SYSTEMS AND METHOD FOR GENERATING A PROXY-SCORING MODEL

1 . A computer-implemented method to generate a proxy score/projection of a business model based on a plurality of statistically significant variables, the method comprising:

receiving a first set of standard model projection generated based on a first data set of said statistically significant variables, wherein said first set of standard model projection is generated during a model development phase;

generating a second set of standard model projection based on a second data set of said statistically significant variables, wherein said second set of standard model projection is generated during a post-implementation phase and wherein said second data set of statistically significant variables is an incomplete set;

categorizing said plurality of independent variables into a plurality of groups based on a data source of said independent variables,

wherein said plurality of groups comprises at least one complete group and at least one incomplete group such that data set of said at least one incomplete group is incomplete/missing post the business model implementation;

generating a base model for each of said plurality of groups based on at least on said first data set and second data set, to generate a base model projection set;

generating a multi-data model based on a data set for said at least one complete group, to generate a multi-data model projection set,

wherein generating multi data model includes at least one new statistically significant variable not yet utilized in base model to generate said multi-data model projection set to enhance projection power due to incomplete group; and

generating a proxy scoring model based on said base model and said multi-data model, to generate a proxy score model projection set,

wherein said proxy score model projection set is based on said base model projection set and said multi-data model projection set.

2 . The computer-implemented method of claim 1 wherein generating a base model for each of said plurality of groups may be based on said first data set and a reject inference set.

3 . The computer-implemented method of claim 1 wherein said standard model, said base model and said multi-data model may be a binary logistic model or any other additive multiple linear regression model.

4 . The computer-implemented method of claim 1 further comprising maintaining a central database, wherein said central database stores one or more of said standard model projection set, said base model projection set, said multi-data model projection set and said proxy score model projection set.

5 . A system for generating a proxy score/projection of a business model based on a plurality of statistically significant variables, the system comprising:

a memory comprising one or more program instruction modules, the one or more program instruction modules comprising

a standard model generator module for generating a first set of standard model projection based on a first data set of said statistically significant variables, wherein said first set of standard model projection is generated during a model development phase, and

wherein said standard model generator module is further configured to generate a second set of standard model projection based on a second data set of said statistically significant variables, wherein said second set of standard model projection is generated during a post-implementation phase and wherein said second data set of statistically significant variables is an incomplete set;

a base model generator module for categorizing said plurality of statistically significant variables into a plurality of groups based on a data source of said independent variables,

wherein said plurality of groups comprises at least one complete group and at least one incomplete group such that data set of said at least one incomplete group is incomplete/missing post the business model implementation, and

wherein said base model generator is further configured to generate a base model for each of said plurality of groups based at least on said first data set and second data set, to generate a base model projection set;

a multi-data model generator module for generating a multi-data model based on a data set for said at least one complete group, to generate a multi-data model projection set,

wherein generating multi data model includes at least one new statistically significant variable not yet utilized in base model to generate said multi-data model projection set to enhance projection power for incomplete group;

a proxy score model generator module for generating a proxy scoring model based on said base model and said multi-data model, to generate a proxy score model projection set,

wherein said proxy score model projection set is based on said base model projection set and said multi-data model projection set; and

a processor operable to execute the one or more program instruction modules.

6 . The system of claim 5 wherein said base model generator module may be configured to generate a base model for each of said plurality of groups based on said first data set and a reject inference set.

7 . The system of claim 5 wherein said standard model, said base model and said multi-data model may be one of a binary logistic model and an additive regression model.

8 . The system of claim 5 wherein said business model may be one of a credit scoring model, an acquisition model, a behaviour model, a collection scorecard model, a fraud scorecard model, a response model.

9 . The system of claim 5 further comprising a central database for storing one or more of said standard model projection set, said base model projection set, said multi-data model projection set and said proxy score model projection set.

10 . A non-transitory computer-readable storage medium storing one or more sequences of instructions, the instructions, when executed by one or more processors, cause the one or more processors to perform steps comprising:

receiving a first set of standard model projection generated based on a first data set of said statistically significant variables, wherein said first set of standard model projection is generated during a model development phase;

generating a second set of standard model projection based on a second data set of said statistically significant variables, wherein said second set of standard model projection is generated during a post-implementation phase and wherein said second data set of statistically significant variables is an incomplete set;

categorizing said plurality of independent variables into a plurality of groups based on a data source of said independent variables,

wherein said plurality of groups comprises at least one complete group and at least one incomplete group such that data set of said at least one incomplete group is incomplete/missing post the business model implementation;

generating a base model for each of said plurality of groups based on at least on said first data set and second data set, to generate a base model projection set;

generating a multi-data model based on a data set for said at least one complete group, to generate a multi-data model projection set,

wherein generating multi data model includes at least one new statistically significant variable not yet utilized in base model to generate said multi-data model projection set to enhance projection power for incomplete group; and

generating a proxy scoring model based on said base model and said multi-data model, to generate a proxy score model projection set,

wherein said proxy score model projection set is based on said base model projection set and said multi-data model projection set.