Adobe, Inc.

Raise Marketplace, LLC

Lars Wright

William Alfred Wright

Christopher Jigme Wright

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.

Predictive fraud analysis system for data transactions

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.

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Lars Wright