Reducing Manual Call Audits Using GenAI for Insurance Quality Assurance

Industry Context

Insurance call centers operate under compliance mandates and service quality benchmarks. Traditional sampling-based audits leave large portions of customer interactions unreviewed, limiting visibility into systemic risk and performance gaps.

Manual auditing models do not scale with call volume growth.

Business Problem

The client relied on human QA teams auditing a limited subset of calls.

This approach led to delayed agent feedback, inconsistent evaluation criteria, and incomplete compliance visibility across customer interactions.

Ganit was engaged to design and deploy an automated call intelligence system capable of evaluating all customer interactions while aligning with compliance and quality frameworks.

The objective was to move from selective auditing to full-spectrum evaluation without expanding QA headcount.

We reframed call auditing as a signal extraction and structured evaluation problem.

Comprehensive Speech Transcription Layer — All calls were transcribed to eliminate sampling dependency and enable full coverage.

Signal and Intent Extraction — Conversational markers, compliance phrases, and behavioural signals were identified to create structured evaluation inputs.

GenAI-Based Quality Scoring — Calls were assessed against standardized quality parameters, replacing subjective scoring with consistent evaluation logic.

Supervisory Insight Dashboards — Aggregated insights enabled trend analysis at agent and team levels, supporting faster intervention.

Automation focused on consistency and coverage rather than novelty.

Manual call audits were reduced by 90% while achieving 100% call coverage.

Operational impact included:

• Faster and more consistent agent feedback
• Improved compliance monitoring visibility
• Reduced QA operational overhead
• Structured quality metrics across the organization

The transformation shifted QA from reactive sampling to continuous evaluation.