Underwriting AI Solution Design for a US Insurance Brokerage

About Our Client

The Client is a US wholesale insurance brokerage and underwriting company focused on the specialty insurance market. The company uses technology extensively to streamline insurance operations and broker interactions.

Complex Specialty Underwriting Remained Largely Manual

The Client already had a mature ecosystem of automation and AI tools supporting submission processing, risk assessment, carrier appetite matching, and placement. However, analyzing high volumes of unstructured data and specialized documents, handling underwriting exceptions, and running complex compliance checks across non-standardized professional lines still required extensive manual effort.

The Client saw an opportunity to make underwriting faster and more consistent through generative artificial intelligence (AI). At the same time, the company didn’t want to introduce another underwriting platform or duplicate capabilities already available in its systems.

To decide which new AI capabilities to build and how to make them work effectively with the existing software landscape, the Client turned to professional AI consultants. ScienceSoft came across as a trusted domestic insurance AI transformation partner with experience in underwriting solutions dating back to 2012 and expertise in generative AI.

Underwriting AI Solution Design and Implementation Planning

Functional concept for an underwriting AI solution

ScienceSoft's insurance AI consultants analyzed the Client's underwriting workflows and the capabilities available across its technology ecosystem. Based on this analysis, ScienceSoft identified AI capabilities that could meaningfully improve the Client's existing underwriting automation setup. These included:

• AI-powered data extraction from documents specific to professional lines, including litigation, credentialing, and cyber risk documentation. This would free underwriters from manually locating and capturing relevant data in complex documents.
• AI-assisted validation of broker submissions and gap detection. Automated checks would help identify incomplete or inconsistent submissions before underwriter review.
• Summarization of insurance risks. Concise risk summaries would help underwriters assess submissions faster and focus on material risk factors.
• Conversational search for comparable historical risk data and risk matching. Easier access to relevant precedents would accelerate risk evaluation.
• AI-assisted drafting of underwriting documents and communications. Ready-to-review drafts would reduce routine writing effort.
• Risk-based AI recommendations for underwriting decisions. This would provide additional decision support for complex and non-standard risks.

ScienceSoft also determined which types of AI would be most suitable for different tasks. The team proposed encoder models for data extraction and classification, large language models (LLMs) for understanding and generating unstructured text, and traditional machine learning for data-driven risk assessments. This approach would limit generative AI to tasks where it could add value without introducing unnecessary risk of LLM hallucinations.

ScienceSoft documented the proposed AI solution concept and categorized its capabilities by implementation complexity and expected value for the Client's underwriting workflows. The team also defined an initial solution scope designed to deliver early payback while limiting implementation risk.

AI explainability and accuracy controls

The Client required its underwriters to see the sources behind AI outputs and recommendations. This transparency would help underwriters verify AI results and make AI-supported decisions reproducible for internal, partner, and regulatory audits.

To meet this requirement, ScienceSoft proposed implementing a curated knowledge layer based on retrieval-augmented generation (RAG). RAG pipelines would give AI access to approved proprietary knowledge, including carrier authority rules, specialty underwriting guidelines, and historical cases. This approach would ground AI outputs in controlled sources and make them relevant to the Client's business context. Adding pre-engineered prompts would require AI to cite its sources, giving underwriters direct access to the evidence behind AI recommendations. Restricting AI to approved knowledge would also help improve output accuracy.

In addition, ScienceSoft proposed to apply deterministic validation of AI outputs. A validator service would check AI-extracted data and recommendations against the Client's explicit underwriting rules and reconcile values across sources. For example, the service could verify dates and numeric ranges, flag missing required fields, and check whether cited evidence supported the extracted data. Unlike probabilistic AI reasoning, deterministic validation would produce the same result for the same input, helping prevent inconsistent interpretation of critical rules.

ScienceSoft also added an audit trail capability. A dedicated decision ledger would automatically record AI outputs, supporting evidence, rule checks, model and knowledge versions, and underwriter actions. This would allow the Client to reconstruct AI-assisted underwriting decisions for review or audit.

AI autonomy model

A critical part of the underwriting AI solution concept was a tiered AI autonomy model defining how much underwriting work AI could perform without human intervention. ScienceSoft recommended applying three tiers of AI autonomy:

• AI models would automatically extract and summarize risk information, prepare underwriting recommendations and drafts, and route the outputs to underwriters.
• AI-processed data and recommendations that successfully passed deterministic validation could be applied automatically to routine processing tasks, while low-confidence or conflicting cases would require human review.
• Binding, pricing, declining, and referral decisions would remain underwriter-only actions.

