Resources for Modern Underwriting Teams

Email

Convr is making it easier than ever to communicate about submissions within the Convr AI Underwriting Workbench. Now there is an email capability where Convr customers can create new messages for submissions. A user would first need to have a specific submission open within the platform to see the email functionality available to them.

Within the left-hand pane they would just need to click, “Email” then “Create New Message.”

From there the “From” section will automatically be generated with their user email and they would need to plug in a recipient email address. The subject line would also be prepopulated with the submission name.

Convr users can also upload submissions assets and additional attachments about the submission in addition to crafting a customized message about the submission.

Comments
Within the Summary screen you can now also add a “Comment” about a submission, and they can be posted anywhere into Forms, Assets, Emails, etc. to support collaboration. Additionally, you can build a thread of comments. You can also reply to your own comment or react to another user’s comment with a thumbs up, as well.

The idea is that you’re creating a record or recorded conversation allowing another user to enter the platform, to get up to speed on the submission chat and join the conversation with the addition of new comments, which will show up within the feed as well.

You can tag users too, so they receive an in-app notification and email. You can also see in-app alerts, click on them and be taken directly to where you as a user were mentioned within the submission. This global, in-app notification feature is useful if a user wants to bring a team member’s attention to a given item within a submission.

The intent is to open lines of communication between underwriting team members to ensure there is greater transparency and oversight of submissions.

Convr is invested in improving the Convr AI Underwriting Workbench user interface for customers and believes these two new communication features will enhance collaboration and visibility throughout the submission process.  
 

To learn more about Emails and Comments capabilities reach out to Convr at convr.com to book a demo.

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For most of its early history, the Artificial Intelligence (AI) that was used in commercial P&C insurance was a co-pilot. It was an always-on analyst sitting beside the underwriting team surfacing data, flagging anomalies, organizing submissions and more. It was genuinely valuable, yet it still relied on a human to make the call.

Agentic AI changes that equation entirely. It doesn't wait for a prompt or pass-back to a human for every decision. It perceives, reasons, decides, and acts autonomously, within defined parameters, at an unmatched speed and scale. With Agentic AI and the organizational shift from AI experimentation to real‑world execution, new challenges are emerging. If Agentic AI systems are making decisions and taking actions, insurance underwriting teams need to be ready. That means new roles and levels of authority need to be defined. That way, Agentic AI agents will operate within clear boundaries, stay anchored to trusted enterprise data, and scale confidently across the organization, so innovation accelerates without sacrificing governance and control.

The reason Agentic AI requires new operational control is specific to its potential independence of reasoning, decisioning and action. It’s collecting information and getting back to the underwriting team member(s) with a result or response. You're no longer just asking it a question, but giving it the autonomy to perform an action — giving it more authority to operate on your behalf.

When engaged via the Convr AI Underwriting Workbench, your organization benefits from the power of  this reasoning capability within an underwriting workflow. For example, it can act on your behalf sending emails back to a broker for more information. But better still, you benefit from the controls required to customize the workflows to your specific business and governance requirements.

What separates Agentic AI from Assistive AI

Assistive AI is reactive. If you ask it a question, you’ll get an answer. If you feed it a commercial insurance underwriting submission, then you’ll get a summary. It’s powerful precisely because it reduces cognitive load — but the human remains in the loop at every decision point.

Agentic AI is proactive. It doesn't wait to be asked or given a prompt. Given a goal — clear a referral queue, flag a declination, prepare a financial analysis — an agentic system executes the full workflow: gathering relevant data, applying business logic, taking action, and reporting the outcome back to the underwriting team.

Here’s a helpful breakdown:

To learn more about Convr’s Agentic AI capabilities and what we’re doing for customers – get a demo now or read more about it on our newly revamped website at convr.com.

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Agentic AI has the potential to change commercial insurance underwriting by allowing artificial intelligence to do more than answer questions or summarize documents. AI agents can work toward defined objectives, gather information, use approved tools, evaluate conditions, and help move underwriting workflows forward.

But giving AI more autonomy introduces a fundamental challenge.

An AI system that can act needs to understand the context behind the information it is using.

Commercial P&C underwriting is not simply a document-processing problem. Underwriters make decisions based on relationships between businesses, locations, exposures, classifications, losses, coverages, limits, appetite rules, historical information, and external risk signals.

A general-purpose AI model may be highly capable at reading language while still lacking the specialized commercial insurance knowledge required to interpret those relationships reliably.

This is why insurance-specific risk context is becoming increasingly important as carriers and MGAs explore generative AI, AI agents, and more autonomous underwriting workflows.

The question for underwriting leaders is no longer simply:

Which AI model should we use?

A more important question is:

What information and insurance context is that model grounded in when it supports an underwriting decision?

What Is Insurance-Specific Risk Context?

Insurance-specific risk context is the structured information that helps an AI system understand what commercial insurance data means and how different pieces of information relate to one another.

A commercial submission might contain:

• Named insured information

• Business descriptions

• Industry classifications

• Revenue

• Payroll

• Locations

• Property characteristics

• Vehicles

• Employees

• Loss history

• Limits

• Coverages

• Supplemental risk information

Simply extracting these values does not necessarily create underwriting intelligence.

The system also needs to understand the relationships between them.

For example, an address might represent:

• Corporate headquarters

• A mailing address

• An insured property

• A warehouse

• A manufacturing location

• A construction project

Those distinctions can materially affect the underwriting analysis.

Similarly, a loss record only becomes useful when the system understands which insured, location, exposure, and policy period it relates to.

Insurance-specific context gives AI the structure required to interpret those relationships.

Why General-Purpose AI Is Not Enough for Commercial Underwriting

General-purpose large language models are trained to understand and generate language across an enormous range of subjects.

That makes them powerful tools for:

• Summarization

• Drafting

• Search

• Question answering

• Document interpretation

• Conversational interfaces

However, fluency should not be confused with underwriting expertise.

