One example method of operation may include receiving a call message associated with a call, determining a service provider network identifier based on a telephone number of a call origination device, identifying, from the call message, an identity header with a link to a public certificate repository storing a public certificate assigned to a service provider network hosting the call origination device, retrieving a service provider code assigned to the service provider network from the public certificate, and determining whether the service provider code matches the service provider network identifier as identified from a verification table.

Various examples are directed to systems and methods for detecting potentially fraudulent voice calls to a financial services institution. A computing system may receive an indication of a voice call placed by a voice caller to an operator. The computing system may generate a network address indicator describing a network location. The network address indicator may be provided to the voice caller. The computing system may receive an indication of a financial services account indicated by the voice caller. The computing system may also receive an indication of an access to the network location by a remote device. The computing system may determine, using the indication of the access to the network location, a first location associated with the remote device and determine that the first location does not match a second location associated with the financial services account. The computing system may generate an alert indicating that the voice call is potentially fraudulent.

Provided are systems, methods, and apparatuses initiating outbound communications. The system may include at least one processor of a telecommunications device comprising a display and a communication application, the at least one processor programmed or configured to: receive, with the communication application, a communication request comprising a number, the communication request initiated by a user of the telecommunications device, determine whether to automatically initiate a communication to the number based on recipient data associated with the number, in response to determining to not automatically initiate the communication to the number, prompt the user, on the display of the telecommunications device, with a selectable option configured to initiate the communication to the number upon selection, and in response to determining to automatically initiate the communication to the number, automatically initiate the communication to the number upon selection.

A computer-implemented method and system for improving caller verification is provided. The method comprises registering an intended communications session by generating a key using, at least, a first call time window identifier, and storing the key in a database; in response to registering the intended communication session, receiving a request for caller verification, wherein the request comprises data representing a second call time window identifier; in response to receiving the request for caller verification, generating a comparison key based on the request; comparing the comparison key with the key stored in the database; and verifying the intended communication session in response to comparing the comparison key with the key.

A computer may train a single-class machine learning using normal speech recordings. The machine learning model or any other model may estimate the normal range of parameters of a physical speech production model based on the normal speech recordings. For example, the computer may use a source-filter model of speech production, where voiced speech is represented by a pulse train and unvoiced speech by a random noise and a combination of the pulse train and the random noise is passed through an auto-regressive filter that emulates the human vocal tract. The computer leverages the fact that intentional modification of human voice introduces errors to source-filter model or any other physical model of speech production. The computer may identify anomalies in the physical model to generate a voice modification score for an audio signal. The voice modification score may indicate a degree of abnormality of human voice in the audio signal.

Systems and methods for detecting indications of a scam caller are disclosed. Call data, such as call audio, is received and used to create a training dataset. Using the training dataset, a machine learning model is trained to detect indications of a scam caller in a phone call. An Interactive Voice Response (IVR) model is trained or configured, using voice samples of speech of a subscriber of a telecommunications service provider, to simulate speech and conversation of the subscriber. A conversational agent is generated using the IVR model and the trained machine learning model. The conversational agent receives a phone call, engages a caller in simulated conversation, and detects indications of whether the caller is a likely scam caller. If the caller is determined to be a likely scam caller, an alert can be generated and/or the call can be disconnected.

Embodiments described herein provide for passive caller verification and/or passive fraud risk assessments for calls to customer call centers. Systems and methods may be used in real time as a call is coming into a call center. An analytics server of an analytics service looks at the purported Caller ID of the call, as well as the unaltered carrier metadata, which the analytics server then uses to generate or retrieve one or more probability scores using one or more lookup tables and/or a machine-learning model. A probability score indicates the likelihood that information derived using the Caller ID information has occurred or should occur given the carrier metadata received with the inbound call. The one or more probability scores be used to generate a risk score for the current call that indicates the probability of the call being valid (e.g., originated from a verified caller or calling device, non-fraudulent).

A computer-implemented method for analyzing call interactions in an interactions database by a Proactive Fraud Exposure (PFE) engine is provided herein. The computer-implemented method may generate a voiceprint for each call interaction; (ii) use a machine learning technique to group the call interactions into one or more clusters based on respective voiceprints in the voiceprints database; (iii) store the one or more clusters; and (iv) rank and classifying the one or more clusters to yield a list of potential fraudsters. The computer-implemented method may further transmit the list of potential fraudsters to a user to enable the user to review said list of potential fraudsters and to add fraudsters from the list to a watchlist database.

Systems and methods for detecting indications of a scam caller are disclosed. Call data, such as call audio, is received and used to create a training dataset. Using the training dataset, a machine learning model is trained to detect indications of a scam caller in a phone call. An Interactive Voice Response (IVR) model is trained or configured, using voice samples of speech of a subscriber of a telecommunications service provider, to simulate speech and conversation of the subscriber. A conversational agent is generated using the IVR model and the trained machine learning model. The conversational agent receives a phone call, engages a caller in simulated conversation, and detects indications of whether the caller is a likely scam caller. If the caller is determined to be a likely scam caller, an alert can be generated and/or the call can be disconnected.

One embodiment provides a method, the method including: receiving, at an information handling device, an active communication; determining, using a scam detection system, the active communication is received from a scamming entity; transferring, using the scam detection system, the active communication to an automated conversation agent; and interacting, using the automated conversation agent, with the scamming entity.