Method and apparatus for generating a bi-gram score in fraud risk analysis
First Claim
1. A method, comprising:
- receiving, using one or more processing units, character data;
determining, using the one or more processing units, a set of character pairs, wherein each character pair in the set represents a pair of adjacent characters in the character data;
generating, using the one or more processing units, bi-gram probability data using data from one or more data sources, wherein the data from the one or more data sources includes one or more words, and wherein the bi-gram probability data includes a plurality of bi-grams, wherein each bi-gram is a pair of letters that is associated with one or more probability values;
matching, using the one or more processing units, each character pair to a bi-gram in the bi-gram probability data;
determining, using the one or more processing units, a probability value for each character pair using the bi-gram that matches that character pair, wherein a probability value represents occurrences of a character pair in the one or more data sources, and wherein a probability value is used to determine a measure of intelligibility associated with the character data; and
generating, using the one or more processing units, a bi-gram score using the determined probability values, wherein the bi-gram score represents a measure of intelligibility associated with the character data.
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Abstract
Evaluating fraud risk in a transaction between consumer and a merchant over a network is disclosed. The merchant requests service over the network using a secure, open messaging protocol. An e-commerce transaction or electronic purchase order is received from the merchant, the level of risk associated with each order is measured, and a risk score is returned to the merchant. In one embodiment, data validation, highly predictive artificial intelligence pattern matching, network data aggregation and negative file checks are used to examine numerous factors to calculate fraud risk. A risk score is generated and compared to the merchant'"'"'s specified risk threshold. The result is returned to the merchant for order disposition.
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Citations
30 Claims
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1. A method, comprising:
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receiving, using one or more processing units, character data; determining, using the one or more processing units, a set of character pairs, wherein each character pair in the set represents a pair of adjacent characters in the character data; generating, using the one or more processing units, bi-gram probability data using data from one or more data sources, wherein the data from the one or more data sources includes one or more words, and wherein the bi-gram probability data includes a plurality of bi-grams, wherein each bi-gram is a pair of letters that is associated with one or more probability values; matching, using the one or more processing units, each character pair to a bi-gram in the bi-gram probability data; determining, using the one or more processing units, a probability value for each character pair using the bi-gram that matches that character pair, wherein a probability value represents occurrences of a character pair in the one or more data sources, and wherein a probability value is used to determine a measure of intelligibility associated with the character data; and generating, using the one or more processing units, a bi-gram score using the determined probability values, wherein the bi-gram score represents a measure of intelligibility associated with the character data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system, comprising:
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one or more processors; a non-transitory computer-readable storage medium containing instructions configured to cause the one or more processors to perform operations, including; receiving character data; determining a set of character pairs, wherein each character pair in the set represents a pair of adjacent characters in the character data; generating bi-gram probability data using data from one or more data sources, wherein the data from the one or more data sources includes one or more words, and wherein the bi-gram probability data includes a plurality of bi-grams, wherein each bi-gram is a pair of letters that is associated with one or more probability values; matching each character pair to a bi-gram in the bi-gram probability data; determining a probability value for each character pair using the bi-gram that matches that character pair, wherein a probability value represents occurrences of a character pair in the one or more data sources, and wherein a probability value is used to determine a measure of intelligibility associated with the character data; and generating a bi-gram score using the determined probability values, wherein the bi-gram score represents a measure of intelligibility associated with the character data. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. A computer program product, tangibly embodied in a non-transitory machine readable storage medium, including instructions configured to cause a data processing apparatus to:
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receive character data; determine a set of character pairs, wherein each character pair in the set represents a pair of adjacent characters in the character data; generate bi-gram probability data using data from one or more data sources, wherein the data from the one or more data sources includes one or more words, wherein the bi-gram probability data includes a plurality of bi-grams, wherein each bi-gram is a pair of letters that is associated with one or more probability values; match each character pair to a bi-gram in the bi-gram probability data; determine a probability value for each character pair using the bi-gram that matches that character pair, wherein a probability value represents occurrences of a character pair in the one or more data sources, and wherein a probability value is used to determine a measure of intelligibility associated with the character data; and generate a bi-gram score using the determined probability values, wherein the bi-gram score represents a measure of intelligibility associated with the character data. - View Dependent Claims (22, 23, 24, 25, 26, 27, 28, 29, 30)
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Specification