Commercial underwriting contains specialized terminology, classifications, data structures, and relationships that may not be obvious from the text alone.

Consider a submission describing a business as:

Commercial painting contractor.

A general-purpose AI model may understand what painting is.

An underwriting system may need to understand considerably more:

• Does the business perform interior or exterior work?

• Does it work at height?

• Does it perform industrial painting?

• Does it work on bridges or infrastructure?

• Are employees using scaffolding?

• Does it subcontract work?

• Which classification is appropriate?

• Which exposures matter for the requested coverage?

These questions require insurance context.

The model needs more than a dictionary definition of the business.

It needs to understand how that business description connects to commercial P&C risk evaluation.

Convr’s current Risk Context Engine positioning reflects this distinction. The company describes its RCE as a commercial P&C-specific knowledge graph and semantic ontology designed to ground AI underwriting in structured insurance context rather than relying solely on general-purpose model inference. (convr.com)

Agentic AI Raises the Importance of Context

The need for contextual grounding becomes more important as AI takes on a more active role.

Consider two uses of AI.

AI Assistant

An underwriter asks:

Summarize the information in this submission.

The AI produces a summary.

The underwriter reviews it and decides what to do next.

AI Agent

The agent is given an objective:

Evaluate whether this submission fits appetite and determine the appropriate next workflow step.

The AI may need to:

1. Interpret the submission.

2. Identify the business.

3. Determine relevant exposures.

4. Gather additional information.

5. Compare the risk against underwriting criteria.

6. Identify missing data.

7. Determine whether referral is required.

8. Route the account.

The second scenario carries more responsibility.

The AI is not merely producing text.

It is using information to influence what happens next.

If the underlying context is wrong, the workflow decision can also be wrong.

Agentic AI Needs Grounding, Not Just Intelligence

Grounding connects an AI system to trusted information that can support its responses and actions.

For underwriting, grounding may include:

• Original submission documents

• Structured risk information

• Carrier guidelines

• Internal underwriting data

• Historical account information

• Approved external data

• Classification structures

• Authority rules

• Insurance-specific knowledge models

Without grounding, the AI may rely too heavily on general model knowledge or inference.

That creates a problem because underwriting decisions frequently depend on organization-specific information.

An AI agent should not invent a carrier’s appetite.

It should use the actual appetite criteria it has been authorized to access.

It should not guess which exposure applies to a particular location.

It should use structured information that connects the location with the relevant risk.

It should not assume a requested limit falls within an underwriter’s authority.

It should evaluate the applicable authority rules.

Grounding turns an AI model from a general source of language intelligence into a tool capable of operating within a specific underwriting environment.

What Is a Knowledge Graph in Commercial Insurance?

A knowledge graph represents information as connected entities and relationships.

Instead of storing every piece of information as an isolated field, a graph can show how information relates.

A simplified commercial risk might contain relationships such as:

Named Insured

↓

Operates

↓

Business Activity

↓

At

↓

Location

↓

Associated With

↓

Exposure

Losses, policies, classifications, and other information can also be connected to the appropriate entities.

This structure is useful for AI because commercial underwriting depends heavily on relationships.

An underwriter does not simply ask:

What addresses appear in the submission?

They need to know:

Which locations represent insured exposures?

They do not simply ask:

What losses exist?

They need to understand:

Which operations or exposures generated those losses?

A knowledge graph can help preserve that context.

Convr currently positions its Risk Context Engine around this architecture, combining a commercial P&C knowledge graph, ontology, schema, and semantic layer to create a normalized view of risk information used across its underwriting capabilities. (convr.com)

What Is an Insurance Ontology?

An ontology defines concepts and relationships within a particular domain.

For commercial insurance, that domain may include concepts such as:

• Business

• Insured

• Exposure

• Location

• Classification

• Loss

• Coverage

• Limit

• Building

• Vehicle

• Employee

The ontology helps the system understand what those concepts represent and how they relate.

This becomes particularly useful when different data sources describe the same thing differently.

For example, one source might use:

Annual sales

while another uses:

Annual revenue

The system needs to determine whether those fields represent the same concept within the underwriting context.

Similarly, multiple classification systems may describe a business using different codes or terminology.

A domain-specific ontology can help normalize those differences into a more consistent representation.

Semantic Understanding Helps Connect Fragmented Submission Data

Commercial insurance data rarely arrives through one clean database.

A submission may include:

• PDFs

• Emails

• Spreadsheets

• ACORD forms

• Loss runs

• Statements of values

• Supplemental applications

• Third-party data

The same risk may be represented differently across those sources.

A semantic layer helps connect information based on its meaning rather than relying only on identical field names.

This matters because an AI agent may need to evaluate information from several sources before determining what should happen next.

If the data remains fragmented, the AI risks treating related information as separate facts or failing to identify important relationships.

Convr’s underwriting architecture unifies structured and unstructured submission data into a normalized commercial insurance model, which then supports AI, analytics, and underwriting workflows. (convr.com)

Risk Context Can Help Detect Conflicting Information

Grounding is not only about finding information.

It can also help identify when information disagrees.

Suppose a submission contains:

Application revenue: $15 million

Financial statement revenue: $22 million

A simple extraction system might successfully extract both values.

But the underwriting problem is not extraction.

The problem is determining that there is a discrepancy that may need investigation.

The same issue can occur when:

• Business descriptions differ

• Named insureds do not match

• Locations appear inconsistently

• Classifications conflict

• Loss totals differ

• Employee counts vary between sources

An AI system with stronger contextual understanding can surface these conflicts rather than simply presenting multiple values.

That allows the workflow to pause or escalate when information needs human review.

Traceability Matters as AI Becomes More Autonomous

If an AI system only generates an internal summary, an underwriter can independently verify the information.

If the system begins influencing triage, referrals, appetite decisions, or workflow routing, traceability becomes much more important.

