System and method for enhancing credit and debt collection

Abstract

A system and method for enhancing assignment of debtor accounts to a plurality of collection parties is presented. The preferred embodiment is capable of optimizing the way by which individual performance entities are assigned to collect on actionable individual debtor accounts by a creditor. An analysis solution uses algorithms to analyze gathered data and to provide a score to each collection party based upon the traits of the individual collection parties, debtor accounts, creditor, externally acquired data, and constraints upon all of the parties involved. The system and method are also capable of enhancing an individual borrower's credit score depending on the risk involved with providing credit to that particular borrower based upon the collectability upon default. One embodiment of the invention would include a risk analysis and compliance assessment system for supply entities to evaluate potential performance entities or other entities.

1 . One or more non-transitory computer-readable media storing computer-executable instructions that, when executed by at least one processor, perform a method of prioritizing debt collection, the method comprising:
 obtaining action entities comprising a plurality of debts to be collected;   determining performance quantifiers associated with a plurality of collection entities;   allocating, by a machine learning algorithm, at least one debt of the plurality of debts to at least one collection entity of the plurality of collection entities based on the action entities and the performance quantifiers;   obtaining historical data indicative of a history of the at least one collection entity;   based on the historical data for the at least one collection entity and at least one risk rule, generating a risk score for the at least one collection entity, the risk score representing a likelihood of compliance violations;   if the risk score for the at least one collection entity exceeds a predetermined threshold, generating a reallocation data file for reallocating the at least one debt based on at least one intervention rule for reducing the risk score; and   reallocating the at least one debt based on the reallocation data file.   
     
     
         2 . The media of  claim 1 , wherein the action entities are a plurality of debtor accounts. 
     
     
         3 . The media of  claim 1 , wherein the machine learning algorithm is one of a neural network, cluster analysis, regression analysis, Bayesian analysis, and at least one decision tree. 
     
     
         4 . The media of  claim 1 , wherein the method further comprises:
 performing a confirmation check with the at least one collection entity; and   reallocating the at least one debt if the confirmation check is not completed by the at least one collection entity.   
     
     
         5 . The media of  claim 4 , wherein the confirmation check is an automated response. 
     
     
         6 . The media of  claim 1 , wherein the method further comprises determining, by the machine learning algorithm, an account type classifier for each of the action entities. 
     
     
         7 . The media of  claim 6 , wherein the allocating is further based on the account type classifier. 
     
     
         8 . A computerized method of prioritizing debt collection, the computerized method comprising:
 obtaining action entities comprising a plurality of debts to be collected;   determining action quantifiers associated with the action entities;   determining performance quantifiers associated with a plurality of collection entities;   allocating, by a machine learning algorithm, at least one debt of the plurality of debts to at least one collection entity of the plurality of collection entities based on the action entities, the action quantifiers, and the performance quantifiers;   obtaining historical data indicative of a history of the at least one collection entity;   based on the historical data for the at least one collection entity and at least one risk rule, generating a risk score for the at least one collection entity, the risk score representing a risk likelihood of compliance violations;   if the risk score for the at least one collection entity exceeds a predetermined threshold, generating a reallocation data file for reallocating the at least one debt based on at least one intervention rule for reducing the risk score; and   reallocating the at least one debt based on the reallocation data file.   
     
     
         9 . The computerized method of  claim 8 , wherein the performance quantifiers comprise at least a location and a past performance of the plurality of collection entities. 
     
     
         10 . The computerized method of  claim 8 , wherein the action quantifiers comprise a debt amount, a location, a past performance, and a type of debt for the action entities. 
     
     
         11 . The computerized method of  claim 8 , wherein the machine learning algorithm comprises at least one of regression analysis, classification analysis, cluster analysis, Bayesian analysis or a decision tree. 
     
     
         12 . The computerized method of  claim 11 , further comprising determining a likelihood of collecting the at least one debt based on the action entities, the action quantifiers, the performance quantifiers, and the at least one collection entity. 
     
     
         13 . The computerized method of  claim 8 , further comprising performing an automated confirmation check with the at least one collection entity. 
     
     
         14 . The computerized method of  claim 13 , further comprising reallocating the at least one debt if the automated confirmation check is not completed by the at least one collection entity within a period. 
     
     
         15 . A system for prioritizing debt collection, the system comprising:
 a data store storing data associated with one or more debtor accounts comprising one or more debts to be collected;
 at least one processor; and 
   one or more non-transitory computer-readable media storing computer-executable instructions that, when executed by the at least one processor, perform a method of:
 obtaining action entities comprising a plurality of debts to be collected; 
 determining performance quantifiers associated with a plurality of collection entities; 
 determining, by a machine learning algorithm, a set of likelihoods of collecting each debt of the plurality of debts based on the action entities and the performance quantifiers; 
 allocating at least one debt of the plurality of debts to at least one collection entity of the plurality of collection entities based on the set of likelihoods, the action entities, and the performance quantifiers; 
 obtaining historical data indicative of a history of the at least one collection entity; 
 based on the historical data for the at least one collection entity and at least one risk rule, generating a risk score for the at least one collection entity, the risk score representing a likelihood of compliance violations; 
 if the risk score for the at least one collection entity exceeds a predetermined threshold, generating a reallocation data file for reallocating the at least one debt based on at least one intervention rule for reducing the risk score; and 
 reallocating the at least one debt based on the reallocation data file. 
   
     
     
         16 . The system of  claim 15 , wherein the method further comprises determining a likelihood of collecting the at least one debt based on the action entities, the action quantifiers, the performance quantifiers, and the at least one collection entity. 
     
     
         17 . The system of  claim 15 , wherein the method further comprises:
 determining action quantifiers associated with the action entities; and   determining the likelihood of collecting the at least one debt further based on the action quantifiers.   
     
     
         18 . The system of  claim 17 , wherein allocation is further based on the action quantifiers. 
     
     
         19 . The system of  claim 15 , wherein the method further comprises:
 performing an automated confirmation check with the at least one collection entity; and   reallocating the at least one debt if the automated confirmation check is not completed by the at least one collection entity.   
     
     
         20 . The system of  claim 19 , wherein the machine learning algorithm comprises at least one of regression analysis, classification analysis, cluster analysis, Bayesian analysis or a decision tree.