A processing system may obtain a customer identifier at a first retail location of a telecommunication network service provider, determine a recency factor of the identifier, obtain an identification of items of interest to the customer, and determine whether the customer has visited a second retail location of the provider within a time period prior to the customer being at the first retail location. The processing system may then apply, to a fraud detection machine learning model, a plurality of factors comprising: a quantity of items of interest, a value of the items, a factor associated with whether the customer has visited the second retail location within the time period, and the recency factor, where the fraud detection machine learning model outputs a fraud indicator value, determine that the fraud indicator value meets a warning threshold and present a warning to a device at the first retail location.

A method and apparatus for performing privacy preserving fraud detection in network based transactions are described. The method may include receiving a fraud detection message during a transaction between a user system and a merchant system, the message having a set of cryptographically transformed universal resource locator (URL) components generated from a URL of a web page of the merchant system on which the transaction is to occur. The method may also include generating one or more secure and anonymous fraud detection features, each fraud detection feature comprising a select subset of the cryptographically transformed URL components. The method may also include performing fraud detection for the web page using the one or more secure and anonymous fraud detection features to determine a likelihood that fraud is occurring in the transaction.

A method executed by a computing entity includes obtaining, in accordance with a securely passing process, control over a secure first token representing a first pending transaction associated with a transaction item. The method further includes obtaining a selection of a first subset of instant assets of a set of candidate instant assets that may be utilized to subsequently provide conversion of the selected contingent asset to complete the first pending transaction. The method further includes generating an updated secure first token in accordance with the securely passing process to represent the selection of the first subset of instant assets of the set of candidate instant assets to subsequently provide the conversion of the selected contingent asset to complete the first pending transaction.

The present invention relates to a method and system for tracking abnormal transactions in e-commerce, and an object of the present invention is to track abnormal transactions by analyzing complex characteristic data of product information uploaded to an e-commerce platform. In order to achieve this object, a method for detecting an abnormal transaction in an electronic device according to the present invention includes: step a of generating an identity map based on first transaction information previously stored in an e-commerce server; step b of collecting second transaction information newly uploaded to the e-commerce server; step c of extracting a first identifier and second identifiers included in the second transaction information and generating a third identifier by combining the plurality of second identifiers; and step d of determining whether the second transaction information is an abnormal transaction by searching the identity map for the first identifier and the third identifier.

System, device, and method of detecting business email fraud and corporate email fraud. A method includes: receiving a user request to perform an online transaction on behalf of a corporate entity; generating a notification that requires the user to indicate whether he obtained managerial authorization for performing that online transaction on behalf of that corporate entity; monitoring user gestures and user interactions in response to that notification; receiving a positive answer from the user; performing an analysis of user gestures and user interactions, and generating a signal indicating a determination that the positive answer from the user is false, based on analyzed metrics that correspond to characteristics of the user gestures and user interactions; blocking or unauthorizing, at least temporarily, that online transaction that was requested on behalf of that corporate entity.

A computer system includes a processor programmed to process a first web page to identify a hyperlink contained thereon. The hyperlink includes a link to a second web page. The processor performs natural language processing on the first web page to determine one or more context word tokens and on the second web page to determine a context of the second web page. The processor also applies a context relevant tag to the hyperlink to generate a tagged hyperlink. The processor maps at least one of the context word tokens to the context relevant tag applied to the hyperlink and generates a transaction score for the tagged hyperlink.

A method is provided comprising receiving, at a first node, transactional data associated with a consumer (wherein the first node comprises a processor and a tangible, non-transitory memory), receiving, at the first node, a credit approval request associated with the consumer, wherein the credit approval request is associated with a proposed transaction, determining, by the first node, whether the transactional data conforms with the proposed transaction, and at least one of approving and denying the credit approval request in response to the determination.

Systems, methods, and apparatus are provided for fraud screening via smart glasses interactions during an ATM session. A smart glasses device may capture an image of an ATM environment. The ATM and the smart glasses device may be edge nodes on an edge network. An edge platform may use a fraud detection model to classify the image and compare it to stored ATM images. The model may be trained at an enterprise server and stored on the edge platform. In response to a determination of fraud at the edge platform, a fraud alert may be transmitted to the smart glasses device during the ATM session. Edge computing reduces latency to enable real-time smart glasses alerts. The smart glasses device may communicate the fraud alert to other smart glasses devices on the edge network.

To detect multiple suspicious patterns while at the same time keeping the number of model parameters low, a learned aggregation model is used to distinguish suspiciously similar applications from unrelated applications.

A system and method for automatically identifying an anomalous pattern. The method encompasses receiving, a stream of data. The method further comprises determining, a monitoring metric for at least one of one or more dimensions and one or more groups of dimensions associated with the stream of data, at a target time and at a benchmark time period. Further the method comprises identifying, the monitoring metric at the target time as an outlier to the monitoring metric at the benchmark time period based at least on a threshold value. The method further comprises automatically identifying, the anomalous pattern based at least on said identification of the monitoring metric for at least one of the dimension(s) and the group(s) of dimensions at the target time as the outlier to the monitoring metric for at least one of the dimension(s) and the group(s) of dimensions at the benchmark time period.