Abstract: A computer-implemented method and system for detecting fraud in electronic commerce traffic are disclosed. A global conversion rate is defined that represents activity related to a purchase made or proxy activity executed during electronic commerce traffic to a commercial website that is indicative of non-fraud activity. Subsets of the electronic commerce traffic to a commercial website are monitored for clusters of activity having a conversion rate that is lower than a global conversion rate by a threshold margin. A number of user-initiated input signals from an input device to navigate from each of one or more affiliate websites to the commercial website, and a number of conversions generated from each of the one or more affiliate websites, are registered by a computer for analysis.

Abstract: A computer-implemented method and system for detecting fraud in electronic commerce traffic are disclosed. A global conversion rate is defined that represents activity related to a purchase made or proxy activity executed during electronic commerce traffic to a commercial website that is indicative of non-fraud activity. Subsets of the electronic commerce traffic to a commercial website are monitored for clusters of activity having a conversion rate that is lower than a global conversion rate by a threshold margin. A number of user-initiated input signals from an input device to navigate from each of one or more affiliate websites to the commercial website, and a number of conversions generated from each of the one or more affiliate websites, are registered by a computer for analysis.

Abstract: A computer-implemented method and system for detecting fraud in electronic commerce traffic are disclosed. A global conversion rate is defined that represents activity related to a purchase made or proxy activity executed during electronic commerce traffic to a commercial website that is indicative of non-fraud activity. Subsets of the electronic commerce traffic to a commercial website are monitored for clusters of activity having a conversion rate that is lower than a global conversion rate by a threshold margin. A number of user-initiated input signals from an input device to navigate from each of one or more affiliate websites to the commercial website, and a number of conversions generated from each of the one or more affiliate websites, are registered by a computer for analysis.

Abstract: A Click Conversion Score (CCS) is a tool to provide a new “Ad-Exchange” for connecting Publishers and Advertisers in the Pay-per-Click advertising market. The CCS provides checks and balances into the Ad-Exchange by assessing the quality of traffic each Publisher provides Advertisers using an adaptive, multi-dimensional profiling system. This assessment is directly used in the charges billed the Advertisers. The score is adaptive, and highly dependent on the conversion feedback loop provided by Advertisers. A CCS system scores each Click and each resulting Conversion an Advertiser receives from the Ad-Exchange. In the preferred implementation, the score reflects a ratio of the measured conversion rate for a Publisher's traffic with the “expected” conversion rate based upon Advertisers histories.

Abstract: A software system and method for executing secure commercial transactions online is disclosed. The system intercepts and verifies, against consumer provided inputs, Secure Socket Layer (SSL) communications from normal Web browser usage. The system can include a software module loaded onto the consumer's client computer, and which uses independently-derived look-ups to associate a web domain name with its SSL public key to verify that a given web session is appropriately encrypted.

Abstract: A software system and method for executing secure commercial transactions online is disclosed. A user's password is received to initiate secure socket layer (SSL) communications with a transaction site on a server. A web session associated with the SSL communications is encrypted by associating a domain name of the transaction site with its SSL public key. Then, the user's password is added to a hypertext markup language (HTML) header of a message within the web session. When added, the password is invisible to a hypothetical man-in-the-middle (MITM) attacker, who cannot read the encrypted message nor mimic the user. The MITM is thus unable to compromise the user's account as the MITM is unable to provide the correct password into any fraudulent message.

Abstract: The invention aids an entity operating on the Internet or on another network to selectively request additional data about a user who has made a request for an interaction with the entity. The invention helps an entity to determine when and how to request additional data so as to reduce the likelihood of causing the user to have an adverse reaction, e.g., terminate the interaction. One embodiment of the invention concerns customers requesting transactions with on-line merchants. More specifically, this embodiment aids merchants by detecting Internet credit card transactions that are likely to be fraudulent, and providing the merchants with mechanisms for managing a suspected transaction as it occurs to obtain additional information that can be useful to reducing the likelihood of fraud.

