Automated Compliance Evaluation Using Machine Learning Context Detection
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Solution Overview
Problem
Evaluating contact center agents is challenging due to the repetitive nature of their work, making it difficult for supervisors to recognize training issues and ensure compliance, as manual review processes are inefficient and prone to missing discrepancies in a high volume of interactions.
Innovation Solution
An automated evaluation process using machine learning to identify context within customer-agent interactions and determine if compliance statements are provided, employing a compliance model trained on relevant phrases and contexts to efficiently assess agent performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review process is used to evaluate agent compliance, then supervisor can exercise judgment and context understanding, but the process is inefficient and prone to missing discrepancies in high volume interactions
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated machine learning system that uses natural language processing to analyze agent compliance. The system automatically processes transcripts and audio recordings, eliminating the need for manual listening and note-taking while maintaining comprehensive review coverage across high volumes of interactions.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between the raw interaction data and the compliance evaluation. The model processes the content through trained algorithms, identifying compliance issues that would be difficult for human supervisors to detect manually, thus bridging the gap between data volume and evaluation accuracy.
2Measurement precision
If supervisor manually processes high volume of calls, then can identify compliance issues, but repetitive nature of work makes it difficult to recognize training issues as they arise
Solution Approach 1:
The patent enables the evaluation system to be self-service by automatically processing and analyzing compliance data without requiring continuous manual intervention. The machine learning model continuously monitors interactions, automatically identifying compliance issues and generating reports, thereby freeing supervisors from repetitive manual tasks while maintaining precise detection capabilities.
Solution Approach 2:
The patent implements continuous feedback mechanisms where the system automatically detects compliance issues in real-time or near-real-time and provides feedback to both the system and supervisors. This enables timely identification of training issues as they arise, allowing for immediate intervention and correction without waiting for manual review cycles.
3Productivity
If automated machine learning system is used to review content, then efficiency and consistency are improved, but system must accurately identify context where compliance statement is required
Solution Approach 1:
The patent applies preliminary action by pre-training the machine learning model on extensive datasets of compliant and non-compliant interactions. The model learns to recognize patterns, contexts, and scenarios where compliance statements are required before actual evaluation occurs. This pre-learning enables the system to accurately identify compliance requirements in real-time without requiring complex real-time analysis.
Solution Approach 2:
The patent utilizes parameter changes by adjusting and optimizing the machine learning model's detection parameters and thresholds to match specific compliance requirements. The system can be fine-tuned to recognize different types of compliance statements and contextual nuances, allowing it to adapt to varying compliance standards while maintaining high processing speed and accuracy.
Data Source
AI summary
Aspects of the present disclosure relate to evaluating a contact center agent using an automated evaluation process that employs aspects of machine learning to review pieces of content, identify context within a piece of content where a compliance statement is required, and determine if a compliance statement was given by the agent. In some embodiments, a compliance model is trained and utilized to recognize context within the customer-agent interaction indicating that a compliance statement should be given by the agent. The presence or absence of a compliance statement in the piece of content may then be evaluated by the model and reported to the contact center supervisor. The automated nature of the invention efficiently and effectively reduces the unnecessary randomness introduced by a manual review process while providing improved assurance that compliance requirements are consistently provided during customer interactions.