METHOD AND SYSTEM FOR USING A BAYESIAN BELIEF NETWORK TO ENSURE DATA INTEGRITY
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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.
13 Citations
30 Claims
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1-9. -9. (canceled)
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10. A method for identifying plausible sources of error in a financial risk assessment (FRA) system, comprising:
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identifying, by a computer, a plurality of variables of the FRA system; implementing, by the computer, a Bayesian network to represent implications between and among the plurality of variables; generating, by the computer, an initial probability for each of the plurality of variables of the FRA system; extracting, by the computer, observed data from one of the plurality of variables of the FRA system; determining, by the computer, an evidentiary finding based on the extracted factual data from the one of the plurality of variables of the FRA system; and assessing, by the computer, the initial probability for the one of the plurality of variables of the FRA system based on the evidentiary finding. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18)
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19. A computerized system for identifying minimizing sources of error in a risk assessment system (RAS), comprising:
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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. - View Dependent Claims (20)
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21. 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:
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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. - View Dependent Claims (22, 23)
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24. A system for ensuring data integrity comprising:
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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. - View Dependent Claims (25, 26, 27, 28, 29, 30)
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Specification