The user should be able to determine:

• Where the information came from

• Which source contained it

• Which data point influenced the action

• Which rule was applied

• Why the system reached its conclusion

For example, an AI agent might state:

Referral required because the requested limit exceeds configured authority.

The underwriting team should be able to inspect:

• The requested limit

• Its original source

• The applicable authority threshold

• The workflow rule

• The resulting action

That is very different from receiving an unexplained recommendation from a black-box model.

Convr’s RCE and MCP positioning emphasizes this concept by making underwriting context available to AI agents while preserving source traceability for the information returned. (convr.com)

Explainability and Traceability Are Not the Same Thing

These concepts are related but distinct.

Explainability

Explains why the system reached a conclusion.

For example:

This risk requires additional review because the exposure falls outside standard appetite criteria.

Traceability

Shows the information that supports that conclusion.

For example:

• Original business description

• Classification

• Exposure information

• Applicable appetite rule

• Source document

An AI system may produce a convincing explanation without having strong traceability.

For underwriting organizations, both matter.

The conclusion should make sense, and users should be able to inspect the evidence behind it.

Insurance-Specific Context Can Support More Consistent Decisions

Commercial carriers often have large underwriting teams operating across:

• Regions

• Offices

• Distribution channels

• Lines of business

• Experience levels

Two underwriters can interpret the same information differently.

Human judgment is an important part of underwriting, but inconsistency in routine data interpretation or guideline application can create unnecessary variability.

Insurance-specific AI can help create a more consistent foundation.

For example, the system can help ensure that:

• The same risk data is interpreted consistently

• Classifications are evaluated against the same framework

• Relevant information is surfaced systematically

• Mandatory referral conditions are checked

• Source information is preserved

The goal is not to eliminate underwriting judgment.

It is to give underwriters a more consistent information layer on which to apply that judgment.

The Underwriter Still Owns the Decision

A stronger contextual foundation does not mean every underwriting decision should become autonomous.

Commercial risks can involve nuance that is difficult to reduce to a universal set of rules.

An experienced underwriter may consider:

• Broker knowledge

• Management quality

• Loss explanations

• Risk controls

• Market conditions

• Strategic account value

• Coverage structure

• Pricing considerations

Insurance-specific AI can organize the evidence and automate repeatable checks.

The underwriter can then focus on interpreting what the evidence means.

This represents an important principle for agentic underwriting.

The more effectively technology manages context, data gathering, and repeatable workflow decisions, the more attention human underwriters can give to the areas where professional judgment creates the greatest value.

AI Grounding Is Becoming an Underwriting Infrastructure Question

As insurers evaluate AI, attention naturally goes to the models themselves.

Which model is most accurate?

Which model is fastest?

Which model can handle the largest documents?

Those questions matter.

But commercial underwriting AI also depends on what surrounds the model.

Underwriting leaders increasingly need to consider whether their AI environment can:

• Understand insurance concepts

• Preserve relationships between risk information

• Normalize fragmented submission data

• Connect to approved sources

• Apply carrier-specific rules

• Trace information to its origin

• Support controlled workflow actions

Without those capabilities, increasing AI autonomy may simply increase the speed at which uncertain information moves through the underwriting process.

With the right risk context, agentic AI has the potential to become something much more valuable: a governed decision-support layer that understands the commercial insurance environment in which it is operating.

What Insurance-Specific Context Changes in Practice

Insurance-specific context changes what AI can do with underwriting information.

A general model may be able to summarize a submission. A context-aware underwriting system can connect that information to the concepts, rules, and relationships that matter for the risk.

That can improve several parts of the workflow.

Submission Triage

The system can identify missing information, evaluate whether a submission appears to fit appetite, and determine whether it should proceed, stop, or be escalated.

Risk Enrichment

External information can be connected to the correct insured, location, operation, or exposure rather than added as disconnected data.

Guideline Application

Carrier-specific rules can be evaluated against structured risk information instead of relying on a model to infer what the organization considers acceptable.

Referral Preparation

The system can identify the relevant trigger, gather supporting evidence, and prepare information for human review.

The more active AI becomes in the workflow, the more important this context becomes.

Context Can Help Reduce Hallucination Risk

One of the best-known risks of generative AI is hallucination, where a model produces information that sounds plausible but is unsupported or incorrect.

In underwriting, a model should not invent:

• A business classification

• A loss event

• A property characteristic

• An appetite rule

• An authority threshold

Grounding helps reduce this risk by directing the AI toward trusted underwriting information.

It does not remove the need for governance or human review. It gives the model a stronger evidence base.

For underwriting leaders, the key question is not whether AI can produce a convincing answer.

It is whether the answer can be tied back to trusted commercial insurance data and the rules governing the decision.

Source Lineage Supports Better Governance

Source lineage preserves the connection between a data point and where it originated.

If an underwriter reviews a location-level exposure, they may need to know whether the value came from:

• The application

• A statement of values

• A prior policy record

• An external data source

• A manually entered field

If sources disagree, lineage helps the underwriter understand the conflict.

As AI agents become more involved in referrals, routing, appetite checks, and other workflow decisions, source lineage also makes those actions easier to review and audit.

Context Should Travel With the AI Workflow

Insurance-specific context should not exist inside one isolated application.

Carriers may use several AI models, assistants, and agents across underwriting.

A stronger approach is to make trusted risk context available wherever approved AI workflows need it.

That could include:

• Underwriting workbenches

• Internal AI assistants

• Agentic workflows

• Portfolio analytics

• Decision-support tools

If several AI systems are evaluating the same account, they should not each reconstruct the risk independently from fragmented source documents.

A shared contextual layer can provide a more consistent understanding of the insured and its exposures.

Model Flexibility Matters

The AI model landscape is changing quickly.

Carriers may use different models for different tasks or change providers as capabilities improve.

