Build an AI-Powered Underwriting and Risk Assessment System
AI improves risk assessment by analyzing unstructured data like inspection reports to identify hidden hazards. It improves underwriting by scoring submissions based on historical loss data, not just standard factors.
Syntora helps insurance carriers enhance risk assessment and underwriting by designing and building AI systems that analyze unstructured data. These systems utilize tools like the Claude API to extract insights from documents and historical loss data, aiming to improve the accuracy of underwriting decisions.
The scope of a build depends on the volume of submissions and the number of data sources. A system for an MGA processing ACORD forms and loss runs would be straightforward. An agency needing to parse supplemental applications, property photos, and inspection reports would require a more complex data extraction pipeline.
Syntora has experience building document processing pipelines using the Claude API for financial documents, and the same technical patterns apply to extracting data from insurance-related documents for risk assessment.
What Problem Does This Solve?
Small carriers and MGAs rely on Agency Management Systems like Applied Epic or Vertafore. These are excellent systems of record, but their automation is limited to rigid, rule-based workflows. An AMS can flag a missing field on an ACORD 125 form, but it cannot read the attached PDF inspection report to identify mentions of "frayed wiring" or "roof damage."
A commercial property underwriter might receive 30 submissions a day, each with an application, three years of loss runs, and a 20-page inspection report. They spend 15-20 minutes per submission manually reading these documents to spot red flags. This manual review creates a bottleneck. During busy periods, critical risk details are missed, leading to poorly priced policies and unexpected claims.
This problem exists because the most valuable risk indicators are buried in unstructured text and images. Off-the-shelf insurance platforms are often built for large enterprises, requiring six-figure budgets and dedicated data teams. Cheaper point solutions are often just glorified OCR that extracts text but fails to understand the context required for genuine risk assessment.
How Would Syntora Approach This?
Syntora would begin by establishing a secure connection to your data sources. This could involve direct API integration with your AMS (Applied Epic, HawkSoft) or a connection to a document store like SharePoint. Syntora would use the Claude API to parse unstructured text from PDFs, Word documents, and emails, extracting key entities such as incident descriptions, property conditions, and prior claim details. We would process a representative batch of historical documents to create a baseline training dataset.
The core of the system would be a FastAPI service managing the underwriting workflow. For each new submission, a Python function would call the Claude API to summarize documents and score specific risk factors on a 1-10 scale. Syntora would then use your historical policy and claims data to train a classification model with scikit-learn, predicting loss likelihood. This document processing to risk score pipeline would be engineered for rapid execution.
The FastAPI application would be deployed on AWS Lambda for serverless execution. This approach would lead to low monthly hosting costs, with typical projections under $50 for a carrier processing 500 submissions per month, depending on specific usage. A webhook from your email server or AMS could trigger the function automatically. The final risk score, a summary of key findings, and extracted red flags would be written back into a custom field in your AMS via API. This would integrate actionable intelligence directly into your underwriters' existing workflow.
Every decision made by the AI would be logged to a Supabase database, including inputs, generated score, and confidence level. For high-value policies or submissions exceeding a defined risk threshold, the system would automatically flag them for mandatory human review. Syntora would configure AWS CloudWatch to monitor performance, with alerts sent to Slack if the API error rate surpasses 1% or processing latency exceeds 120 seconds.
What Are the Key Benefits?
From 4-Hour Response to 12 Minutes
Our claims triage system for a 6-adjuster agency automated FNOL intake and routing, cutting initial claimant contact time by 95%.
Fixed Build Cost, Not Per-Submission
You pay for the initial system build and a minimal monthly hosting fee. No variable costs that punish you for growing your submission volume.
You Get The Full GitHub Repository
We deliver the complete Python source code, deployment scripts, and a runbook. You have full ownership and control, not a black-box subscription.
Real-Time Alerts on API Failures
Using AWS CloudWatch and Slack webhooks, we monitor system health. You get an alert within 5 minutes if a data source changes or the API fails.
Integrates Natively with Your AMS
We use the APIs for Applied Epic, Vertafore, and HawkSoft to write data back. Your team sees AI-generated insights without leaving their primary system.
What Does the Process Look Like?
System & Data Audit (Week 1)
You grant read-only access to your AMS and provide 10-20 sample documents (applications, loss runs). We map the data flow and define the risk scoring logic.
Prototype Build & Review (Week 2)
We build the core parsing and scoring engine with the Claude API and FastAPI. You receive a secure web link to test the prototype with your own documents.
Integration & Deployment (Week 3)
We connect the system to your live data sources and AMS. The AI-generated scores and summaries begin flowing into your production environment for validation.
Monitoring & Handoff (Weeks 4-8)
We monitor system performance and accuracy for one month post-launch, tuning as needed. You receive the full codebase, documentation, and a detailed runbook.
Frequently Asked Questions
- What does a custom underwriting system cost and how long does it take?
- A typical build takes 3-4 weeks. Pricing is a one-time project fee based on the number of document types and the complexity of the AMS integration. It is not a recurring subscription. We provide a fixed-price quote after a 30-minute discovery call where we review your specific workflow and documents. Book a call at cal.com/syntora/discover.
- What happens if the Claude API is down or a PDF is unreadable?
- The system is built with fail-safes. If an API call fails after three retries or a document cannot be parsed, the submission is automatically flagged and routed to a human review queue with an error note. This ensures no submission is ever lost and your team can manually intervene for the 1-2% of edge cases.
- How is this different from using a large platform like Guidewire?
- Guidewire is a full-stack core system for large carriers, often involving multi-year, seven-figure projects. Syntora builds targeted AI components that plug into your existing AMS for a fraction of the cost and time. We solve a specific bottleneck in weeks, not replace your entire infrastructure over years.
- How do you handle sensitive PII in our documents?
- We process all data within a secure AWS environment. PII is handled in-memory during processing and never stored long-term, and logs are purged after 30 days. The Claude API from Anthropic has a zero-data-retention policy, meaning they do not train models on your data. We can provide a signed Business Associate Agreement (BAA).
- How accurate are the AI-generated risk scores?
- Accuracy is benchmarked against your own senior underwriters. During the initial month, we tune the system until its recommendations agree with your experts on at least 90% of reviewed submissions. For data extraction, we target 99% accuracy. All outputs also include a confidence score to indicate when human review is advised.
- How much time is required from my team during the build?
- We need about three hours from one of your subject matter experts. This includes a one-hour kickoff to map the workflow, a one-hour session to review the prototype, and a final one-hour training and handoff session. All other development, deployment, and integration work is done independently by Syntora.
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