Implement AI-Driven Underwriting for Your Agency

Regional insurance agencies frequently rely on their AMS platforms like Applied Epic, Vertafore, or HawkSoft as core systems of record for policy and client management. While excellent for structured data, these systems are not designed for custom automation that extends beyond their predefined workflows. They struggle to ingest and interpret unstructured documents, such as a 45-page scanned PDF application, extract crucial data points, and then intelligently combine that with external information to score risk.

Consider the daily reality for an underwriter or a claims adjuster. An independent agency specializing in commercial property might receive an ACORD 125 application as a scanned PDF. The underwriter then spends 20-30 minutes manually transcribing dozens of fields into their AMS. Following this, they often navigate multiple browser tabs to check a county flood zone database, a property records portal, and a fire risk assessment tool. This data is then manually copied into a separate spreadsheet for preliminary scoring before a final decision is made. This manual workflow is not only time-consuming and expensive but introduces a high risk of transcription errors, which can lead to mispriced policies or incorrect claims routing, impacting profitability and client satisfaction. Similarly, in claims triage, manually parsing FNOL reports and assigning severity can delay critical responses.

The challenge is often compounded by legacy data environments, as seen in benefits enrollment platforms where we've encountered situations requiring the cleanup of 40-50% bad data from systems like Rackspace MariaDB. This underlying data hygiene issue means even if a tool existed, its accuracy would suffer. Many off-the-shelf insurtech products, while promising, are typically built for national carriers with standardized data feeds and vast datasets for model training. They frequently fall short for regional agencies that navigate a unique blend of state-specific forms, diverse scanned documents, and non-API data sources. The fundamental problem is that current tools are either too rigid (AMS platforms) or too generic (many point solutions), failing to orchestrate the specific and often messy data workflows inherent to regional underwriting, claims processing, or policy comparisons.

An engagement with Syntora would begin with a detailed workflow and data audit. We would review a sample of 25-50 recent applications, encompassing both approved and denied cases, to precisely map the data fields and decision rules that inform your underwriting process. This phase includes identifying every external data source your team uses, distinguishing between those with available APIs and those that necessitate browser automation for data retrieval from carrier portals or public databases. The outcome of this audit is a clear data flow map and a technical plan, ensuring alignment before any development work commences.

The core automation system would be engineered to intelligently parse and process incoming documents. For instance, the Claude API would be central to extracting structured data from unstructured sources like ACORD forms, supplemental applications, or FNOL reports, converting them into clean JSON data. We've applied similar document processing patterns using the Claude API for complex financial documents, and the same principles extend to insurance-specific forms. A Python service, likely built using FastAPI and deployed on AWS Lambda, would orchestrate this entire process. This service would efficiently manage fast, parallel API calls to data sources that offer them via libraries like httpx, while utilizing Playwright for robust browser automation against portals lacking direct API access, such as specific carrier portals or property record sites. All extracted and normalized data would be stored securely in a Supabase instance, providing a flexible and scalable database backend.

The delivered system would function as an underwriter's or adjuster's co-pilot. When a new application or FNOL report is uploaded, the system would swiftly return a complete risk summary, a preliminary score based on your specific underwriting rules, and all supporting data directly integrated within your AMS (Applied Epic, Vertafore, or HawkSoft) or CRM (Hive). This setup would typically aim to complete the entire pipeline, from document ingestion to final summary, in under 90 seconds. We would also explore integrating Workato for real-time automation, for example, to automatically route client inquiries or policy service actions (PSR, index allocation) to the correct Tier 1 or Tier 2 service team, a pattern we've successfully implemented for CRM tier-assignment in a wealth management firm using Workato and Hive. Deliverables for you would include the full Python source code, a comprehensive runbook for maintenance, and a simple monitoring dashboard. The cloud infrastructure on AWS would typically cost less than $100 per month to operate. Clients would need to provide access to historical documents, specific underwriting rule sets, and necessary credentials for AMS/carrier portal integrations.