This approach would automate routine work while retaining human oversight of material uncertainties and underwriting decisions. As the AI solution demonstrated sufficient reliability, the Client could gradually expand its autonomy.

AI solution architecture design

Based on the proposed concept, ScienceSoft’s architect designed a solution architecture with several functional layers:

• The integration layer would collect and normalize data from the Client's systems and external sources.
• The knowledge layer would give AI access to approved underwriting knowledge.
• The AI services layer would interpret documents, analyze risks, and generate drafts.
• The rules and validation layer would apply predefined underwriting rules and check AI outputs.
• The recommendation layer would consolidate AI outputs and deliver them to underwriters through their existing tools.

This layered architecture would allow the Client to develop and test individual solution capabilities independently, supporting faster delivery. It would also separate AI reasoning from rule-based controls, making it easier to replace AI models or connect new data sources without redesigning the entire solution.

The central architectural idea was to place the AI underwriting solution between the Client's existing data sources and the applications already used by underwriters. AI would receive underwriting tasks from the existing workflow software and process them using data from the Client's systems. After the required validation and approval, controlled integrations would write AI outputs back to the relevant systems. This would allow the Client to introduce AI capabilities without creating a parallel underwriting system or embedding AI directly into existing systems, which could require substantial redesign.

The proposed architecture also kept AI outside the critical path to binding. If an AI service became unavailable, underwriters could continue regular operations through the Client's existing systems.

AI governance and security framework

As part of the AI solution design, ScienceSoft mapped AI governance controls that would help the Client maintain oversight of the solution as it evolved. These included keeping an inventory of AI models, prompts, rules, and knowledge assets; monitoring AI performance and underwriter overrides; controlling and testing changes; maintaining solution documentation; and running periodic AI output reviews to identify performance drift.

The proposed controls would also help the Client align its AI governance practices with the NAIC AI Model Bulletin and the NYDFS guidance on AI use in insurance underwriting.

To mitigate AI-specific security risks, ScienceSoft designed the solution architecture with strict limits on the data and functions available to AI and dedicated permission-based access controls. The AI would connect only to approved data sources and tools required for underwriting tasks and would not have unrestricted write access to the Client's core systems. Therefore, an incorrect or manipulated AI response could not independently change a data record or trigger a sensitive action.

To protect against prompt injection, ScienceSoft proposed implementing content isolation pipelines. The AI solution would treat all content received from brokers and other external sources as untrusted and separate it from the instructions controlling the AI. This way, malicious instructions embedded in a submission could not compromise the AI behavior.

AI implementation plan

ScienceSoft's project manager prepared a high-level implementation roadmap for the underwriting AI solution, including initial resource, time, and cost estimates. This would allow the Client to assess the required investment and plan resources early.

The project manager recommended starting with a pilot covering one professional line and one carrier binder. This would keep the scope manageable and make the results easier to evaluate. Running the pilot in shadow mode would let the Client compare AI outputs with actual underwriter decisions without affecting live underwriting.

Once the AI demonstrated sufficient reliability, the Client could introduce it into live workflows in one underwriting unit and then expand it to additional lines, carriers, and tasks. This phased rollout would allow the Client to validate AI accuracy and business value before deploying the solution at scale.

Practical Blueprint for Underwriting AI Launch and Scaling

The Client received a functional concept, solution architecture, and a phased implementation plan for introducing AI into underwriting for professional lines.

The proposed functional design would help the Client focus AI investment on activities where the technology could add genuine value. The layered architecture would prevent unnecessary reengineering of existing systems and make it easier to change AI models, technologies, and data sources in the future. With the tiered AI autonomy model, the Client would be able to expand AI automation while retaining human control over sensitive underwriting decisions. Built-in controls would help reduce risks of AI inaccuracy, limited explainability, and non-reproducible outputs.

When implemented, the AI underwriting solution is expected to enable more efficient submission processing, shorter submission-to-quote cycles, and more consistent, traceable underwriting decisions.

Technologies and Tools

Azure OpenAI Service, Azure AI Document Intelligence, Azure AI Search, Azure Blob Storage, Azure Container Apps, FastAPI.