A dialing list is managed, with human intervention, by obtaining a proposed call list for to-be-called (TBC) parties from a source, which list is displayed to a call center (CC) agent who selects one or more TBC parties, which causes generation of an agent-approved (AA) call list. Outbound calls are made using the AA call list. TBC parties not selected are not called by the dialing platform. The dialing platform responsive in-bound calls and non-productive (NP) calls. The AA call list is supplemented with NP call data such that TBC parties are linked to NP call data, thereby creating a productive TBC call list. A telecom session is initiated between the CC agent and the TBC called party.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving an input from a call center agent indicating a symptom of a quality of an electronic communication between the agent and a caller. Identifying a cause of the quality of the electronic communication based on the input. And, sending instructions to adjust the cause of the quality of the electronic communication.

Disclosed in some examples, are methods, systems, and machine-readable mediums which build and utilize an artificial intelligence model to predict debtor payment timing. The past debtor payment history and other debtor information may be for a plurality of accounts over a past time period. Once the model is created, it may be used when a debtor misses a payment to determine a prediction of when the debtor will pay. The model uses characteristics of past debtors and their payment dates to predict, based upon the characteristics of the late debtor, when the late debtor will make a payment. The predicted timing may include a predicted probability for whether the payment will be made within the predicted timing. The predicted timing may be a specific date, or a window (e.g., a three-day window).

A call request is received, from a mobile device, to establish a communication with a contact center. For example, the call request may be to establish a voice call with the contact center. In response to the call request, the mobile device sends authentication factors to a cloud authentication service that the user/mobile device has previously registered with. For example, the authentication factors may include usage factors of the mobile device, such as a call history of the user on the mobile device. If the authentication factors are validated, a token is generated. The token is sent to the contact center along with the call request. The token is validated at the contact center. At this point, the contact center knows that the user/mobile device are authentic. A call is then established between the user and the contact center.

A call request is received, from a mobile device, to establish a communication with a contact center. For example, the call request may be to establish a voice call with the contact center. In response to the call request, the mobile device sends authentication factors to a cloud authentication service that the user/mobile device has previously registered with. For example, the authentication factors may include usage factors of the mobile device, such as a call history of the user on the mobile device. If the authentication factors are validated, a token is generated. The token is sent to the contact center along with the call request. The token is validated at the contact center. At this point, the contact center knows that the user/mobile device are authentic. A call is then established between the user and the contact center.

A speaker identification system (system) automatically assigns a speaker to voiced segments in a conversation, without requiring any previously recorded voice sample or any other action by the speaker. The system enables unsupervised learning of speakers' fingerprints and using such fingerprints for identifying a speaker in a recording of a conversation. The system identifies one or more speakers, e.g., representatives of an organization, who are in conversation with other speakers, e.g., customers of the organization. The system processes recordings of conversations between a representative and one or more customers to generate multiple voice segments having a human voice, identifies the voice segments that have the same or a similar feature, and determines the voice in the identified voice segments as the voice of the representative.

System and method to adjust an automatic speech recognition (ASR) engine, the method including: receiving social network information from a social network; data mining the social network information to extract one or more characteristics; inferring a trend from the extracted one or more characteristics; and adjusting the ASR engine based upon the inferred trend. Embodiments of the method may further include: receiving a speech signal from a user; and recognizing the speech signal by use of the adjusted ASR engine. Further embodiments of the method may include: producing a list of candidate matching words; and ranking the list of candidate matching words by use of the inferred trend.

A computer-implemented method for providing an objective evaluation to a customer service representative regarding his performance during an interaction with a customer may include receiving a digitized data stream corresponding to a spoken conversation between a customer and a representative; converting the data stream to a text stream; generating a representative transcript that includes the words from the text stream that are spoken by the representative; comparing the representative transcript with a plurality of positive words and a plurality of negative words; and generating a score that varies according to the occurrence of each word spoken by the representative that matches one of the positive words, and/or the occurrence of each word spoken by the representative that matches one of the negative words. Tone of voice, as well as response time, during the interaction may also be monitored and analyzed to adjust the score, or generate a separate score.

A system and methods for contact center fault diagnostics, comprising a diagnostic engine and test cases used for testing components and services in a contact center, designed to operate on a contact center with a specified test campaign, allowing a contact center's various services and systems to be tested either internally or externally in an automated fashion with specified testcases being used to specify the format and expectations of a specific test, with reports of failures and points of failure being made available to system administrators.

Disclosed in some examples, are methods, systems, and machine-readable mediums which build and utilize an artificial intelligence model to predict debtor payment timing. The past debtor payment history and other debtor information may be for a plurality of accounts over a past time period. Once the model is created, it may be used when a debtor misses a payment to determine a prediction of when the debtor will pay. The model uses characteristics of past debtors and their payment dates to predict, based upon the characteristics of the late debtor, when the late debtor will make a payment. The predicted timing may include a predicted probability for whether the payment will be made within the predicted timing. The predicted timing may be a specific date, or a window (e.g., a three-day window).