A leading health insurance provider specializing in retail and group health coverage, overseas medical insurance, and personal accident policies. With a pan-India presence and a network of 7K+ hospitals, the company serves millions of customers, pioneering innovative insurance solutions and industry-first offerings.

The client’s non-STP underwriting process relied on manual workflows, causing delays, inefficiencies, and high operational costs. Key challenges included:

• Manual proposal reviews: Underwriters spent excessive time reviewing medical documents, slowing policy approvals.
• Inconsistent risk classification: The lack of automation led to inaccurate risk categorization, increasing underwriting discrepancies.
• High operational costs: Manual document handling and risk assessment required extensive resources, limiting scalability.

These inefficiencies increased loss ratios, delayed policy issuance, and impacted business expansion efforts.

Turing developed an AI-driven underwriting engine, integrating OCR, LLM-powered risk assessment, and intelligent document processing to automate underwriting workflows.

• Medical entity extraction: Utilized Azure OpenAI’s LLM to extract and categorize medical conditions from unstructured documents.
• Risk classification engine: Implemented AI-powered categorization into Red, Amber, and Green risk levels based on underwriting heuristics.
• Intelligent document processing: Deployed OCR-based data extraction, digitizing medical records for faster decision-making.
• Automated pricing decisioning: Introduced AI-assisted pricing models, reducing dependency on manual assessments.
• User-friendly interface: Developed a Streamlit-based UI, enabling seamless document uploads and real-time underwriting summaries.

• 50% faster underwriting processing, reducing manual workload and accelerating policy issuance.
• 70% of non-STP proposals automated, freeing underwriters to focus on high-risk cases.
• 60% more accurate risk classification, minimizing human errors in risk assessment.
• 30% cost reduction, lowering manual processing expenses and improving operational efficiency.