Transforming Insurance Underwriting: From 15-Day Delays to Real-Time Processing

Understanding that no off-the-shelf solution existed for this level of document variety and processing complexity, Binariks designed a comprehensive approach:

Discovery & Team Assembly

We assembled specialists who underwent rigorous external and internal interviews, ensuring expertise in both insurance domain knowledge and cutting-edge AI technologies. Our team included data scientists familiar with unstructured document processing, Azure architects, and insurance industry veterans.

Problem Framing & Solution Architecture
Through intensive discovery sessions, we identified that the client lacked labeled datasets—a critical requirement for AI model training. We developed a custom tool to select documents with varied structures, ensuring balanced training and testing datasets while maintaining data quality standards.

Phased Implementation Strategy

Rather than attempting a big-bang transformation, we structured the project in phases:

• PoC for validation (2 months)
• Data Platform MVP (9 months)
• AI Classification & Extraction MVP (7 months)
• UAT and optimization (1 month)

This approach allowed for continuous validation, risk mitigation, and iterative improvement while delivering value at each milestone.

Our technical solution addressed the core challenge through multiple integrated components:

Intelligent Document Processing Engine

• Advanced OCR + NLP pipeline processing PDFs, Word docs, Excel files, emails, and scanned images
• Document classification models fine-tuned on insurance submission data to identify quote requests, policy schedules, and other document types
• Hybrid extraction system combining rule-based logic with GPT-4 for handling variable formats and unstructured content
• Confidence-based routing automatically flagging uncertain cases for human review

Centralized Data Platform on Azure

• Multi-pattern data ingestion supporting both batch processing (Azure Data Factory) and real-time streams (Event Hub/Functions)
• Standardized data model across all submission types, enabling consistent analytics and reporting
• Enterprise data warehouse integration for advanced analytics and compliance reporting
• Operational data store (Azure PostgreSQL) backing APIs and user interfaces

AI-Powered Decision Workflows

• Automated triage system scoring submissions for completeness, appetite fit, and priority
• Risk enrichment algorithms augmenting submissions with external data sources
• Fraud detection pipeline combining rule-based checks with machine learning anomaly detection
• GenAI reasoning engine using RAG (Retrieval-Augmented Generation) with Pinecone vector search

Production-Ready Infrastructure

• Infrastructure as Code (Terraform) enabling repeatable deployments across business units
• Comprehensive CI/CD pipelines for data, ML models, and infrastructure components
• Enterprise security with zero trust architecture, RBAC, encryption at rest and in motion
• Full observability through Azure Monitor, Application Insights, and cost management tools

Technology Stack: Azure Data Factory, Azure ML, Azure OpenAI (GPT-3.5/4), Pinecone, Azure SQL, Event Hub, Logic Apps, AKS, Python, Synapse, Power BI