The commercial insurance context, data relationships, underwriting rules, and source lineage should remain valuable even when the underlying model changes.

The model can provide reasoning and language capabilities.

The underwriting context provides the domain-specific knowledge that makes those capabilities useful.

This creates a more durable AI architecture than tying underwriting intelligence to one model alone.

Human-AI Collaboration Improves When Evidence Is Visible

Insurance-specific context can also improve the underwriter experience.

An underwriter may ask:

Why was this account referred?

A context-aware system should be able to identify the relevant rule and the information that triggered it.

The underwriter might then ask:

Which source provided that value?

The system should be able to show the supporting evidence.

This makes AI easier to challenge, validate, and trust.

The goal is not to remove underwriting judgment. It is to give underwriters stronger information and reduce the manual work required to reach a decision.

A Practical Roadmap for Context-Aware Agentic AI

Underwriting organizations can approach agentic AI in stages:

1. Identify trusted sources for important underwriting information.

2. Normalize concepts such as insureds, locations, exposures, losses, classifications, and coverages.

3. Connect the relationships between those entities.

4. Preserve source lineage back to the original information.

5. Ground AI workflows in relevant risk context and carrier rules.

6. Define which actions agents can take and which require human approval.

7. Start with focused use cases such as triage, risk enrichment, referral preparation, or renewal review.

8. Measure accuracy, speed, overrides, and underwriter trust before expanding autonomy.

This creates a more controlled path toward agentic underwriting than attempting to automate the entire underwriting process at once.

Frequently Asked Questions

Why does agentic AI need insurance-specific context?

Agentic AI needs insurance-specific context because commercial underwriting depends on specialized concepts and relationships between insureds, locations, exposures, losses, classifications, coverages, and carrier rules. General AI may understand the language without fully understanding its underwriting significance.

What is grounding in insurance AI?

Grounding connects an AI system to trusted underwriting information, such as submission data, carrier guidelines, internal risk information, approved external sources, and structured insurance knowledge. It helps responses and actions rely on evidence relevant to the insurer’s actual environment.

What is a knowledge graph in commercial insurance?

A commercial insurance knowledge graph represents businesses, locations, exposures, losses, policies, and other entities as connected information rather than isolated fields. This helps AI understand how different data points relate to the same commercial risk.

Can insurance-specific context reduce AI hallucinations?

Grounding AI in trusted insurance data can reduce unsupported outputs by giving the model a stronger evidence base. It does not remove the need for validation, governance, or human oversight, particularly for material underwriting decisions.

Why is source lineage important for underwriting AI?

Source lineage allows underwriters to see where information originated and which evidence supported an AI recommendation or action. This helps resolve conflicting data, supports auditability, and builds trust in AI-assisted underwriting.

Give Agentic AI the Commercial Insurance Context It Needs With Convr

The future of underwriting AI will not be determined by model capability alone.

Commercial insurers need AI systems that can understand the structure of commercial risk, connect fragmented information, preserve source lineage, apply relevant underwriting context, and operate within governed workflows.

That foundation becomes even more important as organizations move from generative AI assistants toward agentic systems that can recommend or take actions.

Convr is built specifically for commercial P&C underwriting. Its Risk Context Engine provides an insurance-specific foundation for structuring, connecting, and contextualizing risk information so AI-powered underwriting workflows can operate with stronger grounding and traceability.

For carriers and MGAs exploring agentic underwriting, the priority should be more than giving AI greater autonomy. It should be giving AI the right commercial insurance context before that autonomy expands.

Explore Convr to see how its AI-powered underwriting platform can support more intelligent, traceable, and context-aware underwriting workflows.

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Summary

MSIG USA modernized their underwriting ecosystem to address fragmented legacy processes, enabling the insurer to scale specialty product offerings and improve risk evaluation through data integration and AI-driven automation. 

The insurer implemented Convr, an AI-enabled underwriting workbench, to systematize and streamline underwriting intake, clearance, and risk assessment workflows. This transformation reduced submission processing time to one hour, supported additional submission volume without increasing headcount, and enabled increases in revenue while fostering a more consistent, data-driven underwriting culture. 

MSIG USA's initiative illustrates what’s become a significant trend over the past several years, as insurers seeking to improve their underwriting processes and outcomes have shown increasing interest in modern underwriting workbench platforms. They have demonstrated how AI and automation can be leveraged not merely to digitize workflows but to fundamentally enhance underwriting quality, speed, and scalability.

Hiscox Partners with Convr AI to Drive Underwriting Excellence Through Data Accuracy

Serving more than 500,000 small business customers, Hiscox USA delivers insurance policies with a focus on the small business market.

Hiscox USA is part of the Hiscox Group, which has more than 3,000 employees across 14 countries worldwide.

The organization continually embraces new technologies, strategies and processes that best support the business and its customers.

Hiscox USA puts technical excellence at the heart of its strategy, helping to move the business forward and keep it competitive as the landscape changes.

Accurate data is key to that technical excellence.

Jim Cadieux, Head of Product and Portfolio Management at Hiscox, is leading the charge as he ensures that the company is growing efficiently and that all the different areas of the business are working together towards this goal.

Today, one of his primary areas of focus is ensuring that the data Hiscox uses for underwriting is as accurate as possible so that Hiscox can deliver a stellar customer experience while reducing risk.

"At Hiscox, we are constantly shaping an exceptional end-to-end experience for our customers and partners. Accurate data enables us to better understand our customers and help them to achieve their goals as well as our own."

Challenge: Accurate Data Necessary for Technical Excellence

Cadieux and the Hiscox team partnered with Convr AI to gain a better and more accurate understanding of the small businesses they insure.

By understanding more about these businesses, they can make better decisions as they assess risk and loss.

Accurate data is also essential for underwriting, as misleading data can lead to risky business decisions down the line.

The more accurate the data, the more protected the policyholder and insurer.

