01
Submission intake for one program
Broker emails turned into structured submissions with extraction and prefill.
Insurtech / Underwriting Automation
Underwriting automation that knows when to refer
Underwriters spend hours retyping broker emails into a workbench, then looking up data the system could have fetched. Agnotic Technologies builds underwriting automation that reads the submission, prefills third-party data, applies your appetite and authority rules, and sends only the risks that need judgment to a person.
Intake
Emails and ACORD forms
Prefill
Third-party data
Referral
By authority level
Trusted by global innovators
Industry Overview
What is underwriting automation?
Underwriting automation is software that takes an insurance submission from intake to a quote, decline or referral using your rules, data and authority levels. Agnotic Technologies builds it for carriers and MGAs: broker submission extraction, third-party data prefill, appetite and referral rules, straight-through processing where allowed, and governance for any AI model used.
Industry Challenges
Why do underwriting desks stay manual?
The bottleneck is rarely the decision. It is everything before it and the record after it.
Submissions arrive as emails with PDFs, spreadsheets and scanned ACORD forms, and someone keys every field.
Underwriters look up property, vehicle or business data on separate sites and paste it into notes.
Appetite guidelines sit in a PDF, so two underwriters quote and decline the same kind of risk.
Authority limits are enforced from memory, and referrals to a senior underwriter happen by email with no record.
A model score is added without testing, and nobody can explain a decline to the broker or the regulator.
Declines based on a consumer report go out without the notice the law requires.
Decision table
Which submissions can go straight through?
A starting point we refine with your underwriting leads. Your appetite, authority levels and filed rules decide the final version.
| Submission type | Straight-through eligibility | Common referral triggers | Data sources used |
|---|---|---|---|
| Small commercial (business owners and package) | Often, inside appetite and authority | Class outside appetite, prior losses above your threshold, construction or building age outside guidelines | ACORD application data, business and property data providers, loss runs |
| Specialty and excess lines | Rarely; the system prepares the file | Most risks, by design, for underwriter judgment | Broker submission, schedules, loss runs, industry data |
| Personal auto and home | Often, where rules are filed and data is complete | Driving record or claims history hits, property characteristics, missing data | Motor vehicle records, claims history reports, property data, credit-based insurance scores where permitted |
| Life (simplified issue) | For lower face amounts within your rules | Medical disclosures, prescription or record hits, face amount above the limit | Application answers, prescription and medical record data where authorized |
Our Approach
What decides whether a risk is quoted, referred or declined?
Three layers, applied in order, each one logged with its reason.
Appetite and eligibility rules
Classes, territories, limits and exclusions as versioned rules. A risk outside appetite stops early with a reason code the broker can read.
Authority levels
Each underwriter and role has limits by line, limit and premium. Anything above them goes to the next level with the full file attached.
Model scores, reviewed by people
A model may rank or flag a risk. A person reviews every decline it recommends, and the model is tested and documented before and after release.
Capabilities
What do our underwriting automation services cover?
We build everything from intake to decision record. Rating against filed rates is on our property and casualty page, and health insurance underwriting has its own page.
Submission inbox
Broker emails and portal uploads collected, deduplicated and matched to an account.
Document extraction
Fields from applications, eForms data, schedules and loss runs, each with a confidence score.
Data prefill
Third-party data called by address, vehicle or business identity, with the source and date stored.
Appetite and eligibility rules
Versioned rules owned by underwriting, with reason codes for every outcome.
Referral and authority engine
Routing by line, limit and premium to the right underwriter, with every referral recorded.
Underwriter workbench
One screen with the submission, prefilled data, rule results and referral history.
Model governance records
Inventory, test results and version history for every model that touches a decision.
Adverse action notices
Notices generated when a consumer report contributed to a decline or a higher price.
How It Works
How does a submission move through the pipeline?
The path we build for each submission, from inbox to decision record.
Intake
Emails and uploads land in one inbox, and attachments are classified by type.
01
1
Extraction
Fields are read from applications and schedules, or taken from eForms data where the broker sends it, with low-confidence fields flagged.
02
2
Prefill
Third-party data is fetched for the insured and locations, and the source and retrieval date are stored.
03
3
Rules
Appetite and eligibility rules run, producing reason codes for each pass, refer or decline.
04
4
Route
Eligible risks go to rating for a quote; others go to the referral queue for the right authority level.
05
5
Decide and record
The decision, reason, user and any model version are stored, and an adverse action notice is generated where one is due.
06
6
Engineering Stack
Standards, data and tools for underwriting automation
Chosen around your rating engine, policy system and data providers.
