Abstract: A processing system may maintain a relationship graph that includes nodes and edges representing phone numbers and device identifiers having associations with the phone numbers. The processing system may obtain an identification of a first phone number or a first device identifier for a fraud evaluation and extract features from the relationship graph associated with at least one of the first phone number or the first device identifier. The plurality of features may include one or more device identifiers associated with the first phone number, or one or more phone numbers associated with the first device identifier. The processing system may then apply the features to a prediction model that is implemented by the processing system and that is configured to output a fraud risk value of the first phone number or the first device identifier and implement at least one remedial action in response to the fraud risk value.

Abstract: A processing system may maintain a relationship graph that includes nodes and edges representing phone numbers and device identifiers having associations with the phone numbers. The processing system may obtain an identification of a first phone number or a first device identifier for a fraud evaluation and extract features from the relationship graph associated with at least one of the first phone number or the first device identifier. The plurality of features may include one or more device identifiers associated with the first phone number, or one or more phone numbers associated with the first device identifier. The processing system may then apply the features to a prediction model that is implemented by the processing system and that is configured to output a fraud risk value of the first phone number or the first device identifier and implement at least one remedial action in response to the fraud risk value.

Abstract: A processing system may obtain a request to add at least a first feature to a feature graph database, where the request comprises a first feature ontology of the first feature, and where the first feature ontology comprises: a label of the first feature and a relationship of the first feature to a concept or to another feature. The processing system may then identify whether the first feature is a duplicate of a second feature in the feature graph database based at least upon the first feature ontology and a second feature ontology of the second feature and generate an indication of whether the first feature is a duplicate in response to the identifying.

Abstract: A processing system may apply a community detection process to a feature graph database to identify a plurality of communities of features, the feature graph database comprising: a plurality of objects, each associated with one of a feature or a concept, and a plurality of relationships between the plurality of objects. Next, the processing system may label a first plurality of features of the feature graph database with at least a first community label, where the first plurality of features comprises features of at least a first community of the plurality of communities. The processing system may then obtain a search associated with at least one feature of the feature graph database, where the at least one feature is a part of the at least the first plurality of features of the at least the first community, and provide the first plurality of features in response to the search.

Abstract: 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.

Abstract: A processing system may maintain a relationship graph that includes nodes and edges representing phone numbers and device identifiers having associations with the phone numbers. The processing system may obtain an identification of a first phone number or a first device identifier for a fraud evaluation and extract features from the relationship graph associated with at least one of the first phone number or the first device identifier. The plurality of features may include one or more device identifiers associated with the first phone number, or one or more phone numbers associated with the first device identifier. The processing system may then apply the features to a prediction model that is implemented by the processing system and that is configured to output a fraud risk value of the first phone number or the first device identifier and implement at least one remedial action in response to the fraud risk value.

Abstract: An example method performed by a processing system obtaining a first port-in number for a first mobile device from a first mobile communications service provider, wherein the first port-in number is known to be involved in fraudulent activity, constructing a social graph of communications between the first port-in number and a plurality of other numbers associated with a plurality of other communications devices, identifying, by the processing system, a maximal subgraph of the social graph, wherein the maximal subgraph connects the first port-in number and a subset of the plurality of other numbers that includes those of the plurality of other numbers for which a usage metric is below a predefined threshold for a defined period of time prior to the first port-in number being ported into the first mobile communications service provider, and identifying, by the processing system, a potential fraud ring, based on the maximal subgraph.

Abstract: An example method performed by a processing system obtaining a first port-in number for a first mobile device from a first mobile communications service provider, wherein the first port-in number is known to be involved in fraudulent activity, constructing a social graph of communications between the first port-in number and a plurality of other numbers associated with a plurality of other communications devices, identifying, by the processing system, a maximal subgraph of the social graph, wherein the maximal subgraph connects the first port-in number and a subset of the plurality of other numbers that includes those of the plurality of other numbers for which a usage metric is below a predefined threshold for a defined period of time prior to the first port-in number being ported into the first mobile communications service provider, and identifying, by the processing system, a potential fraud ring, based on the maximal subgraph.

Abstract: The described technology is generally directed towards a machine learning feature recommender, for use in connection with a feature store. By collecting data and recommending machine learning features to users based on collected data, embodiments can facilitate data scientists' discovery of features that have been used by their colleagues and that are likely to make their machine learning models more performant. The disclosed machine learning feature recommender can reduce the effort involved in developing machine learning models.

Abstract: 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.

Abstract: An example method performed by a processing system obtaining a first port-in number for a first mobile device from a first mobile communications service provider, wherein the first port-in number is known to be involved in fraudulent activity, constructing a social graph of communications between the first port-in number and a plurality of other numbers associated with a plurality of other communications devices, identifying, by the processing system, a maximal subgraph of the social graph, wherein the maximal subgraph connects the first port-in number and a subset of the plurality of other numbers that includes those of the plurality of other numbers for which a usage metric is below a predefined threshold for a defined period of time prior to the first port-in number being ported into the first mobile communications service provider, and identifying, by the processing system, a potential fraud ring, based on the maximal subgraph.

Abstract: A method and apparatus for identifying a plurality of users of a service of a network service provider as users known to each other are disclosed. For example, the method implemented via a processor obtains location information of a plurality of users of a service of a network service provider, selects a subset of users from the plurality of users that are deemed to be stationary, generates a score for each particular pair of users selected from the subset of users, determines for each particular pair of users, whether the score that is generated is greater than or equal to a threshold, identifies the pair of users as users who know each other, when the score for the particular pair of users is greater than or equal to the threshold, and performs an analysis to provide an offer to the pair of users, wherein the offer is based on the pair of users being identified as users who know each other.

Abstract: A method and apparatus for identifying a plurality of users of a service of a network service provider as users known to each other are disclosed. For example, the method implemented via a processor obtains location information of a plurality of users of a service of a network service provider, selects a subset of users from the plurality of users that are deemed to be stationary, generates a score for each particular pair of users selected from the subset of users, determines for each particular pair of users, whether the score that is generated is greater than or equal to a threshold, identifies the pair of users as users who know each other, when the score for the particular pair of users is greater than or equal to the threshold, and performs an analysis to provide an offer to the pair of users, wherein the offer is based on the pair of users being identified as users who know each other.