For small business owners across the United States, there is a pervasive lack of understanding of insurance.

According to the Hiscox Underinsurance Study, 83% of U.S. small business owners do not understand what a General Liability policy covers.

This lack of understanding can lead to policyholders not providing accurate data or updating their insurance when they need to.

In fact, 75% of small business owners in the United States are underinsured.

It is crucial for policyholders to provide accurate and up-to-date data to their insurance provider to ensure their business is fully protected.

If they do not, they could leave their business exposed and be financially and legally liable for any claims.

"We were looking to improve technical excellence when it came to digital underwriting – we wanted to know more about the risks and more efficiently rate policies."

Hiscox partnered with Convr AI to make the process of gaining accurate data more efficient.

Convr’s Risk 360 AI and Answers AI products, along with its promised return rate of 93%, helped Hiscox advance its goal of technical excellence and ensure that the policies they write are based on the best data possible.

Solution: Partnering with Convr

Using Convr, Hiscox is able to review tranches of renewals or new policies and understand whether self-reported data is accurate.

Having an accurate picture of individual business functions allows the company to improve rating accuracy, which is critically important when writing and renewing policies.

The Hiscox and Convr teams meet regularly to discuss data, insights and establish new goals and priorities.

Together, they have developed a set of key performance indicators to measure progress and are meeting and exceeding those goals.

Partnering with Convr has allowed Hiscox to gain a more detailed picture of the small businesses they insure.

Leveraging Risk 360 and Answers

Hiscox USA leverages two modules within Convr’s Underwriting Workbench:

• Risk 360
• Answers

Risk 360 unleashes detailed insights from the intersection of tens of thousands of data elements.

Answers provides access to available information about an applicant’s business while answering underwriting questions directly through insurance-trained AI models.

"Those data sources are just going to get better and better over time."

Creating a Better Customer Experience

Hiscox strives to create a frictionless path for customers while simultaneously improving underwriting accuracy.

Rather than requiring customers to provide increasing amounts of information, Convr helps move more of the intelligence gathering process behind the scenes.

This enables a more efficient customer journey while helping underwriters make better decisions.

"Hiscox strives to be America’s leading small business insurer. To do that, we need to have a frictionless path for customers to find us and for us to address their needs. Convr plays a role in that as we’re able to do more on the back end rather than putting it all on the customer."

The Impact of Accurate Data

For Hiscox, underwriting excellence begins with data accuracy.

Better data enables better understanding of risk.

Better understanding of risk enables more accurate pricing, improved policy decisions and stronger protection for both policyholders and insurers.

By leveraging Convr’s AI-powered underwriting tools, Hiscox has strengthened its ability to evaluate risk while continuing to improve the customer experience.

The partnership has also established a framework for continuous improvement as new data sources, insights and capabilities become available.

Underwriting Excellence Through Data Accuracy

Technical excellence is not simply about improving operational efficiency.

It is about ensuring that every underwriting decision is supported by the most complete and accurate information available.

Through its partnership with Convr, Hiscox has enhanced its ability to understand small businesses, improve rating accuracy and support a customer-first approach to underwriting.

As data quality continues to improve, the value delivered by AI-powered underwriting solutions will continue to grow.

Convr AI Underwriting Workbench

Convr AI delivers a full suite of AI-infused commercial insurance tools that support underwriting analysis and decisions.

Intake

Eliminates manual submission paperwork by ingesting, preparing and analyzing submissions for a more effective digital process.

Answers

Applies decision science to correctly answer complex underwriting questions.

Risk 360

Provides underwriters a unified view of a submission’s digital footprint to correctly classify and respond to underwriting questions.

Scores

Applies decision science to risk selection, relativity and lead scoring.

Convr is an AI underwriting and intelligent document processing workbench that drives world-class customer experiences.

It delivers premium growth, insights and efficiency for commercial P&C insurance organizations of all sizes, including many of the top 20 carriers, MGAs, brokers and reinsurers.

Convr is revolutionizing the industry through data, discovery and decisioning intelligence.

Crum & Forster Partners with Convr to Digitally Transform Submission Intake and Unleash Human Potential

You don’t get to be 200 years old without doing something right. Crum & Forster is a commercial P&C carrier with a long history of offering innovative solutions to overcome challenges. The company’s Surplus and Specialty Lines (S&S) Division provides bespoke solutions for hard-to-place risks.

As one of the country’s largest Excess and Surplus (E&S) carriers, Crum & Forster works with select wholesale brokers to provide the customized service the company is known for.

"I had a vision to streamline the submission intake process."

In 2021, the S&S Division kicked off a five-year plan to double its top-line underwriting performance. Lauren Dieterich, Senior Vice President, Head of Operations and Digital, Surplus & Specialty, recognized that to achieve that goal, her team needed to streamline a manual, labor-intensive submission intake process.

Specifically, Dieterich envisioned using intelligent document processing (IDP) to automatically pull data elements out of submission materials.

The Challenge

The S&S submission process requires more flexibility than most vendors can offer.

For example, the division deals mostly with wholesale brokers, which are typically not listed in an ACORD 125 form. Their contact information is more often buried in a long email thread featuring multiple parties.

In addition, submissions often include other documents, such as supplemental applications, driver lists or location schedules.

"I was looking for a vendor who could do more than just ACORDs."

"And I wanted a vendor who could grow with us as we could consume and leverage more and more data."

Convr Brings Crum & Forster's Vision to Life

Dieterich found exactly what she was looking for in Convr’s AI-infused commercial underwriting platform. The S&S Division selected Convr’s Intake™ to enable a more efficient process for new business submission intake.

With Intake, Crum & Forster’s S&S Division can:

• Quote faster
• Streamline its submission intake process
• Leverage data for future insights

Intake eliminates most of the data entry required to clear and prepare an underwriting file, which transforms both the underwriting and customer experience.

