Resources for Modern Underwriting Teams

The intersection of insights and innovation... challenge and collaboration – that's what #teamconvr found this year at InsureTech Connect (ITC) Vegas 2026, held September 27 through October 1 at Mandalay Bay in Las Vegas, NV.

Like every year we attend the convention, it was busy, busy, and busy again, with demos, conversations, new agreements and presentations. If you walked the expo floor, you were sure to find Convr on both ends. Through it all, for Convr, this year's event sponsorship further confirmed our purpose and value in the commercial P&C insurance marketplace.  

Over the course of the event, Convr team members talked with hundreds of conference goers about the efficiencies and decisioning advancements our Convr AI Underwriting Workbench brings to commercial P&C carries, MGAs/MGUs, and brokers through the practical application of AI.

But if you missed us – with so many vendors and sessions competing for attention – we still want to give you a chance to get to know us and connect.

What we do

Convr is an AI underwriting workbench and data platform that digitizes and fuses submissions with the best available data sources to surface underwriting insights, business classification, and risk scores. At ITC Vegas, we met with some of the most tech-forward people in the industry, true professionals devoted to bettering the experience for insureds and the underwriting teams that help protect them.

That's exactly what Convr's AI Underwriting Workbench is built to do. Every day, our comprehensive workflow speeds the path from submission to quote by simplifying and expediting the underwriting process, improving the experience for both underwriters and their customers along the way. With the fast return on investment our platform delivers, ITC Vegas was the perfect stage for #teamconvr to share what we bring to the table.

We showed up in force. The Convr team took part in the Datos Insights Insurance Leaders' Summit panel on Tuesday, September 29, delivered a Mic Drop presentation on the Innovation and Solutions Stage on Wednesday, September 30, hosted private demos in #MR18, and welcomed a steady stream of visitors to our purple booth, #2618, on the main exhibit floor.

Thanks to everyone who stopped by. We're already looking forward to continuing the conversations, starting new ones, and seeing what the post-conference brings! Visit us here to meet with us or book a demo now.

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Explainability becomes much more valuable when it is preserved as part of the underwriting record.

For each material AI-assisted recommendation, carriers should be able to reconstruct:

• What information was used

• Which source documents supported it

• Which underwriting rule or guideline applied

• What recommendation was generated

• Whether the underwriter accepted, changed or overrode it

• When the decision was made

This creates an evidence trail that can support internal review, governance and audit.

It also gives underwriting leaders a clearer view of how AI is being used across the organization.

Track Rule and Guideline Changes Over Time

Underwriting guidelines change.

Authority levels move.

Appetite evolves.

Referral thresholds are updated.

An explainable system should preserve the version of the rule that applied when the decision was made.

Otherwise, a reviewer looking at the account months later may see a different rule from the one the underwriter actually used.

Versioning helps answer a simple but important question:

What did the system know and what rule was in force at that moment?

That level of traceability is essential when AI becomes embedded in real underwriting workflows.

Make Overrides Visible

Underwriters will sometimes disagree with an AI recommendation.

That is not a failure.

Commercial insurance is full of exceptions, nuance and context that may justify a different decision.

The important thing is to capture the override.

A governed workflow can record:

• The original AI recommendation

• The evidence behind it

• The underwriter's final decision

• The reason for the override

That creates useful feedback for underwriting leaders and helps identify where rules, data or workflows may need improvement.

It also reinforces the role of AI as decision support rather than an unchallengeable authority.

Explainability Supports Better AI Governance

As AI use expands, carriers need confidence that underwriting decisions remain controlled.

Explainability supports that by making it easier to review:

• How data entered the workflow

• Which rules were applied

• Where human approval was required

• When exceptions occurred

• Which decisions were automated

• Which decisions were escalated

This gives governance teams a clearer view of how AI operates in practice.

Instead of relying on broad claims that a model is accurate, the carrier can inspect how individual underwriting recommendations were produced.

Avoid Black-Box Automation

The biggest risk in AI-assisted underwriting is not simply that a model could be wrong.

It is that the organization cannot tell why it was wrong.

A black-box recommendation creates operational friction because underwriters either have to trust it or recreate the analysis manually.

A traceable recommendation creates a better path.

The underwriter can review the inputs, inspect the evidence, understand the rule and decide whether the recommendation makes sense.

That is what turns AI from an isolated tool into part of a governed underwriting process.

Frequently Asked Questions

What Is Explainable AI in Underwriting?

Explainable AI connects an underwriting recommendation to the data, source documents and rules that produced it, so the underwriter can understand and verify the decision.

Why Is Source Traceability Important?

