Discover how Quantiphi partnered with a global insurer to automate submission intake using Dociphi, achieving 95% classification accuracy and ~90% data extraction accuracy—streamlining underwriting workflows and accelerating decision-making.

About the Client

A global insurance provider offering a comprehensive suite of commercial and personal insurance solutions, including property, casualty, life, and reinsurance. With a strong international footprint and a focus on operational excellence, the client is continuously investing in digital capabilities to enhance underwriting efficiency and decision-making.

Problem Statement

The client sought to modernize their underwriting operations through automation. The objective was to implement a solution that could intelligently classify incoming documents, extract relevant data, and integrate with third-party APIs to improve accuracy and processing speed across submission intake workflows.

Challenges

Operational Inefficiencies & Manual Workloads: Underwriters were burdened with time-intensive, repetitive tasks that detracted from strategic decision-making.
Inconsistent Broker Submissions: Non-standardized formats in broker quotes led to a lack of transparency, complicating quote comparison and evaluation.
Error-Prone Processes: Manual data handling introduced a high rate of errors, requiring rework and creating operational bottlenecks.

The Solution

Quantiphi deployed its AI-powered intelligent document processing platform, Dociphi, to digitize the client’s end-to-end submission intake workflow. This solution replaced manual tasks with intelligent automation, enabling faster, more accurate underwriting processes.

Key Capabilities Implemented:

Email Ingestion: Automated intake of submission-related emails and documents in real time.
Document Analysis & Classification: AI models categorized documents into five distinct business types with high precision.
Information Extraction: Accurate parsing and extraction of 13 key business data fields, including embedded objects and structured data.
Data Validation & Enrichment: Integrated Dun & Bradstreet APIs to verify and enhance extracted information.
Intelligent Submission Triage: Evaluated submissions based on parameters such as complexity and completeness for optimal routing and prioritization.

Results and Impact Created

95% Classification Accuracy: Accurate categorization across varied document types, improving triage and handling.
~90% Extraction Accuracy: Automated extraction of critical business fields from complex and unstructured formats.
Workflow Standardization: Achieved consistent and streamlined underwriting processes, minimizing manual intervention.
Enhanced Operational Visibility: Deployed a real-time analytics dashboard to monitor process performance and enable data-driven decisions.

With Dociphi, the client not only automated routine tasks but also unlocked strategic advantages across their underwriting value chain.

Explore how Dociphi can help your organization achieve intelligent, scalable document automation.