Artificial intelligence fraud management solution

Abstract

An artificial intelligence fraud management solution comprises an expert programmer development system to build trainable general payment fraud models that integrate several artificial intelligence classifiers like neural networks, case based reasoning, decision trees, genetic algorithms, fuzzy logic, and rules and constraints. These are further integrated by the expert programmers and development system with smart agents and associated real-time profiling, recursive profiles, and long-term profiles. The trainable general payment fraud models are trained with supervised and unsupervised data to produce an applied payment fraud model. This then is applied by a commercial client to process real-time transactions and authorization requests for fraud scores.

1 . An automation tool for the application development of multi-discipline artificial intelligence fraud management systems, comprising:
 an expert programmer development system with tools and software libraries for building trainable general payment fraud models that are constructed of and integrate several artificial intelligence classifiers, including smart agents, neural networks, case-based reasoning, decision trees, and business rules;   wherein, said trainable general payment fraud models are capable of being trained with historical transaction data that causes:
 an initial population of smart agents and associated profiles to be generated, 
 an initial set of neural networks to assume a beginning weight matrix, 
 an initial decision tree to be structured from data mining logic, 
 an initial case-based reasoning set to be structured, and 
 an initial set of business rules to be fixed; 
   wherein, said trainable general payment fraud models are detachable once built;   
     
     
         2 . The automation tool of  claim 1 , wherein said trainable general payment fraud models further comprise:
 an incremental learning technology embedded in a run-time machine algorithm and smart-agent technology able to continually re-train said artificial intelligence classifiers using false positives and negatives that occur during use.   
     
     
         3 . The automation tool of  claim 2 , wherein said incremental learning technology further comprises:
 data mining logic for incrementally changing the initial decision trees by creating new links or updating its existing links and weights.   
     
     
         4 . The automation tool of  claim 2 , wherein said incremental learning technology further comprises:
 means for the initial neural networks to have their weight matrix updated.   
     
     
         5 . The automation tool of  claim 2 , wherein said incremental learning technology further comprises:
 means for the initial case-based reasoning logic to update its generic cases or create new ones.   
     
     
         6 . The automation tool of  claim 2 , wherein said incremental learning technology further comprises:
 means for the initial population of smart-agents to self-update their profiles and to adjust their normal/abnormal thresholds or by creating exceptions.