A U.S. based specialty managing general agent (MGA) was unable to grow its business because of inefficiencies and bottlenecks in a few business-critical processes. Coforge’s business process experts implemented an AI-driven data intelligence and reporting solution that enabled faster, more informed underwriting decisions and put them back on the path to growth.
The Challenge
As an MGA, the client is trusted to underwrite risks and bind specialty policies for other insurers, but they were limited by slow loss run review and underwriting. These critical processes involve analyzing a large volume of unstructured data to review previous losses and claims in order to evaluate risk and price the policy appropriately.
They were not meeting industry benchmarks due to manual review processes and inefficient workflows. They were also unable to monitor broker and underwriter performance in real time, making them less responsive to clients and restricting their decision-making.
Our Approach
We built an AI‑powered data extraction and normalization engine that enabled the client to quickly transform unstructured loss run documents into high-quality structured datasets ready for analysis.
Data Integration
An AI-driven data integration layer that enables scalable data ingestion and centralized storage in a MySQL repository on AWS.
Decision Enhancement
Coforge built a cloud‑hosted, human‑in‑the‑loop (HITL) enabled AI data hub on Azure, which serves as a single source of truth across all underwriting workflows.
Performance Monitoring
Real‑time broker and underwriter performance dashboards using Power BI on Microsoft Fabric.

Impact to Date
Our solution enabled faster, insight-driven underwriting decisions based on reliable structured data, as well as real-time insight into how brokers and underwriters are performing. The client is now performing up to industry standards and has experienced significant growth since implementing the solution.
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40% business growth in just 12 months
Coforge’s expertise provided the client with a scalable, AI‑ready operating model, with automated data ingestion and downstream analytics to support its future growth.
