An anti-communications fraud apparatus 1 includes: an analysis unit 13 that analyzes a voiceprint of a communication voice of a calling party; a determination unit 14 that acquires, from a database 17 in which the voiceprint and a degree of fraud risk are stored in association with each other, the degree of fraud risk corresponding to the voiceprint of the calling party, and determines whether the degree of fraud risk exceeds a predetermined threshold; and a notification unit 15 that notifies that the calling party is dangerous when the degree of fraud risk exceeds the threshold.

The present disclosure relates to determining the shareability of values of node profiles. Record objects and electronic activities of a system of record corresponding to a data source provider may be accessed. Each record object may correspond to a record object type and have one or more object field-value pairs. Node profiles may be maintained. Values of fields corresponding to a predetermined type of field including fewer than a predetermined threshold number of data source providers may be identified. A restriction tag used to restrict populating other node profiles may be generated. Provision of the value with a second data source provider may be restricted.

Disclosed herein are systems and methods for handling unwanted telephone calls through a branching node. In one aspect, an exemplary method comprises, intercepting a call request from a terminal device of a calling party to a terminal device of a called party, establishing a connection through the branching node via two different communication channels, a first communication channel being with the terminal device of the called party and a second communication channel being with a call recorder; duplicating media data between the terminal devices such that one data stream is directed towards a receiving device of the media data and a second data stream is directed towards the call recorder; recording and sending the recorded call to an automatic speech recognizer for converting the media file to digital information suitable for analysis; and when the call is unwanted, handling the call based on classification of the call.

Systems and methods described herein can automatically route an inbound call from an identified customer to one of a plurality of agents, the agent being selected on the basis of likelihood of a favorable outcome. The method determines a predictive model appropriate for the identified customer, with model variables including call center data, and targeted marketing data based upon risk data for the customer. An analytical engine calculates outcome predictions by applying the predictive model to values of model variables over a recent time interval. In a time-series analysis, this calculation is repeated while dynamically adjusting the recent time interval, until identifying a call routing option that satisfies a favorable outcome criterion. This method may be used to select the agent to handle the incoming call, and optionally to select a product for that agent to discuss with the identified customer.

The present disclosure relates to generating performance profiles of member nodes. A plurality of electronic activities can be accessed. A subset of electronic activities from the plurality of electronic activities can be identified. The subset of electronic activities can be parsed to identify participants of the electronic activities. A second node profile can be accessed for each participant. Participant types can be identified from each second node profiles. A distribution of the subset of electronic activities can be determined. A performance profile can be generated.

A method for protecting subscriber data includes intercepting network traffic associated with a call. The network traffic includes call parameters and call stream data. A first set of the call parameters is analyzed. A first probability value of the call being declared as unwanted is determined. The call stream data is analyzed to define a second set of call parameters. The first set of call parameters is reanalyzed based on the second set. A second probability value of the call being declared as unwanted is determined. A determination is made if the second probability value exceeds a second threshold value. The call is declared as unwanted, in response to determining that the second probability value exceeds the second threshold. The first and second sets of call parameters are transmitted to an application configured to protect data of a protected subscriber.

Embodiments described herein provide for performing a risk assessment using graph-derived features of a user interaction. A computer receives interaction information and infers information from the interaction based on information provided to the computer by a communication channel used in transmitting the interaction information. The computer may determine a claimed identity of the user associated with the user interaction. The computer may extract features from the inferred identity and claimed identity. The computer generates a graph representing the structural relationship between the communication channels and claimed identities associated with the inferred identity and claimed identity. The computer may extract additional features from the inferred identity and claimed identity using the graph. The computer may apply the features to a machine learning model to generate a risk score indicating the probability of a fraudulent interaction associated with the user interaction.

An example method of operation may include one or more of identifying an inbound call intended for a mobile device subscribed to a protected carrier network, determining the inbound call is assigned an origination telephone number that is subscribed to the protected carrier network, determining whether an inbound call origination source location indicates the protected carrier network or an out-of-network carrier network based on one or more call parameters received with the inbound call, and determining whether to transmit an indication to the mobile device that the inbound call has an elevated likelihood of being a scam call based on the inbound call origination source location.

Example embodiments of systems and methods for telephone call regulation include the use of a predictive model. An example method includes receiving, by a predictive model, a telephone number from a user application comprising instructions for execution on a user device and determining, by the predictive model, a spam factor for the received telephone number. The example method further includes determining, by the predictive model, a classification of the received telephone number based on the spam factor and transmitting, by the predictive model, the classification of the received telephone number to the user application. The example method further includes receiving, by the predictive model, a feedback from the user application, the feedback indicative of a corrective classification of the received telephone number and modifying, by the predictive model, the spam factor for the received telephone number based on the corrective classification.

A method, computer system, and a computer program product for filtering a phone call are provided. An incoming phone call to a phone is detected. Call metadata from the incoming phone call is received. The call metadata is compared to entries of a learned relationship filter. The entries of the learned relationship filter are obtained by monitoring behavior of a phone user on the phone during previous calls to the phone and application usage on the phone so that learned relationships to the phone user are identified and saved. The learned relationship filter provides a filtering response. The filtering response is performed to the incoming phone call.