AI use case in Sales: Automating call audits and compliance for insurance sales

The challenge:

The client was managing outbound insurance sales for a global financial services company. Their operations included:

• 120 customer agents handling over 4,800 monthly calls across multiple business lines.
• Roughly 40% of calls involved direct insurance sales, all of which required mandatory audits to ensure regulatory compliance.
• A 25-member QA team manually reviewed every compliance call against 36 parameters using a keyword-based assessment method.

While the team was diligent, the QA process faced several critical challenges:

1. Manual auditing bottlenecks: Reviewing thousands of calls each month required extensive bandwidth, creating a dependency on a large QA team.
2. Subjective assessments: Scores were heavily dependent on individual reviewer interpretation, resulting in inconsistent evaluations.
3. Missed compliance context: Important regulatory breaches were sometimes overlooked if exact phrases weren’t spoken, leaving the firm exposed to risk.
4. High operational costs: Managing a large QA workforce with full call coverage was expensive and hard to scale.

These challenges highlighted a clear need for automation, consistency, and evidence-based auditing.

Our AI solution: AI-powered regulatory compliance engine:

InteligenAI designed a custom AI system combining advanced language models (LLMs), speech analysis, and document understanding to automate the entire compliance audit process.

1. Call transcription and analysis

• Every sales call was transcribed in real time.
• AI models evaluated script adherence, tone, and agent performance.
• Compliance risks were flagged even when non-standard phrasing was used, addressing the limitations of keyword-based QA.

This eliminated human subjectivity and ensured consistent scoring across all calls.

2. Hybrid document intelligence

• Many insurance sales processes involve supporting documents, policies, and forms in various formats.
• InteligenAI deployed OCR + layout-aware AI models to extract meaningful information from documents.
• The system linked document insights with call data to provide a complete compliance audit.

3. Evidence-based audit reporting

• Compliance scores were backed by transcript excerpts, detected risks, and justification reports.
• A custom RAG (Retrieval-Augmented Generation) model generated summaries that were easy for QA managers and compliance officers to verify.
• This approach reduced review time while ensuring transparency and accountability.

Results:

Based on industry-standard benchmarks for similar AI implementations, the system delivered substantial improvements:

These gains translated into faster audits, reduced costs, and more accurate regulatory compliance, enabling the BPO to scale operations without adding staff.