Standards and data
ACORD forms and eFormsACORD P&C XMLThird-party data provider APIs
Extraction and AI
OCR and document classificationLarge language models for field extraction, with reviewModel registry
Rules
Versioned rules serviceDecision tables owned by underwritingRating engine API
Platform and security
Role-based access and SSOAudit loggingEncryption in transit and at restCloud hosting
Sizing
What sizes an underwriting automation build?
An underwriting automation build is sized by the lines and programs in scope, the submission formats brokers send, the third-party data providers, the rating engine and policy system integrations, the referral and authority rules, and whether pricing or risk models are included. Agnotic Technologies reviews real submissions against each driver with your underwriting leads before scoping.
| Driver | Question to answer before scoping | What it changes in the build |
|---|---|---|
| Lines and programs | Which lines, programs and MGA arrangements go first, and do they share one appetite guide? | Each line brings its own appetite rules, required fields and referral triggers to configure and test. |
| Submission formats | Do submissions arrive as broker emails with attachments, broker portal entries, ACORD forms or a mix? | Email and PDF intake needs extraction with human review; structured portal or ACORD data skips most of it. |
| Third-party data providers | Which providers do you license for prefill, such as property, motor vehicle, loss history or business data? | Every provider adds an API connection, field mapping, cost controls on calls and handling for no-hit results. |
| Rating engine and policy system integrations | Can the rating engine be called by API, and does the policy system accept a quote or bind request from outside? | Without APIs, the build adds a hand-off file or screen step, and straight-through quoting stays limited. |
| Referral and authority rules | How many authority levels exist, and are referral rules written down or held by senior underwriters? | Unwritten rules need workshops before they can become versioned decision tables the underwriting team owns. |
| Pricing or risk models in scope | Will the first release use models to score or price risks, or only rules and prefilled data? | Models add training data work, validation, monitoring and the AI governance records listed on this page. |
Measured Impact
What should an underwriting leader track?
Measures we build into the underwriting dashboard.
Straight through
Quoted without referral
Eligible submissions quoted with no manual step.
Referral age
Time in the queue
How long referred risks wait for each authority level.
Corrected
Fields fixed by people
Extracted fields underwriters had to change.
Reviewed
Model declines checked
Model-recommended declines with a recorded human review.
Use Cases
Where do underwriting automation projects start?
First releases we scope, each one program or line of business.
02
Small commercial prefill
Business and property data fetched before the underwriter opens the file.
03
Referral and authority engine
Authority limits enforced in software, with every referral on record.
04
Renewal pre-underwriting
Renewals checked against current rules and data before they reach a person.
05
AI underwriting governance pack
Inventory, test results and decline reviews for models already in use.
06
Underwriting automation for startups and MGAs
A first intake and rules layer for a new program on a carrier's paper.
Integrations
Which systems does underwriting automation connect to?
Data comes in from brokers and providers; decisions go to rating and the policy system.
Submissions
Broker email and portals
Distribution
Agency management systems
Prefill
Third-party data providers
Reports
Consumer reporting agencies
Quotes
Rating engine
Bind and issue
Policy administration system
Files
Document management
Reporting and model testing
Data warehouse
What we check
What governance does artificial intelligence underwriting need?
When a model touches an underwriting decision, these records should exist. We build them into the release, not after it.
Inventory
- Each model listed with purpose, owner, data and version
- Third-party models and data sources included
- The lines and states where each model runs
Testing
- Validation on hold-out data before release
- Live results compared with development results after release
- Testing for unfair discrimination where state rules require it, as in Colorado
Explainability
- Reason codes stored with every decline or price change
- A person reviews each decline the model recommends
- Broker-facing reasons written in plain language
Consumer reports
- Use of a consumer report recorded on each decision
- Adverse action notice generated when one is due
- Notice content checked against the FCRA requirements
Compliance-First Development Services Backed by Global Standards
We build secure, scalable products designed for privacy, interoperability, and regulatory readiness from day one across every sector we serve.
General Data Protection Regulation
Implement lawful consent flows, data minimization, and secure processing for global data privacy.
Service Organization Control 2
Verified controls for security, availability, and confidentiality of enterprise data systems.
Information Security Management
Adhering to the international gold standard for managing information security risks.
Our Edge
Why Global Leaders Choose Us
We combine deep technical expertise with industry-specific knowledge to deliver solutions that aren't just functional, but transformational.
Enterprise-Grade Security
We implement rigorous security protocols and compliance standards (HIPAA, GDPR, SOC2) across all industrial solutions to protect sensitive data.
High-Performance Scaling
Our architectures are built to handle massive data loads and user bases, keeping performance steady whether you're serving ten or ten million.
