Abstract: In various exemplary embodiments, a system and associated method to perform an adaptive risk-based assessment of a user is disclosed. A method includes receiving a request from a user to perform an action at an electronic marketplace, retrieving a plurality of risk assessment factors associated with the action and the user, performing a risk assessment process on the risk assessment factors to identify a risk mitigation process, requesting that the user perform the identified risk mitigation process, and allowing the user to perform the action in response to the user completing the risk mitigation.

Abstract: A method for execution by a computing entity of a data transactional network includes generating a plurality of risk analysis responses regarding a transaction for fraud evaluation, where the transaction is between a first computing device and a second computing device regarding transactional subject matter. The method further includes performing a first level interpretation of the plurality of risk analysis responses to produce a first level fraud answer. The method further includes determining a confidence of the first level fraud answer compares unfavorably with a confidence threshold. The method further includes determining a second level interpretation of the plurality of risk analysis responses based on a level of the confidence of the first level fraud answer. The method further includes performing the second level interpretation of the plurality of risk analysis responses to produce a fraud evaluation answer regarding the transaction.

Abstract: A method includes selecting, for evaluation, an entry regarding a transaction from a current transaction-answer (TA) matrix that includes a prediction field for the entry. The method further includes determining whether the prediction field associated with the entry includes an indication that a predicted fraud answer for the transaction is favorable. When unfavorable, the method further includes obtaining inputted data utilized by a fraud assessment model that includes a plurality of artificial intelligence (AI) modules, obtaining additional data regarding the transaction, augmenting the inputted data with the additional data to produce updated data, utilizing the updated data to produce an updated fraud evaluation answer, and determining whether the updated fraud evaluation answer is favorable.

Abstract: A method includes receiving, by a computing entity, a transaction for fraud evaluation. The method further includes generating, by the computing entity, evidence vectors regarding the transaction, wherein an evidence vector is a piece of information regarding a topic, or portion thereof, of a list of topics. The method further includes engaging, by the computing entity, tools to generate risk analysis responses based on the evidence vectors. The method further includes discarding, by the computing entity, indeterminate responses of the risk analysis responses to produce a group of risk analysis responses. The method further includes interpreting, by the computing entity, the group of risk analysis responses to produce a fraud evaluation answer of low risk of fraud, high risk of fraud, or further analysis is required.

Abstract: A method includes receiving, by a computing entity, a transaction for fraud evaluation. The method further includes generating, by the computing entity, evidence vectors regarding the transaction, wherein an evidence vector is a piece of information regarding a topic, or portion thereof, of a list of topics. The method further includes engaging, by the computing entity, tools to generate risk analysis responses based on the evidence vectors. The method further includes discarding, by the computing entity, indeterminate responses of the risk analysis responses to produce a group of risk analysis responses. The method further includes interpreting, by the computing entity, the group of risk analysis responses to produce a fraud evaluation answer of low risk of fraud, high risk of fraud, or further analysis is required.

Abstract: In various exemplary embodiments, a system and associated method to perform an adaptive risk-based assessment of a user is disclosed. A method includes receiving a request from a user to perform an action at an electronic marketplace, retrieving a plurality of risk assessment factors associated with the action and the user, performing a risk assessment process on the risk assessment factors to identify a risk mitigation process, requesting that the user perform the identified risk mitigation process, and allowing the user to perform the action in response to the user completing the risk mitigation.

Abstract: In various exemplary embodiments, a system and associated method to perform an adaptive risk-based assessment of a user is disclosed. A method includes receiving a request from a user to perform an action at an electronic marketplace, retrieving a plurality of risk assessment factors associated with the action and the user, performing a risk assessment process on the risk assessment factors to identify a risk mitigation process, requesting that the user perform the identified risk mitigation process, and allowing the user to perform the action in response to the user completing the risk mitigation.

Abstract: In various exemplary embodiments, a system and associated method to perform an adaptive risk-based assessment of a user is disclosed. The method includes assigning a risk assessment process to the user and providing a plurality of assessment factors to the risk assessment process. The plurality of assessment factors are based on both the user (e.g., previously assessed factors, residence, phone number, address, etc.) and one or more actions the user may perform (e.g., selling an item in an electronic marketplace and the value of the item). A post-action analysis of the risk assessment process is performed. The risk assessment process can be modified based on a determination of the post-action analysis.

Abstract: In various exemplary embodiments, a system and associated method to perform an adaptive risk-based assessment of a user is disclosed. The method includes assigning a risk assessment process to the user and providing a plurality of assessment factors to the risk assessment process. The plurality of assessment factors are based on both the user (e.g., previously assessed factors, residence, phone number, address, etc.) and one or more actions the user may perform (e.g., selling an item in an electronic marketplace and the value of the item). A post-action analysis of the risk assessment process is performed. The risk assessment process can be modified based on a determination of the post-action analysis.

Abstract: Embodiments of the present invention relate to transforming a nominal feature to a numeric feature that indicates a likelihood or probability of a particular outcome. Numeric features are determined that indicate a likelihood of an outcome given the value of the collected data (nominal values). Such numeric features are used to represent the corresponding nominal features for use in generating a machine learned model. As such, a nominal feature initially captured in a data set is transformed or converted to a numeric feature that represents a likelihood of a corresponding outcome as opposed to a Boolean value. Upon transforming nominal values to numeric values based on the likelihood of outcome, the numeric values can be used to generate a machine learned model that is used to predict future outcomes.

Abstract: Embodiments of the present invention relate to transforming a nominal feature to a numeric feature that indicates a likelihood or probability of a particular outcome. Numeric features are determined that indicate a likelihood of an outcome given the value of the collected data (nominal values). Such numeric features are used to represent the corresponding nominal features for use in generating a machine learned model. As such, a nominal feature initially captured in a data set is transformed or converted to a numeric feature that represents a likelihood of a corresponding outcome as opposed to a Boolean value. Upon transforming nominal values to numeric values based on the likelihood of outcome, the numeric values can be used to generate a machine learned model that is used to predict future outcomes.

Abstract: In various exemplary embodiments, a system and associated method to perform an adaptive risk-based assessment of a user is disclosed. The method includes assigning a risk assessment process to the user and providing a plurality of assessment factors to the risk assessment process. The plurality of assessment factors are based on both the user (e.g., previously assessed factors, residence, phone number, address, etc.) and one or more actions the user may perform (e.g., selling an item in an electronic marketplace and the value of the item). A post-action analysis of the risk assessment process is performed. The risk assessment process can be modified based on a determination of the post-action analysis.