By using Intake’s IDP capabilities, the S&S Division can automatically ingest and organize information from nearly any structured or unstructured document.

d3 Intake enables the team to collect, analyze and retain the information from the application process. These insights can inform future pricing models, underwriting decisions or claims handling.

The flexibility of Convr’s solutions is essential for Crum & Forster. Convr took time to truly understand what Crum & Forster needed, whether being able to pull specific data points from long email threads, working with a variety of structured and unstructured documents or determining source priority when presented with multiple documents.

"We achieved our vision of a more efficient submission intake process with Convr’s Intake on the front end."

We Saw Results Within Weeks of Turning It On

"We saw results within weeks of turning it on."

Streamline Submission Intake Process

Before Intake, a team of contractors was responsible for clearing submissions, a process that required manual data entry into multiple legacy systems.

First, a submission would have to pass a clearance call, which meant combing through emails and attachments to manually enter data into a legacy system.

Next, if the submission passed clearance, then it was assigned to an underwriter. That meant navigating business rules spread across multiple tabs in a Microsoft Excel spreadsheet.

Finally, the account was triaged in a separate Excel sheet with additional required inputs.

After Intake, the S&S Division could automatically ingest submission data from structured and unstructured sources. In turn, that allowed team members to automate business rules.

Finally, those two critical capabilities made it possible for Crum & Forster to develop a new submission intake platform that virtually eliminates manual data entry.

Cut Submission Processing Time by 50%

Before Intake, the inefficient and labor-intensive submission intake process resulted in a one to two day backlog for submissions.

After Intake, the S&S Division has cut its submission processing time in half. Typically, it sees same-day turnaround on submissions.

With automatic data ingestion and a new submission intake platform, the clearance team can clear, triage and prepare underwriting files faster than before.

Redeploy Team Members to Higher-Value Roles

Before Intake, Crum & Forster’s S&S Division kept extra capacity on the clearance team to manage the backlog of submissions.

After a file had cleared submission, members of the Operations team would assemble the underwriting file.

After Intake, there was no submissions backlog even at peak activity, and no need for overtime or excess capacity.

Within a month of implementing Intake, Crum & Forster’s S&S Division was able to more strategically allocate its most important resources: its people.

Approximately 40% of the clearance team members moved into file preparation roles. In turn, 20% of the Operations team was redeployed into revenue-generating production and underwriting positions.

"The bulk of the savings came from being able to redeploy part of my Operations team into underwriting roles. Now our human resources are aligned with what’s really going to drive growth, and I could achieve those additional goals with no extra spend."

Digital Technology That Enables True Change

Crum & Forster’s S&S Division knew what it needed: a more efficient submission intake process.

It also knew how to make it happen: by leveraging IDP to automate and streamline how it was ingesting and capturing data.

"Convr provided exactly what I was looking for."

Convr’s IDP capabilities made it possible for the S&S team to automate business rules and, in turn, to develop a proprietary submission intake platform.

That platform has enabled a more streamlined intake process that reduces submission intake time by half.

Building the Foundation for Future Growth

But true digital transformation comes from using technology not just to reduce costs, but to do things differently.

Crum & Forster did just that, by offering advancement opportunities to contractors and redeploying Operations employees into critical and revenue-generating underwriting roles.

"Now we have other questions to answer."

"How do we extend the Convr relationship to use more of the data that Intake can pull out of documents?"

By leveraging Convr’s Intake and its IDP capabilities, Crum & Forster’s S&S Division has the foundation it needs to achieve its goal of doubling its top line within five years.

It has also planted the seeds for the next phase of its growth strategy.

"Convr has been instrumental in enabling our five-year growth plan."

Transform Your Organization Today

• Grow premiums
• Avoid losses
• Improve underwriting efficiency
• Quote faster
• Drive accurate pricing
• Enhance customer experience
• Augment underwriting productivity

Convr Product Suite

Intake™

Eliminates manual submission paperwork by ingesting, preparing and analyzing submissions for a more effective, digital process.

Risk 360™

Provides underwriters a unified view of a submission’s digital footprint to correctly classify and respond to underwriting questions.

Answers™

Applies decision science to correctly answer complex underwriting questions.

Scores™

Applies decision science to risk selection, relativity and lead scoring.

Executive Summary

Insights from Insurance Management on the Shifts Driving Underwriting Performance and Retention

The commercial property and casualty underwriting function is undergoing rapid transformation. As digital tools, automation, and AI reshape traditional processes, commercial property and casualty (P&C) insurance leaders are racing to modernize their teams and technology.

The recent 2025 Convr Insurance Talent and Tech Trends Survey reveals that more than 90% of insurance managers and above are actively up-training their underwriting teams in data analytics, automation, and digital underwriting. Yet despite these efforts, organizations still face significant talent and technology challenges that threaten efficiency, accuracy, and employee retention.

Key findings include:

• Employee retention risk is highest among underwriters aged 21-40 - the very demographic positioned to lead the industry forward.

• 45% of new underwriting hires request better access to technology and tools, and 42% want training on the latest systems.

• 82% of managers believe their underwriting teams struggle more than other departments to attract quality talent.

• 95% of leaders expect more underwriting tasks to be automated in the coming years, yet manual data entry remains the top barrier to speed and accuracy.

The message is clear: the next era of underwriting depends on smarter tools, simpler processes, and strategic investment in talent.

The Talent Challenge: Retaining and Reskilling the Next Generation

Insurance leaders overwhelmingly agree that underwriting talent can be challenging to find - and even harder to keep.​

The takeaway: attracting and retaining the next generation of underwriters requires a dual focus on career growth and technology empowerment.​

Technology Gaps Undermine Efficiency and Accuracy​

Understaffing and outdated technology are having measurable downstream effects on underwriting accuracy and customer experience.​

• 70% of managers believe that understaffing leads to inaccurate information in quotes.​
• 82% believe understaffing directly harms customer experience.​
• 56% cite manual data entry and collection as the number one issue slowing down underwriting operations.