Accelerated Time-to-Market
Using our suite of internal tools and proven frameworks, we cut setup work so your first release reaches users sooner.
Embedded AI Integration
Beyond simple wrappers, we build deep-learning integrations and predictive analytics directly into the core of your industry-specific workflows.
Engagement Model
How does an underwriting automation engagement run?
We start with one program and its real submissions. We agree a fixed first release on the scoping call.
01
Scoping call
Lines, programs, submission volumes and the systems in place today.
02
Rules and authority mapping
Appetite guides and authority letters turned into rules with your underwriting leads.
03
Fixed-scope first release
One program from intake to quote or referral, measured against current handling.
04
Add lines and data
More programs, data sources and models on the same pipeline.
05
Governance reviews
Model tests and decline reviews repeated on the schedule your program sets.
Facts and sources
Which rules shape underwriting automation?
Short facts we design around, each linked to its primary source. States adopt the NAIC bulletin individually, so your compliance team confirms what applies where you write.
The NAIC model bulletin defines an Adverse Consumer Outcome as an insurer decision, subject to insurance regulatory standards, that adversely affects the consumer in a way that violates those standards, and expects the AIS Program to cover AI use in underwriting, rating and pricing.
Source: NAIC Model Bulletin, adopted 4 December 2023, sections 2 and 3 (1.6)Colorado's amended Regulation 10-1-1, effective 15 October 2025, sets governance and risk management framework requirements for life insurers, private passenger auto insurers and health benefit plan insurers that use external consumer data and information sources, algorithms and predictive models.
Source: Colorado Division of Insurance, SB21-169 and Regulation 10-1-1Under the Fair Credit Reporting Act, adverse action includes a denial or cancellation of, an increase in any charge for, or an unfavourable change in the terms or amount of any insurance, existing or applied for, in connection with underwriting.
Source: 15 U.S.C. 1681a(k)(1)(B)(i) (govinfo.gov, 2023 edition)A person who takes adverse action based in whole or in part on a consumer report must notify the consumer, name the consumer reporting agency that furnished the report and state that the agency did not make the decision.
Source: 15 U.S.C. 1681m(a) (govinfo.gov, 2023 edition)An ACORD eForm is an electronic fillable ACORD Form that collects data for re-use, with a consistent XML format and a unique XML tag, called an eLabel, for each form field.
Source: ACORD, eForms
Voice of success
We don't just build products; we forge lasting partnerships. See how we've helped industry leaders transform their vision into technical reality.
“I can clearly see how Agnotic has a unique way of handling end-to-end development. They are always active on quick chat and provide support quickly.”
Aaron Phelan
Founder, Benchmark
“Agnotic combines deep technical expertise with strong domain knowledge. They understand the business context, anticipate challenges, and make collaboration smooth and effective.”
John Pasmore
Founder, Latimer
“Agnotic is the best technical team we evaluated. Their engineering excellence made our work dramatically easier and allowed us to stay focused on what matters most for maternal care outcomes. They took full ownership of the technical execution, and we are always happy to continue working together.”
Kim Smith
Founder, My Lauren
Related pages
Related insurance software services
This page covers intake to decision. These pages cover rating, line-specific work and health underwriting.
Insurance software for policy and claims
Our overview of insurance platforms and portals.
Property and casualty insurance software
Rating against filed rates and state product configuration.
Workers' compensation insurance software
Payroll exposure, claim EDI and medical bill review.
Professional liability insurance software
Claims-made underwriting data for E&O and malpractice writers.
Health insurance risk assessment
Group and individual health underwriting, with its own rating limits.
MGA software development
Underwriting and referral rules enforced within each carrier's binding authority.
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Frequently Asked Questions
Underwriting automation is the whole pipeline: intake, prefill, rules, referral and records. Artificial intelligence underwriting is one part of it, where a model extracts data or scores a risk. Most of the gain usually comes from intake and rules; models are added where they are tested and explainable.
Rules can stop a risk that is clearly outside appetite, with a stated reason. Declines a model recommends go to a person for review, and any decline that used a consumer report produces the notice the FCRA requires.
The six drivers in the sizing table on this page set it. A sample of real submissions on the scoping call answers most of them, which an underwriting automation calculator cannot do.
In states that adopt it, the bulletin expects your AI program to cover models used in underwriting, rating and pricing, including vendor models. We build the inventory, testing and documentation those programs ask for; your compliance team confirms the position in each state.
The pipeline calls your rating engine through its API for eligible risks and stores the result with the submission. Rating stays where it is; we automate the work around it.
Yes. A new program usually needs intake, a small rule set and authority limits agreed with the carrier, so the first release covers those and leaves room for data and models as volume grows.
Industries