SYSTEMS AND METHODS FOR PREDICTIVE MODELING IN MAKING STRUCTURED REFERENCE CREDIT DECISIONS
A structured reference credit decision device includes a database configured to store information related to applicants, potential customers, referencers, potential referencers, lenders, and other third parties, a fetch data component coupled with the database, the fetch data component configured to receive input application information, fetch relevant information from the database, based on the application information, related to a subject applicant of the input application information and at least one referencer, and generate a plurality of linked data packages based on the fetched information, and an evaluation device coupled with the fetch data component, the evaluation engine configure to apply credit outcome models to the plurality of linked data packages and generate a recommendation relative to the subject applicant or application.
1 . A method of generating a prediction of an outcome comprising:
In a processing system,
Collecting data related to a prospect requesting an action and storing the data in a database;
using the processing system to identify relationships related to the prospect;
using a network graph engine to generate a network graph where the nodes of the graph represent the relationships and the edges of the graph represent the strength of those relationships;
using a prediction engine to apply a predictive modeling step to the graph to determine a predicted outcome related to the requested action, the predictive modeling step comprising generating a plurality of matrices, including at least one predictor linked data matrix and at least one singular vector matrix.
2 . The method of claim 1 wherein the action is a loan.
3 . The method of claim 2 wherein the relationships are references.
4 . The method of claim 3 wherein the references are forward references.
5 . The method of claim 4 wherein the references are backward references.
6 . The method of claim 3 wherein the network graph is generated by recursion.
7 . The method of claim 6 wherein a set of predictor variables are defined for the network graph.
8 . The method of claim 7 wherein only the predictor variables are used in the predictive modeling step.
9 . The method of claim 8 wherein a predictive model is trained on linked data of the network graph.
10 . The method of claim 9 wherein the predicted outcome is provided to an evaluation engine to determine if the requested action will be approved.