In fact, managers ranked the top 10 causes of underwriting slowdowns as follows:​

The Automation Imperative

Nearly 95% of insurance leaders expect a higher percentage of underwriting tasks to be automated over the coming years. In fact, more than 80% believe that at least 25% of today's manual underwriting work could be automated.​

The potential gains are substantial. One Convr customer reported reducing quote turnaround time from 1-2 days to just nine minutes after implementing automated underwriting workflows - a testament to the power of automation and intelligent data ingestion.

For an industry still battling staffing shortages and data bottlenecks, automation represents not just efficiency - but sustainability.

Technology as a Retention Strategy

Better technology doesn't just improve productivity - it improves retention. 84% percent of insurance managers believe that upgraded tools and automation will probably or definitely reduce employee attrition.​ ​As one senior underwriting leader noted, "When we remove the repetitive, manual work underwriters can focus on what they actually trained for - risk assessment and decision-making."

Technology investments are already accelerating. 73% of companies delivered new underwriting tools to their teams in 2024, and 83% plan to deliver more in 2025.​

When asked which tools have the most impact, managers ranked the top five as:​

This hierarchy highlights a clear emphasis on data visibility, compliance, and automation - all key to modernizing underwriting at scale.

What Underwriters Need Most

Respondents indicated that their underwriting teams would benefit most from simplified access to critical resources. In rank order, they cited:

Simplifying access to this information - and integrating it into daily workflows - is essential to improving accuracy, speed, and employee satisfaction.​

Top Skills for the Modern Underwriter

Even as automation grows, the human element of underwriting remains central. Commercial P&C insurance managers identified communication, detail orientation, and decision-making as the top three skills they seek in underwriting team hires.​ ​Even as automation grows, the human element of underwriting remains central. Commercial P&C insurance managers identified communication, detail orientation, and decision-making as the top three skills they seek in underwriting team hires.​ ​These "power skills" complement automation by ensuring underwriters can interpret, validate, and communicate insights from digital tools effectively.

A Vision for a Smarter, Faster Underwriting Future​

Nearly 95% of underwriting management agrees that their company's performance could improve through greater efficiency in underwriting processes. This consensus underscores a critical opportunity: to unite people, process, and technology in a cohesive digital strategy.

The path forward involves:

Automating routine work such as data collection, entry, and validation.​ Modernizing workflows through integrated data sources and workflow management systems.​ Training underwriters on AI tools and analytics to support faster, more accurate risk assessment.​ Improving data quality and accessibility to eliminate guess work and manual searches.​ ​Investing in experience - ensuring underwriters have tools that make their jobs easier, faster, and more rewarding.

Conclusion

The commercial P&C insurance underwriting profession stands at a turning point.​ ​While challenges in talent, staffing, and technology persist, the industry's direction is unmistakable: digital transformation is no longer optional - it's a competitive necessity.​ ​As automation expands and data becomes more central to decision-making, insurers that prioritize technology, training, and employee experience will not only operate more efficiently but also build the kind of workplaces that attract and retain top underwriting talent.​ ​The future of underwriting isn't about replacing human expertise -it's about amplifying it through technology.

Convr surveyed 200 commercial property and casualty (P&C) insurance decision-makers across the U.S. to dig into the role talent and technology play in driving results.

Modernizing Underwriting:

Turning to Scores

An Age-Old Problem

How do you select the right risk and price it appropriately? That's the goal of insurance underwriting. There is a delicate balance to charging acceptable rates and maintaining profitability. This is the domain of insurance underwriters who rely on historical data and actuarial analyses to evaluate and analyze the risks.

While the science behind it is valid, little has changed over the years in the way underwriting is performed by commercial insurance underwriters. Every day, thousands of these workers clock into their jobs with the intent to manage risk. They trudge through the mire of imperfect data, old policies, predictive analytics, online information and more to get to a place where they can comfortably accept, avoid or reduce the risk to their company. And why is it so hard? Because no single source is reliable, transparent, or comprehensive for assessing current or future risk.

The very essence of the underwriter's job is about making the best decision possible for their company, and they don't want to get it wrong. An inaccurate assessment of the risks associated with writing a policy or from insufficient premium could result in negative impact to loss and combined ratios. But, needless to say, sometimes they make mistakes.

As if this pressure is not enough, the sky-high pile of submissions and staffing shortages are among the many challenges these professionals face. Plus, there are dozens of cumbersome tasks and weeding through voluminous data sets and sources. Old underwriting tools require them to manually key in submission information from incoming documents. And then, the required quality checks contribute to longer lead times and added stress to maintain their high underwriting standards. The pace of work for today's underwriting professionals is accelerating and the pressure is only getting greater.

Now more than ever, the demand for faster submission processing and improved customer experience has carriers looking for ways to speed submission to quote. Insurance providers want straight through processing (STP) and their customers want real-time answers, quotes, and binders.

Happily, there are new solutions. Convr's Scores is one way underwriters are expediting their workflow, improving speed-to-quote and feeling assured their decisioning is sound.

Learn More

Machine Learning (ML) Modeling for Scoring Risks

Since 2019, Dr. John Henry, a consultant for Convr and the data science team have been forecasting risk related outcomes one year into the future. This might not seem like a big deal but since most commercial policies have a one-year duration or less, this is enough to provide underwriters with greatly increased confidence throughout the policy term. Examples of the events ML models are trained to forecast include injuries and fatalities.

The team trains the models on recurring schedules to include most recent data, consider new data sources, and improve performance over time. And the findings are presented in an easy-to-understand format - on a scale of zero to 100 called a risk score.

Convr's patented AI technology leverages ML models that are specifically trained for the commercial insurance industry. These models navigate structured and unstructured data from thousands of data sources and more than four billion data points to verify, cross-reference and deliver critical underwriting insights in real time.