Abstract: Transaction processing of online transactions at merchant sites determines the likelihood that such transactions are fraudulent, accounting for unreliable fields of a transaction order, which fields do not reliably identify a purchaser. A scoring server using statistical model uses multiple profiles associated with key fields, along with weights to indicate the degree to which the profiles identify the purchaser of the transaction.

Abstract: Computer implemented methods and systems of processing transactions to determine the risk of transaction convert high categorical information, such as text data, to low categorical information, such as category or cluster IDs. The text data may be merchant names or other textual content of the transactions, or data related to a consumer, or any other type of entity which engages in the transaction. Content mining techniques are used to provide the conversion from high to low categorical information. In operation, the resulting low categorical information is input, along with other data, into a statistical model. The statistical model provides an output of the level of risk in the transaction. Methods of converting the high categorical information to low categorical clusters, of using such information, and other aspects of the use of such clusters are disclosed.

Abstract: A method of generating a model for performing account-level fraud detection is disclosed, and includes obtaining a plurality of candidate rules for referrals of financial transactions, calculating a savings density for each of the plurality of rules, selecting a rule from the plurality of rules having the maximum savings density, iteratively rebalancing savings densities for the remaining rules of the plurality of rules and selecting a next rule until a volume constraint is satisfied, and applying the selected rules to one or more financial transactions.

Abstract: Techniques for compressing data transaction history profiles are disclosed. Such profiles can include a plurality of profile variables with each profile variable comprising a real number that provides a factor for determining whether a proposed data transaction is indicative of fraud. A bit string is generated for each profile variable in the profiles that characterizes a first value plus a second value. The first value is equal to a mantissa for the real number corresponding to the profile variable. The second value is equal to a number of orders of magnitude above a minimum required expressed as multiples of the number of orders magnitude required to represent the plurality of real numbers in each the plurality of transaction history profiles divided by a range of bits. The generated bit string is stored as compressed profile variable within the data transaction history profiles. Related systems, apparatus, methods, and/or articles are also described.

Abstract: A computer-implemented simulator models the entire analytic value chain so that data generation, model fitting and strategy optimization are an integral part of the simulation. Data collection efforts, data mining algorithms, predictive modeling technologies and strategy development methodologies define the analytic value chain of a business operation: data?models?strategies?profit. Inputs to the simulator include consumer data and potential actions to be taken regarding a consumer or account. The invention maps what is known about a consumer or an account and the potential actions that the business can take on that consumer or account to potential future financial performance. After iteratively performing simulations using varying inputs, modeling the effect of the innovation on a profit model, the simulator outputs a prediction of the commercial value of an analytic innovation.

Abstract: A computer-implemented simulator models the entire analytic value chain so that data generation, model fitting and strategy optimization are an integral part of the simulation. Data collection efforts, data mining algorithms, predictive modeling technologies and strategy development methodologies define the analytic value chain of a business operation: data?models?strategies?profit. Inputs to the simulator include consumer data and potential actions to be taken regarding a consumer or account. The invention maps what is known about a consumer or an account and the potential actions that the business can take on that consumer or account to potential future financial performance. After iteratively performing simulations using varying inputs, modeling the effect of the innovation on a profit model, the simulator outputs a prediction of the commercial value of an analytic innovation.

Abstract: A computer-implemented simulator models the entire analytic value chain so that data generation, model fitting and strategy optimization are an integral part of the simulation. Data collection efforts, data mining algorithms, predictive modeling technologies and strategy development methodologies define the analytic value chain of a business operation: data?models?strategies?profit. Inputs to the simulator include consumer data and potential actions to be taken regarding a consumer or account. The invention maps what is known about a consumer or an account and the potential actions that the business can take on that consumer or account to potential future financial performance. After iteratively performing simulations using varying inputs, modeling the effect of the innovation on a profit model, the simulator outputs a prediction of the commercial value of an analytic innovation.

