Portfolio Accelerator · Insurance · Amazon Web Services
AWS Underwriting Risk AI: Insurance Underwriting Automation Built on Amazon Web Services
A documented AWS architecture for submission triage, pricing models, and catastrophe exposure analytics, built for commercial lines underwriting.
problem
The Problem: Manual Underwriting Can't Keep Pace With Risk Complexity
A commercial-lines underwriter reviewing ACORD forms, loss runs, and statements of values by hand can't move at the pace of submission volume, and pricing models built in spreadsheets don't scale to catastrophe-exposed portfolios. Carriers and MGAs need submission triage, pricing, and exposure analytics that speak AWS natively rather than a point solution layered on top.
demo
The Accelerator: What We're Building on AWS
This page shows the architecture for Kriv AI's underwriting risk accelerator on AWS. In the interest of transparency: the reference-research phase is complete; the document processing pipeline, underwriting models, and dashboards are specified but not yet built. We're presenting this honestly as a blueprint.
What the Design Calls For: Submission Intake to Risk Score
The plan is for Amazon Textract to extract structured data from ACORD forms (125, 126, 130, 131, 140), loss runs, and statements of values, Amazon Bedrock to classify and triage submissions with generative reasoning over underwriting guidelines, and Step Functions to orchestrate the intake-to-risk-score pipeline with human-in-the-loop review at each decision point.
AWS Architecture: Textract, SageMaker, Bedrock, and Comprehend Working Together
The design layers Amazon SageMaker frequency and severity pricing models by line of business on top of a Property Casualty Data Model schema, uses Amazon Bedrock for risk-narrative generation grounded in underwriting guidelines, and is designed to integrate catastrophe exposure scoring (via the open-source OasisLMF framework) and FEMA flood-zone lookups for portfolio-level probable-maximum-loss analysis.
What This Is Designed to Measure
Once built, the design is intended to report submission-to-quote cycle time, document extraction accuracy, and portfolio-level rate adequacy against filed loss costs, on synthetic submission and policy data, design targets rather than measured results today.
status
What's Real Today, and What's Roadmap
Honestly: the reference-research phase is complete, 23 repositories reviewed across document processing, insurance underwriting patterns, catastrophe modeling, actuarial pricing, and AWS orchestration. The document-processing pipeline, underwriting and pricing models, catastrophe exposure analytics, and dashboards are all specified in detail but not yet built and exercised on synthetic data.
differentiation
Why an AWS Implementation Partner, Not Just a Model Vendor
Most underwriting AI vendors sell a single point capability, document extraction, or pricing, or fraud scoring, in isolation. This architecture is designed to connect submission intake, pricing, and catastrophe exposure into one AWS-native pipeline, so a carrier gets one governed system instead of three vendor integrations.
governance
Governance, Explainability, and Regulatory Fit
The design targets the NAIC Model Bulletin's expectations for explainable underwriting AI, with human-in-the-loop review, confidence scoring on every Bedrock-generated risk narrative, and structured output validation built in, rather than a black-box score handed to an underwriter.
engagement
How Kriv AI Delivers This for Your Carrier or MGA
A Big 4 underwriting-modernization engagement typically opens with a discovery workshop and a staffing plan. A scoped Kriv AI engagement builds this architecture out against your real ACORD forms and policy data, carrying the same governance and explainability design through from day one.
Straight answers
Frequently asked questions about AWS Underwriting Risk AI: Insurance Underwriting Automation Built on Amazon Web Services
Is the underwriting risk AI accelerator built and running on AWS today?
No, and we want to be direct about it. The reference-research phase is complete, but the document processing pipeline, underwriting models, and catastrophe exposure analytics are specified but not yet built and exercised on synthetic data.
What AWS services does the architecture use?
Amazon Textract for ACORD form and loss-run extraction, Amazon Bedrock for submission triage and risk-narrative generation, Amazon SageMaker for frequency and severity pricing models, and Step Functions for pipeline orchestration.
Will this use real submission or policy data?
No. This accelerator, once built, will run entirely on synthetic submission and policy data. No real carrier or policyholder data is used at any stage.
How does this handle catastrophe exposure?
The design is intended to integrate the open-source OasisLMF catastrophe modeling framework and FEMA flood-zone data for portfolio-level probable-maximum-loss analysis by peril and geography.
How is this explainable for regulators?
The design targets the NAIC Model Bulletin's explainability expectations, with human-in-the-loop review, confidence scoring, and structured output validation built into every Bedrock-generated risk narrative.
Can Kriv AI build this out for our carrier or MGA?
Yes. A scoped engagement can build this architecture against your real ACORD forms and policy data. Contact us to discuss scope and timeline.
Ready to see the accelerator run against your data model?
Bring your requirements to a working session and we'll walk through the live system.
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