Provided herein are methods, systems, computing devices, and/or computing entities for generating a performance metric recommendation by generating an agent group data object for an agent profile; generating an agent assessment data object for the agent profile; generating an inferred performance gap data object for the agent profile; and generating the performance metric recommendation based at least in part on the inferred performance gap data object.

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.

A heuristic engine includes capabilities to collect an unstructured data set, including document image data and correlate the image data with known valid data to detect document fraud. Notifications of fraud detection may include particular signature images, or characteristics of a document image. By feeding back indications of document fraud with correlations to past instances of fraud into the heuristic algorithm, the algorithm may learn and improve in fraud detection accuracy.

A heuristic engine includes capabilities to collect an unstructured data set and detect instances of transaction fraud in a financial account. By providing a heuristic algorithm with unstructured transaction sets and indications of particular instances of transactions that correlate with past fraudulent activity allows prevention of future occurrences of fraud. Such heuristic algorithms may learn from past indications of fraudulent activity and improve accuracy of detection of future fraud detections.

Disclosed is an AI avatar coaching system based on a free speech emotion analysis for acting for CS managers. The AI avatar coaching system includes: an AI avatar coach server generating an AI avatar coach video for practical counseling training, and providing the generated AI avatar coach video; an educated/inexperienced counselor terminal receiving and outputting the AI avatar coach video provided from the AI avatar coach server; a purchase customer terminal performing a voice call for a counseling inquiry of a purchase customer; a counselor terminal performing the voice call for a counselor to perform counseling processing for the counseling inquiry of the purchase customer; and an omni channel customer/company consulting service server setting a voice call session for the voice call between the purchase customer terminal and the counselor terminal, and transmitting a report for the counseling inquiry and the counseling processing, in order to act for counseling services for multiple selling company customers. By the AI avatar coaching system based on a free speech emotion analysis for acting for CS managers, there is an effect that a counseling video of an experienced counselor is configured to be simulated into an avatar video and provided to educated/inexperienced counselors to learn a counseling/response method and effectively train the counselors through a specific practical cases.

A system and method configured to generate a simulated caller dialog including a caller intended issue for a scenario for testing a customer service representative (CSR). A simulated caller dialog is presented to the CSR and a CSR response to the simulated caller dialog is received and includes a CSR interpretation of the caller intended issue to the simulated caller dialog. An understanding determination result based on an intent determination recognition score is generated by an intent determination recognition model is generated in response to a comparison of the CSR interpretation of the caller intended issue matching the caller intended issue in the simulated caller dialog. A CSR score is generated for the scenario based on the understanding determination result. The CSR score is recorded to a database.

A heuristic engine includes capabilities to collect an unstructured data set, including document image data and correlate the image data with known valid data to detect document fraud. Notifications of fraud detection may include particular signature images, or characteristics of a document image. By feeding back indications of document fraud with correlations to past instances of fraud into the heuristic algorithm, the algorithm may learn and improve in fraud detection accuracy.

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.

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.

A method and an apparatus for coaching call center agents is provided. The method includes analyzing a conversation of the agent with a first customer, determining a performance of the agent on at least one behavioral skill based on the analysis, generating automatically, a custom training package (CTP) based on the determined first performance, and sending the CTP for presentation on the agent device.