Abstract: A software system and method for executing secure commercial transactions online is disclosed. A user's password is received to initiate secure socket layer (SSL) communications with a transaction site on a server. A web session associated with the SSL communications is encrypted by associating a domain name of the transaction site with its SSL public key. Then, the user's password is added to a hypertext markup language (HTML) header of a message within the web session. When added, the password is invisible to a hypothetical man-in-the-middle (MITM) attacker, who cannot read the encrypted message nor mimic the user. The MITM is thus unable to compromise the user's account as the MITM is unable to provide the correct password into any fraudulent message.

Abstract: Computer implemented methods and systems of processing transactions to determine the risk of transaction convert high categorical information, such as text data, to low categorical information, such as category or cluster IDs. The text data may be merchant names or other textual content of the transactions, or data related to a consumer, or any other type of entity which engages in the transaction. Content mining techniques are used to provide the conversion from high to low categorical information. In operation, the resulting low categorical information is input, along with other data, into a statistical model. The statistical model provides an output of the level of risk in the transaction. Methods of converting the high categorical information to low categorical clusters, of using such information, and other aspects of the use of such clusters are disclosed.

Abstract: A Click Conversion Score (CCS) is a tool to provide a new “Ad-Exchange” for connecting Publishers and Advertisers in the Pay-per-Click advertising market. The CCS provides checks and balances into the Ad-Exchange by assessing the quality of traffic each Publisher provides Advertisers using an adaptive, multi-dimensional profiling system. This assessment is directly used in the charges billed the Advertisers. The score is adaptive, and highly dependent on the conversion feedback loop provided by Advertisers. A CCS system scores each Click and each resulting Conversion an Advertiser receives from the Ad-Exchange. In the preferred implementation, the score reflects a ratio of the measured conversion rate for a Publisher's traffic with the “expected” conversion rate based upon Advertisers histories.

Abstract: A computer-implemented simulator models the entire analytic value chain so that data generation, model fitting and strategy optimization are an integral part of the simulation. Data collection efforts, data mining algorithms, predictive modeling technologies and strategy development methodologies define the analytic value chain of a business operation: data?models?strategies?profit. Inputs to the simulator include consumer data and potential actions to be taken regarding a consumer or account. The invention maps what is known about a consumer or an account and the potential actions that the business can take on that consumer or account to potential future financial performance. After iteratively performing simulations using varying inputs, modeling the effect of the innovation on a profit model, the simulator outputs a prediction of the commercial value of an analytic innovation.

Abstract: A computer-implemented simulator models the entire analytic value chain so that data generation, model fitting and strategy optimization are an integral part of the simulation. Data collection efforts, data mining algorithms, predictive modeling technologies and strategy development methodologies define the analytic value chain of a business operation: data?models?strategies?profit. Inputs to the simulator include consumer data and potential actions to be taken regarding a consumer or account. The invention maps what is known about a consumer or an account and the potential actions that the business can take on that consumer or account to potential future financial performance. After iteratively performing simulations using varying inputs, modeling the effect of the innovation on a profit model, the simulator outputs a prediction of the commercial value of an analytic innovation.

Abstract: Techniques for compressing data transaction history profiles are disclosed. Such profiles can include a plurality of profile variables with each profile variable comprising a real number that provides a factor for determining whether a proposed data transaction is indicative of fraud. A bit string is generated for each profile variable in the profiles that characterizes a first value plus a second value. The first value is equal to a mantissa for the real number corresponding to the profile variable. The second value is equal to a number of orders of magnitude above a minimum required expressed as multiples of the number of orders magnitude required to represent the plurality of real numbers in each the plurality of transaction history profiles divided by a range of bits. The generated bit string is stored as compressed profile variable within the data transaction history profiles. Related systems, apparatus, methods, and/or articles are also described.