Enterprise AI
AIG's Underwriting by AIG Assist Achieves 30-40% Gains in Lexington Insurance
The insurance company's multi-agent LLM deployment has automated commercial submission triage, shortening review cycles and increasing quote and bind volumes across eight lines of business.
Underwriting by AIG Assist is AIG's multi-agent system powered by Anthropic Claude LLMs and Palantir Foundry that automates the ingestion, risk evaluation, and prioritization of commercial insurance submissions.
Executive Summary
AIG operates in the insurance sector and has implemented Underwriting by AIG Assist to handle commercial underwriting triage for its Lexington Insurance subsidiary and other lines.
The AI system deploys large language models from Anthropic and ontology tools from Palantir to handle submission processing, risk scoring, and ranking.
Quantified outcomes include a 30 percent increase in quoted submissions, a 55 percent reduction in time to quote, and an approximately 40 percent increase in submissions bound in the Lexington middle market property line, according to the Q1 2026 earnings call transcript.
What challenges did AIG face with manual commercial underwriting triage?
Prior to the deployment, commercial submissions required seven distinct manual steps for review and processing.
This approach limited the volume of submissions that could be evaluated thoroughly and extended turnaround times to between three and four weeks.
Only a filtered subset of submissions received full attention due to resource constraints in the underwriting teams.
How does the multi-agent LLM architecture of Underwriting by AIG Assist operate?
The system utilizes a multi-agent architecture with specialized agents responsible for different stages of the workflow.
These include agents for ingestion of submissions, extraction of approximately 100 attributes, risk evaluation against the company's appetite and guidelines, pricing benchmarking, and synthesis of information for prioritization.
Integration with Palantir Foundry provides the ontology for structured data handling while Anthropic's Claude models power the language processing capabilities.
What performance improvements has AIG documented following the rollout?
In early deployments, the turnaround time for submissions decreased from three to four weeks to less than one day.
This change enabled the review of 100 percent of applicable submissions rather than limiting analysis to a subset.
The submit-to-bind ratio in Lexington Middle Market Property improved by 35 percent after the rollout, per the AIG 2025 Annual Report.
| Metric | Before | After |
|---|---|---|
| Turnaround Time | 3-4 weeks | Less than 1 day |
| Submissions Reviewed | Filtered subset | 100% applicable |
| Submit-to-Bind Improvement | Baseline | 35% |
| Quoted Submissions (Lexington) | Baseline | 30% increase |
| Time-to-Quote | Baseline | 55% reduction |
| Submissions Bound (Lexington) | Baseline | 40% increase |
Which lines of business have adopted the AIG Assist system?
AIG Assist has been deployed across eight lines of business including Lexington middle-market property.
Additional deployments cover areas such as Financial Lines and Private Not-for-Profit as noted in investor materials.
Lexington surpassed 370,000 submissions by the end of 2025, representing a 26 percent year-over-year increase, with an ambition to reach 500,000 by 2030.
- Ingestion agent processes incoming submissions and extracts key data fields.
- Risk evaluation agent assesses submissions against predefined risk appetite and guidelines.
- Pricing benchmarking agent compares proposed terms with market standards.
- Synthesis agent compiles findings and ranks submissions for underwriter attention.
What executive commentary has accompanied the AIG Assist results?
AIG leadership has highlighted the system's role in targeted growth areas.
In Lexington middle market property, which is an area we have targeted for growth, AIG Assist has helped deliver a 30% improvement on quoting more submissions, reduced time to quote for the underwriters by 55% and increased binding of submissions by approximately 40%.Peter Zaffino, CEO & Chairman, AIG
What are the broader implications for enterprise AI adoption in insurance?
The automation of core processes like underwriting triage demonstrates potential for workflow efficiency in regulated industries.
By handling routine analysis, the system allows underwriters to focus on higher-value decisions.
Sector peers may examine similar integrations of LLM agents with data platforms to achieve comparable productivity gains.
What does AIG plan for the next phase of its AI initiatives?
The company has indicated plans to advance the next phase of agentic AI capabilities.
This follows the initial scaling of Underwriting by AIG Assist across multiple lines.
Continued expansion aims to further enhance submission handling and decision support in commercial insurance.
Frequently asked
How has AIG Assist impacted submission turnaround times?
Submission turnaround fell from three to four weeks to less than one day. This enabled full review of applicable submissions.
In how many lines of business has Underwriting by AIG Assist been deployed?
The system has been deployed across eight lines of business. This includes Lexington middle-market property.
What growth target has Lexington set for submissions by 2030?
Lexington aims to process 500,000 submissions by 2030. It reached 370,000 by the end of 2025.
Sources
- Seeking Alpha — In Lexington middle market property, AIG Assist has helped deliver a 30% improvement on quoting more submissions, reduced time to quote for the underwriters by 55% and increased binding of submissions by approximately 40%.
- AIG — During 2025, we scaled our first AI solution, Underwriting by AIG Assist... submission turnaround fell from three to four weeks to less than one day... 100% of applicable submissions... 370,000 submissions... submit-to-bind ratio improved 35%.
- AIG / SEC — AIG Underwriter Assistance... AI extracts submission data... analyzes submissions... synthesizes and summarizes automatically... analyzes risk factors and reprioritizes submissions... In production in Financial Lines…