Method and apparatus for evaluating fraud risk in an electronic commerce transaction
First Claim
1. A method for evaluating fraud risk in an electronic commerce transaction and providing a representation of the fraud risk to a merchant using electronic communication, the method comprising:
- one or more processors generating and storing two or more fraud risk mathematical models, each model having a corresponding distribution of fraudulent transactions and a corresponding distribution of non-fraudulent transactions;
defining for each mathematical model a first point corresponding to a risk estimate at which a percentage of fraudulent transactions begins to have a non-zero value;
a second point corresponding to a risk estimate at which a percentage of non-fraudulent transactions begins to have a zero count; and
a third point corresponding to a risk estimate at which the percentage of fraudulent transactions equals the percentage of non-fraudulent transactions;
the one or more processors receiving information about a transaction and performing;
for each fraud risk mathematical model of the two or more fraud risk mathematical models, applying the information about the transaction to said each fraud risk mathematical model, said each fraud risk mathematical model producing a corresponding raw score;
transforming the corresponding raw score into a corresponding risk estimate for said each fraud risk mathematical model;
blending the corresponding risk estimate of said each fraud risk mathematical model into a single fraud score for the transaction including;
for said each risk estimate corresponding to said each fraud risk mathematical model, determining a corresponding fraud risk zone, wherein the fraud risk zones are determined based on the first point, the second point, and the third point defined by a particular fraud risk mathematical model;
determining a weighting factor to apply to said each risk estimate based on the corresponding fraud risk zone;
modifying said each risk estimate using that weighting factor.
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Abstract
Transaction information is received and applied to multiple fraud risk mathematical models that each produce a respective raw score, which are transformed with respective sigmoidal transform functions to produce optimized likelihood of fraud risk estimates to provide to a merchant. Respective fraud risk estimates are combined using fusion proportions associated with the respective risk estimates, producing a single point risk estimate, which is transformed with a sigmoidal function to produce an optimized single point risk estimate. The sigmoidal functions approximate a relationship between risk estimates produced by fraud risk detection models and a percentage of transactions associated with respective risk estimates; the relationship is represented in terms of real-world distributions of fraudulent and non-fraudulent transaction.
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Citations
16 Claims
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1. A method for evaluating fraud risk in an electronic commerce transaction and providing a representation of the fraud risk to a merchant using electronic communication, the method comprising:
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one or more processors generating and storing two or more fraud risk mathematical models, each model having a corresponding distribution of fraudulent transactions and a corresponding distribution of non-fraudulent transactions; defining for each mathematical model a first point corresponding to a risk estimate at which a percentage of fraudulent transactions begins to have a non-zero value;
a second point corresponding to a risk estimate at which a percentage of non-fraudulent transactions begins to have a zero count; and
a third point corresponding to a risk estimate at which the percentage of fraudulent transactions equals the percentage of non-fraudulent transactions;the one or more processors receiving information about a transaction and performing; for each fraud risk mathematical model of the two or more fraud risk mathematical models, applying the information about the transaction to said each fraud risk mathematical model, said each fraud risk mathematical model producing a corresponding raw score; transforming the corresponding raw score into a corresponding risk estimate for said each fraud risk mathematical model; blending the corresponding risk estimate of said each fraud risk mathematical model into a single fraud score for the transaction including; for said each risk estimate corresponding to said each fraud risk mathematical model, determining a corresponding fraud risk zone, wherein the fraud risk zones are determined based on the first point, the second point, and the third point defined by a particular fraud risk mathematical model; determining a weighting factor to apply to said each risk estimate based on the corresponding fraud risk zone; modifying said each risk estimate using that weighting factor. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A non-transitory computer-readable volatile or non-volatile medium storing one or more sequences of instructions for evaluating fraud risk in an electronic commerce transaction and providing a representation of the fraud risk to a merchant using electronic communication, which instructions, when executed by one or more processors, cause the one or more processors to perform:
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generating and storing two or more fraud risk mathematical models, each model having a corresponding distribution of fraudulent transactions and a corresponding distribution of non-fraudulent transactions; defining for each mathematical model a first point corresponding to a risk estimate at which a percentage of fraudulent transactions begins to have a non-zero value;
a second point corresponding to a risk estimate at which a percentage of non-fraudulent transactions begins to have a zero count; and
a third point corresponding to a risk estimate at which the percentage of fraudulent transactions equals the percentage of non-fraudulent transactions;receiving information about a transaction and performing; for each fraud risk mathematical model of the two or more fraud risk mathematical models, applying the information about the transaction to said each fraud risk mathematical model, said each fraud risk mathematical model producing a corresponding raw score; transforming the corresponding raw score into a corresponding risk estimate for said each fraud risk mathematical model; blending the corresponding risk estimate of said each fraud risk mathematical model into a single fraud score for the transaction including; for said each risk estimate corresponding to said each fraud risk mathematical model, determining a corresponding fraud risk zone, wherein the fraud risk zones are determined based on the first point, the second point, and the third point defined by a particular fraud risk mathematical model; determining a weighting factor to apply to said each risk estimate based on the corresponding fraud risk zone; modifying said each risk estimate using that weighting factor. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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Specification