19 wherein the voting algorithm or voting algorithms applied to said selected features selected from said plurality of loan account histories to create a second data structure x r perform the further steps of selectively one or more of the following a.-c.:
a. Selecting variables that appear at least r times in the first data structure x f ;
b. Selecting variables that appear r times pairwise; and
c. Selecting variables that appear r times in models that have a certain average accuracy.
29 . The system of claim 24 further comprising after generating the nonlinear model y, then using M algorithms to independently confirm features in the generated nonlinear model y.
30 . The system of claim 19 wherein said plurality of algorithms selects features from said plurality of loan account histories by operating in parallel.
31 . The system of claim 19 wherein said plurality of algorithms selects features from said plurality of loan account histories by operating sequentially.
32 . The system of claim 19 wherein said plurality of algorithms comprises selectively two or more of the following: an Elastic Net Algorithm, a LASSO Algorithm, a Stepwise Regression with the RIC Penalty Algorithm, and a Multivariate Adaptive Regression Splines Algorithm.
33 . A method for loan risk prediction comprising:
Receiving by a computing device a plurality of loan account histories X containing variables x transmitted from a database;
Utilizing by said computing device a plurality of algorithms to independently select features from said plurality of loan account histories, the selected features being functions of the received variables x;
Grouping said selected features selected from said plurality of loan account histories into a first data structure x f ;
Applying by said computing device a voting algorithm or voting algorithms to said selected features selected from said plurality of loan account histories and grouping results into a second data structure x r ;
Generating by the computing device a third data structure x I of interaction terms from the second data structure x r ;
Generating by the computing device a fourth data structure x NL wherein x NL equals selectively one of x r ∪x I and x∪x I ;
Generating by the computing device a data structure X NL wherein X NL is formed by selecting the elements in the columns of X whose features are also in the fourth data structure x NL ;
Executing a model that selects significant features from the fourth data structure x NL ; and
Generating a nonlinear model y=f(X NLR ) where f is a nonlinear function, the nonlinear model y indicating risk associated with each of the received plurality of loan account histories on a monthly basis for a time period into the future.