A global insurance carrier struggled with inefficient document processing, leading to high error rates, operational costs, and delays in underwriting. Quantiphi deployed Dociphi, an AI-powered Intelligent document processing platform, to streamline submission intake with intelligent classification, real-time data ingestion, and patented ML techniques. The solution improved processing accuracy to over 90%, reduced underwriting time, and automated 65+ distinct document types across 13 business lines, enabling faster, more efficient operations.

About the Client

A leading U.S.-based insurance provider with a long-standing presence in the industry sought to enhance efficiency in managing high volumes of document submissions. Specializing in multiple lines of commercial and personal insurance, the client required an advanced automation solution that could integrate seamlessly into their existing ecosystem while ensuring high accuracy and compliance in document processing.

Problem Statement

The client faced significant inefficiencies in handling document submissions due to the diversity of templates and formats, making data extraction a complex and time-consuming task. The manual processing of these documents was highly error-prone, leading to increased operational costs and compliance risks. Additionally, the lengthy turnaround time negatively impacted underwriting decisions, ultimately resulting in potential customer attrition.

Challenges

High Error Rate: Manual document processing was prone to inaccuracies, requiring extensive rework.
High Operational Cost: Processing large volumes of documents manually was resource-intensive and costly.
Long Turnaround Time: Delays in document handling impacted underwriting efficiency and customer experience.

The Solution

To address these challenges, Quantiphi deployed Dociphi, an AI-powered intelligent document processing platform, designed to enhance accuracy, efficiency, and integration within the client’s workflow.

Key Capabilities of the Solution:

Patented ML Techniques: Advanced machine learning algorithms enabled the accurate extraction of structured and unstructured data from various document formats.
Seamless Submission & Integration: Real-time data ingestion from multiple sources, including APIs, emails, and SFTP.
Smart Classification & Data Extraction: AI-powered classification and extraction for various document types and templates.
User-Friendly Interface: A sleek UI that facilitates data comparison, triangulation, enrichment, and validation.

Key Features of Submission Intake Automation:

Identification and Classification: AI-driven classification of policy documents across different business lines.
Bounding Box Highlighting: Visual representation of extracted values for easy validation.
Comprehensive Data Extraction: Automated extraction of tables, key values, hierarchical structures, and conceptual data.
Human-in-the-Loop Validation: Ensuring precision and consistency through manual review when necessary.
Data Enrichment: Augmenting extracted data using external sources like Dun & Bradstreet (D&B) and Google Maps API.
Downstream Connectivity: Output format aligned with downstream processing requirements for seamless integration.

Results & Business Impact

The implementation of Dociphi’s AI-powered solution led to significant improvements in operational efficiency and accuracy:

Classification and Extraction of 65+ Distinct Document Types across 13 Lines of Business (LOBs), including Marine, Commercial Property, Commercial Auto, General Liability, and Cyber.
Over 90% Classification and Extraction Accuracy, reducing manual effort and improving compliance.
10% Reduction in Underwriting Time, accelerating decision-making.
86% Extraction Accuracy from Handwritten Samples, ensuring comprehensive data capture even from challenging inputs.

Conclusion

By implementing Quantiphi’s AI-powered submission intake automation, the client significantly improved the efficiency, accuracy, and cost-effectiveness of their document processing. The seamless integration of Dociphi within their existing ecosystem enabled faster underwriting decisions, reduced operational costs, and enhanced customer experience.