A system and method may identify a fraud ring based on call or interaction data by analyzing by a computer processor interaction data including audio recordings to identify clusters of interactions which are suspected of involving fraud each cluster including the same speaker; analyzing by the computer processor the clusters, in combination with metadata associated with the interaction data, to identify fraud rings, each fraud ring describing a plurality of different speakers, each fraud ring defined by a set of speakers and a set of metadata corresponding to interactions including that speaker; and for each fraud ring, creating a relevance value defining the relative relevance of the fraud ring.

A system and methods for contact center fault diagnostics, comprising a diagnostic engine and datastore of 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.

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).

A computer-implemented method is presented for selecting a preferred live agent from a plurality of live agents. The method includes constructing, via the processor, a human expertise matrix pertaining to each of the plurality of live agents by determining an average net promoter score (NPS) for each of the plurality of live agents for each category of a plurality of categories, and in response to a voice call by a user, determining, via the processor, a predicted human expertise on average by collectively assessing the human expertise matrix, a predicted NPS derived from a first deep neural network, and a predicted category derived from a second deep neural network. The method further includes, based on the predicted human expertise on average determined, triggering communication via the live agent communication network between the user and the preferred live agent to initiate a conversation between the user and the preferred live agent.

A system and method may identify a fraud ring based on call or interaction data by analyzing by a computer processor interaction data including audio recordings to identify clusters of interactions which are suspected of involving fraud each cluster including the same speaker; analyzing by the computer processor the clusters, in combination with metadata associated with the interaction data, to identify fraud rings, each fraud ring describing a plurality of different speakers, each fraud ring defined by a set of speakers and a set of metadata corresponding to interactions including that speaker; and for each fraud ring, creating a relevance value defining the relative relevance of the fraud ring.

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.

Systems and methods are provided for dynamic routing of an automated telephony system. The automated telephony system facilitates functions desired by a caller, via an automated call with the caller. A machine-learning analysis system extracts data from the automated call, performs machine-learning via the extracted data to identify a likely motivation of the caller associated with the automated call, and provides the likely motivation to the automated telephony system. The automated telephony system then receive the likely motivation from the machine-learning analysis system and dynamically routes the automated call based upon the likely motivation.

A secure payment agent assist (“SPAA”) feature provides assistance to a contact center agent during a transaction involving sensitive information, where the sensitive information provided by the remote party is maintained in secure manner, so that the agent is not exposed to it. The agent is assisted by being provided with a pop-up window that allows the agent to invoke a “recollect” and “cancel” function during the transaction. The pop-up window also provides information to the agent making the agent aware of the progress of the transaction as the remote party interacts with an interactive voice response (“IVR”) unit. In other embodiments, a configuration parameter allows the prompts for the payment information to be provided by the agent or the IVR.