Why Scoring Risks Works

Convr's risk scoring capability is driven by the need for accuracy and efficiency. Scores upgrade underwriter capabilities by providing the advanced data they need to make better and faster decisions. Convr's Scores deliver a single number measure of risk for businesses that an insurer can look to and quickly assess the relative risk of insuring a business and prioritize submissions. Companies that return a higher score would be more risky to insure while those with lower scores would likely present less risk.

Risk scoring brings more science and speed to the art of underwriting

For example, an underwriter can quickly see that a score of 90 means only 10% of businesses are more risky, while a score of 30 means 30% of businesses are less risky. The higher the score, the higher the risk. For an underwriter, it is as simple as filling in the name and address of a business and seeing real results that can affirm decisioning.

Scoring Simplified

Scores tell the underwriter where a business is on the risk distribution. A business with a score of 100 means it is in the riskiest 1% of businesses. A business with a score of 1 means that it is in the 1% of least risky businesses.

View Risk Score Sample

Convr's Data Science team found that underwriting profit in commercial auto had been trending down for several years at the time they began working on commercial auto models. His initial research looked at commercial auto loss experience at the industry level, and preliminary loss models were functions of (mostly) many geotemporal demographic and economic variables. Additional models were trained for commercial auto litigation verdict amounts, and large individual losses.

"Ultimately, we found that modeling accidents, injuries, and fatalities at the business level in a forward-looking way resulted in the most valuable predictive model(s) for underwriters. In the years since, these models have evolved and improved through collaborating with underwriters, actuaries, and claims professionals, and using more sophisticated methodologies."

Dr. John Henry

Consultant, Convr

Source: Best's Market Segment Report; March 28, 2019

US Commercial Auto

Net Underwriting Performance

Today, Convr's commercial auto risk score modeling framework incorporates all these learnings, leverages Convr's massive data-lake that includes thousands of data sources - updated on a recurring schedule - always forecasting risk one year into the future. In 2022, Convr performed a retrospective study to validate how well our commercial auto risk scoring model was forecasting risk.

• Accidents
• Injuries
• Fatalities

Accidents

Injuries

Fatalities

The Results

The results showed a convincing (and expected) relationship between Convr's Scores and injuries and fatalities per power unit. Businesses with higher scores experienced more of these events, on average, and businesses with lower scores experienced fewer of these events, on average. The study shows clearly that insurers with access to Convr's Scores in early 2021 would have been able to make decisions about current and potential insureds that would have resulted in better loss experience in the year ahead.

In the area of commercial auto for example, customers who were writing these policies have been avoiding the most risky insureds or those with the highest scores. They have been able to prioritize best risks (businesses with lowest scores). And they have been able to charge more (less) premium for insureds with higher (lower) scores.

See Real Results with

Scores and Risk 360 AI

Increase underwriting productivity

Prioritizing and reviewing submissions is often very manual and cumbersome. With d3 Risk Score, underwriters can rapidly narrow risks within their appetite and deep dive on selected risks via d3 Risk 360 (vs. Google search and DOT/Safer reports).

Reduce underwriting operating costs

Convr's implementations have seen a material reduction in operational cost across clearance, underwriting file preparation. Automation of these steps have helped our customers lower operational costs (FTE or BPO).

Increase speed to quote

A systematic approach, with d3 Score and d3 Risk 360 reduces administrative burden on Underwriters, reduces time to quote.

See growth of premiums

Increased underwriting productivity and speed-to-quote is expected to drive increased quote ratios, resulting in increased binds/new business.

Calibrate risk selection

Utilizing d3 Risk Score and d3 Risk 360, Underwriters can leverage relevant and timely information on insureds from Convr's data-lake of 2000+ data sources; high performing d3 Risk Score ML models further inform risk selection - this typically translates to better calibration of risk and greater consistency across the underwriting team.

Pricing adequacy

By monitoring loss performance with d3 Risk Scores, customers can identify segments of their book of business where they have an opportunity to adjust pricing to better reach their target loss ratio.

"The real opportunity is to look at your current book of business and incoming submissions and make better decisions today, tomorrow and going forward."

Dr. Addison Putnam

Consultant, Convr

Dive Deeper with Risk Relativity

Through risk relativity, underwriters can learn how the risk of a business compares to the average of the population through a score. One such score is specifically focused on workplace safety. For this model, we have found that some of the significant predictor variables for risk are previous infractions committed, type of permits assigned to a company and the number of years a company has been in business.

Why Scores, Why Convr?

To recap, achieving superior underwriting performance with Scores AI and Risk 360 AI can mean a commercial insurance underwriter can make more informed decisions, faster. In this way, insurance providers can realize transformative business growth and success. But, it is important to recognize that true transformation requires more than new technology. You need to be prepared for a shift in organizational mindset and culture, as well as the skill sets and roles of underwriters themselves. This is the secret to true competitive advantage.

As technology continues to transform the insurance industry, you can lead the revolution in your organization by reaching out to Convr. Convr's AI-infused commercial underwriting platform turbo-charges underwriting with more accurate and efficient decision-making and a greatly improved user experience.

At Convr, we are your partners in defining a new and better vision for commercial P&C insurance underwriting.

AM Best TV interviewed Convr CEO John Stammen at National Association of Mutual Insurance Companies (NAMIC) in September 2024. He shared insights on the value of AI underwriting and operations and how Convr AI can streamline submission to quote by reducing data entry, research and aggregation tasks to provide a better customer experience. Take a listen and hear how agents and others are winning new business with Convr AI.

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With a rich underlying data lake, time-tested data pipeline with data orchestration, and data currency with pre-plumbed, continuous updates, Convr AI sits on a rock-solid foundation that is unrivaled in the commercial insurance space. AI is integrated into everything we do. It informs our customers' submission selection and prioritization, risk quantification, and relativity assessments leading to improved decision-making. Explore even more at convr.com.

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