System, method and computer program for underwriting and processing of loans using machine learning

Abstract

A system and method for processing loans includes a machine learning model associated with processing loans. The machine learning model may be configured based on an objective function associated with loan processing. One or more weights of the objective function may be updated to account for changes in one or more business conditions. The machine learning model may be configured based on the updates to the one or more weights.

What is claimed: 
     
         1 . A method comprising:
 causing a machine learning model to be configured based on modifying one or more weights applied to one or more variables of an objective function to account for changes in one or more business conditions;   determining, based on receiving an indication of a loan application, data associated with the loan application;   determining, based on providing the data associated with processing the loan application to the machine learning model, a loan decision associated with the loan application; and   sending an indication of the loan decision.   
     
     
         2 . The method of  claim 1 , further comprising determining, based on analysis of the machine learning model, a reason for the loan decision and sending an indication of the reason for the loan decision. 
     
     
         3 . The method of  claim 2 , wherein determining the reason for the loan decision comprises determining, based on a localized linearity process being applied to the machine learning model, one or more categories of reasons for rejection mapped to action notices. 
     
     
         4 . The method of  claim 1 , wherein the machine learning model comprises an ensemble machine learning model based on a plurality of machine learning models, and wherein causing the machine learning model to be configured comprises causing the plurality of machine learning models to be trained based on the objective function. 
     
     
         5 . The method of  claim 1 , wherein causing the machine learning model to be configured comprises:
 updating the objective function based on causing the one or more weights to be varied over time to account for the changes in the one or more business conditions, and   one or more of updating or generating the machine learning model based on the updated objective function.   
     
     
         6 . The method of  claim 1 , wherein the one or more variables of the objective function comprises one or more of: a first payment default recovered variable, a return on capital variable, a cost of customer acquisition variable, a cost of maintaining a customer variable, or a customer lifetime value variable. 
     
     
         7 . The method of  claim 1 , further comprising determining, based on a computational model for maximizing valuation, one or more business objectives and generating the objective function based on the one or more business objectives. 
     
     
         8 . The method of  claim 1 , wherein the data associated with the loan application comprises one or more of data provided by an applicant of the loan application or external data determined from one or more sources different than a source of the loan application. 
     
     
         9 . A method comprising:
 receiving data indicating one or more weights to apply to one or more variables of an objective function to account for changes in one or more business conditions;   configuring, based on updating the objective function using the data indicating the one or more weights apply to one or more variables of the objective function to account for the changes in the one or more business conditions, a machine learning model;   receiving data associated with a loan application;   determining, based on inputting the data associated with the loan application to the machine learning model, a loan decision associated with the loan application; and   sending an indication of the loan decision.   
     
     
         10 . The method of  claim 9 , further comprising determining, based on analysis of the machine learning model, a reason for the loan decision and sending an indication of the reason for the loan decision. 
     
     
         11 . The method of  claim 10 , wherein determining the reason for the loan decision comprises determining, based on applying a localized linearity process to the machine learning model, one or more categories of reasons for rejection mapped to action notices. 
     
     
         12 . The method of  claim 9 , wherein the machine learning model comprises an ensemble machine learning model based on a plurality of machine learning models, and wherein configuring the machine learning model comprises training the plurality of machine learning models based on the objective function. 
     
     
         13 . The method of  claim 9 , wherein configuring the machine learning model comprises:
 updating the objective function based on causing the one or more weights to be varied over time to account for the changes in the one or more business conditions, and   one or more of updating or generating the machine learning model based on the updated objective function.   
     
     
         14 . The method of  claim 9 , wherein the one or more variables of the objective function comprises one or more of: a first payment default recovered variable, a return on capital variable, a cost of customer acquisition variable, a cost of maintaining a customer variable, or a customer lifetime value variable. 
     
     
         15 . A method comprising:
 determining an objective function associated with loan processing and comprising one or more variables and one or more weights applied to corresponding variables of the one or more variables;   determining, based on the objective function, a machine learning model configured to provide loan application decisions;   receiving data indicating one or more changes to the one or more weights, wherein the data accounts for changes in one or more business conditions; and   configuring, based on updating the objective function using changes to the one or more weights, an updated machine learning model.   
     
     
         16 . The method of  claim 15 , further comprising determining, based on analysis of the machine learning model, a reason for a loan decision and sending an indication of the reason for the loan decision. 
     
     
         17 . The method of  claim 16 , wherein determining the reason for the loan decision comprises determining, based on applying a localized linearity process to the machine learning model, one or more categories of reasons for rejection mapped to action notices. 
     
     
         18 . The method of  claim 15 , wherein the machine learning model comprises an ensemble machine learning model based on a plurality of machine learning models, and wherein configuring the machine learning model comprises causing the plurality of machine learning models to be trained based on the objective function. 
     
     
         19 . The method of  claim 15 , wherein configuring the updated machine learning model comprises:
 updating the objective function based on causing the one or more weights to be varied over time to account for the changes in the one or more business conditions, and   one or more of updating the machine learning model based on the objective function or generating the updated machine learning model based on the updated objective function.   
     
     
         20 . The method of  claim 15 , wherein the one or more variables of the objective function comprises one or more of: a first payment default recovered variable, a return on capital variable, a cost of customer acquisition variable, a cost of maintaining a customer variable, or a customer lifetime value variable.