Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, and/or computing entities for processing performance metric recommendations for an agent profile. In accordance with one embodiment, a method is provided that includes: generating an agent feature data object for the agent profile from communication data objects representing communications; processing the agent feature data object using an agent group identifier machine learning model to generate an agent group data object; identifying a top agent performer data object based at least in part on the agent group data object; generating an agent assessment data object representing performance of an agent represented by the agent profile; processing the agent assessment data object and the top agent performer data object using a comparison machine learning model to generate inferred performance gap data objects; and generating the performance metric recommendations based at least in part on the inferred performance gap data objects.

A method, device, and computer-readable medium provide for receiving, via a chatbot access channel, a chat message from a user device associated with a customer chat session; determining that the chat message includes a customer intent that corresponds to a chat flow for the customer chat session; generating one or more suggested response messages based on the chat message, wherein at least one of the one or more suggested response messages includes a previously stored chat message response corresponding to the customer intent and approved by a service agent; presenting, via a display, a transcript of a messaging sequence for the customer chat session concurrently with a user interface that enables the service agent to perform an action with respect to the one or more suggested response messages; and sending, via the chatbot access channel, a selected one of the one or more suggested response messages to the user device.

A system and method of use to train customer service agents. The training system employs intelligent systems to facilitate or enable the training of customer service agents. The training system provides training to customer service agents and tracks the progress of the customer service trainees. In one aspect, the training system emulates a customer engaging with the customer service trainee, by emulating one or both of the persona of the customer and the scenario of the customer/trainee interaction.

A system and method are disclosed for recommending a resource to a customer service representative that includes one or more databases that store data describing electronic communication between one or more customer system communication devices and one or more service center communication devices. Embodiments further include a computer coupled with one or more databases and configured to monitor communication activity to determine whether a customer service ticket has been opened between one or more customer system communication devices and one or more service center communication devices and determine a customer service representative score based on one or more customer service representative ranking factors.

A computerized-method for calculating an agent skill-satisfaction-index and utilization thereof, is provided herein. The computerized-method includes operating an Agent-Skill-Satisfaction-Index (ASSI)-scoring module. The ASSI-scoring module may include: (a) operating an interaction microservice to retrieve one or more agent's interactions which were conducted during the first preconfigured-period and related interaction-level key performance indicator (KPI)s, from a data store of interactions; (b) organizing the retrieved one or more agent's interactions in one or more groups by one or more second-preconfigured-periods; (c) checking a duration of each skill from a set of skills of an agent if it is assigned to the agent above a preconfigured-period-threshold to be marked as a related-skill; (d) for each group, calculating a skill-core based on a calculated evaluation-sum of each interaction in the group that is associated with a related-skill; and (e) calculating an ASSI-score based on the calculated one or more skill scores.

Aspects of the disclosure relate to cognitive automation-based engine processing on audio files and streams received from meetings and/or telephone calls. A noise mask can be applied to enhance the audio. Real-time speech analytics separate speech for different speakers into time-stamped streams, which are transcribed and merged into a combined output. The output is parsed by analyzing the combined output for correct syntax, normalized by breaking the parsed data into record groups for efficient processing, validated to ensure that the data satisfies defined formats and input criteria, and enriched to correct for any errors and to augment the audio information. Notifications based on the enriched data may be provided to call or meeting participants. Cognitive automation functions may also identify callers or meeting attendees, identify action items, assign tasks, calendar appointments for future meetings, create email distribution lists, route transcriptions, monitor for legal compliance, and correct for regionalization issues.

A heuristic money laundering detection engine includes capabilities to collect an unstructured data set, such as a transaction record, and detect indications of money laundering activity. By detecting money laundering activity and feeding back indications of money laundering transactions, the heuristic algorithm may continue to learn and improve detection accuracy. Such indications may include correlations to sets of transaction activity among a number of financial accounts and past indications of money laundering activity. Indications of money laundering may allow generation of audit reports for reporting to regulatory authorities.

Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, and/or computing entities for processing performance metric recommendations for an agent profile. In accordance with one embodiment, a method is provided that includes: generating an agent feature data object for the agent profile from communication data objects representing communications; processing the agent feature data object using an agent group identifier machine learning model to generate an agent group data object; identifying a top agent performer data object based at least in part on the agent group data object; generating an agent assessment data object representing performance of an agent represented by the agent profile; processing the agent assessment data object and the top agent performer data object using a comparison machine learning model to generate inferred performance gap data objects; and generating the performance metric recommendations based at least in part on the inferred performance gap data objects.

A system, method, and computer-readable medium for performing a customer service interaction estimation operation, comprising: training a customer service interaction estimation system using a training dataset of cases to provide a trained predictive model; identifying current open cases via the customer service interaction system; applying the trained predictive model to the current open cases to identify low customer experience cases; generating an estimation output relating to the current open cases, the estimation output identifying an open case subset of cases having a high risk of high effort to resolve.

Method starts with processing, by a processor, audio signal to generate audio caller utterance. Processor generates an agent action ranking score associated with the audio caller utterance and determines whether the agent action ranking score is below a minimum threshold. In response to determining that the agent action ranking score is below the minimum threshold, processor generates a transcribed caller utterance using a speech-to-text processor and generates an identified task based on the transcribed caller utterance. Using the transcribed caller utterance and a task-specific agent coaching neural network associated with the identified task, processor generates an ideal response. Processor generates a feedback result and causes the feedback result to be displayed on a display device of the agent client device. Other embodiments are disclosed herein.