Method and System for Using a Bayesian Belief Network to Ensure Data Integrity

Abstract

The present invention relates to a method and system for assessing the risks and/or exposures associated with financial transactions using various statistical and probabilistic techniques. Specifically, the present invention relates to a method and system for identifying plausible sources of error in data used as input to financial risk assessment systems using Bayesian belief networks as a normative diagnostic tool to model relationships between and among inputs/outputs of the risk assessment system and other external factors.

1 .- 18 . (canceled) 
     
     
         19 . A computerized system for identifying minimizing sources of error in a risk assessment system (RAS), comprising:
 a computer program executed by a server, the computer program comprising:
 an application program interface (API) receiving a plurality of variables of the RAS and an initial probability for each of the variables and implementing a Bayesian network to represent implications between and among the plurality of variables; 
 a first module accessing the API to retrieve beliefs based on the implications between and among the plurality of variables; 
 a second module receiving the beliefs from the first module and interpreting the beliefs; 
 a third module receiving prospects based on the interpretation of the beliefs from the second module and converting the prospects to factoids based on additional data received; and 
 a fourth module receiving the factoids from the third module and weighing the factoids to evaluate the initial probability for each of the variables. 
   
     
     
         20 . The computerized system of  claim 19 , further comprising:
 a data extracting module extracting the additional data used by the third module for converting the prospects to factoids.   
     
     
         21 . The computerized system to  claim 20 , wherein the data extracting module receives extracted evidence from a hypothesizer, searches the RAS for raw biases and fact data of observable variables, and converts the evidence to factoids. 
     
     
         22 . The computerized system of  claim 19 , wherein the plurality of variables comprises input data of the RAS. 
     
     
         23 . The computerized system of  claim 19 , wherein the plurality of variables comprises information implicated from input data of the RAS. 
     
     
         24 . The computerized system of  claim 19 , wherein evaluating the initial probability for each of the variables comprises:
 setting each of the variables to a hypothesized state;   generating an initial probability for each of the variables in the set hypothesized state.   
     
     
         25 . A computer-implemented method for identifying a plausible source of error in data used as input to a financial risk assessment system, the method comprising:
 receiving, using a server, financial information about a market;   estimating, using the server, a market scenario based on the financial information;   calculating, using a computer, an exposure profile based upon the market scenario;   determining, using a computer, whether there is a change in the exposure profile;   computing, using a computer, a conditional probability of a cause of the change in the exposure profile; and   assessing, using a computer, the plausibility of the cause.   
     
     
         26 . The method according to  claim 25 , further comprising characterizing the cause as based upon the normal operation of the system involving statistical simulation, expected market fluctuations, business operations, system fault, or bad data. 
     
     
         27 . The method according to  claim 25 , further comprising identifying a source that plausibly accounts for the change in the exposure profile. 
     
     
         28 . The method according to  claim 25 , wherein computing the conditional probability of the cause of the change in the exposure profile comprises:
 setting each of a plurality of variables to a hypothesized state;   generating the conditional probability for each of the plurality of variables in the set hypothesized state.   
     
     
         29 . A system for ensuring data integrity comprising:
 a risk assessment system;   a virtual assistant for implementing a Bayesian belief network to explain a change in an exposure profile based on data from the risk assessment system; and   a hypothesizer to determine which evidence to extract from the risk assessment system and provide the evidence to the virtual assistant for analysis.   
     
     
         30 . The system according to  claim 29 , wherein the risk assessment system comprises a pre-settlement exposure server. 
     
     
         31 . The system according to  claim 29 , wherein the risk assessment system receives financial information and uses a statistical process to estimate market scenarios. 
     
     
         32 . The system according to  claim 29 , further comprising a data grabber for obtaining data from the risk assessment system for use by the virtual assistant. 
     
     
         33 . The system according to  claim 29 , wherein the virtual assistant further comprises an evidence extraction component for converting the data from the risk assessment system into evidence. 
     
     
         34 . The system according to  claim 33 , wherein the evidence extraction component receives extracted evidence from the hypothesizer, searches the risk assessment system for raw biases and fact data of observable variables, and converts the evidence to factoids. 
     
     
         35 . The system according to  claim 34 , further comprising a weigh-in that weighs the factoids using statistical re-sampling and calculates the conditional for a given factoid, wherein the conditional is the probability of a null hypothesis that the factoid does not represent a significant change.