It lets underwriters confirm where a risk attribute came from and inspect the original evidence rather than relying only on an AI-generated output.

Does Explainable AI Replace Human Judgment?

No. It gives underwriters better context and evidence so they can review, accept, modify or override a recommendation with confidence.

How Can Carriers Make AI Decisions Auditable?

They can preserve source lineage, structured risk data, applied rules, rule versions, recommendations, human overrides and final decisions as part of the underwriting record.

Why Do Insurance Ontologies and Knowledge Graphs Matter?

They give AI a structured understanding of insurance concepts and relationships, helping the system connect risk data in a way that is more consistent and easier to explain.

Make AI-Assisted Underwriting More Transparent With Convr

AI delivers the most value when underwriters can trust the path behind the recommendation.

Convr helps commercial P&C carriers turn fragmented submission information into structured, connected underwriting intelligence through an insurance-specific ontology, knowledge graph and standardized risk schema.

That foundation helps create AI-assisted workflows where risk attributes can be traced back to source documents, recommendations can be tied to underwriting rules, and underwriters remain in control of the final decision.

The result is not just faster underwriting.

It is underwriting that is more transparent, reviewable and governed.

Explore Convr to see how traceable, explainable underwriting intelligence can help your team adopt AI without sacrificing the evidence, context and control that commercial underwriting requires.

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Agentic AI in commercial underwriting uses AI systems to coordinate tasks toward a defined underwriting objective. Within approved boundaries, these systems can review submissions, gather risk information, apply configured criteria, and move work to the appropriate next step. Underwriters retain responsibility for decisions that require their judgment and authority.

For commercial property and casualty (P&C) carriers and managing general agents (MGAs), the opportunity is practical: reduce the work between receiving a submission and making an informed decision.

A single account can require document review, data validation, external research, appetite checks, and referral preparation. When underwriters coordinate every step manually, valuable time disappears into administration.

Agentic workflows can connect these activities so underwriters begin their review with better information and clearer priorities. The foundation is structured insurance data, relevant risk context, and controls that define what AI can do.

The Convr AI Underwriting Workbench brings submission processing, risk intelligence, and workflow capabilities together to support this approach to commercial underwriting modernization.

How Is Agentic AI Different From Underwriting Automation and Generative AI?

Traditional automation, generative AI, and agentic AI contribute different capabilities to underwriting modernization. They can work together within the same process.

Traditional underwriting automation follows predefined rules. For example, a workflow routes a submission to a senior underwriter when the requested limit exceeds an authority threshold. This works well when the condition and response are clearly defined.

Generative AI interprets or creates content. It can summarize a loss run, extract information from documents, or draft a referral note. A user or surrounding workflow determines how that output is used.

Agentic AI coordinates steps toward an objective. An agent may determine that reviewing loss information, checking a guideline, and preparing a referral are necessary before a submission can progress. It uses available tools and permitted actions to complete that sequence.

Consider the objective: prepare this submission for underwriting review.

An agentic workflow could check whether required documents are present, identify conflicting values, gather approved external information, and route the account when the configured conditions are met. If essential information is missing, it could pause and flag what needs attention.

The distinction is the ability to coordinate work around the objective while respecting the insurer's rules.

Where Can Agentic AI Improve Commercial Underwriting?

Strong use cases combine several related tasks, reliable information, and a clear point at which a human should review the result. Submission intake, triage, risk enrichment, referral preparation, and renewal review are useful starting points.

Submission Intake and Data Validation

Commercial submissions often include ACORD forms, statements of values, loss runs, spreadsheets, supplemental questionnaires, and broker correspondence. Relevant facts may appear in several documents, sometimes with different values.

An agentic intake workflow could identify the documents received, organize the information, and evaluate whether the submission is complete enough for the next stage.

For example, an application may list annual revenue that differs from a supplemental questionnaire. Simply extracting both figures leaves the underwriter to discover the discrepancy later. A coordinated workflow could flag the disagreement, preserve both sources, and request review before the figure is used downstream.

This gives underwriting teams a clearer starting point and reduces the risk of passing unresolved data problems into later decisions.

Submission Triage and Appetite Evaluation

Underwriting teams need to decide which opportunities deserve attention first. That requires more than sorting submissions by arrival time.

An agentic triage workflow can bring together characteristics such as business operations, geography, requested coverage, limits, and loss information. It can then evaluate the account against configured appetite and prioritization criteria.

The result might be a recommendation to proceed, request more information, or refer the submission for review.

A useful distinction is whether an account falls outside appetite or simply lacks enough information to assess it. An incomplete submission may still represent a strong opportunity. Good triage makes that uncertainty visible instead of treating missing information as a definitive underwriting answer.

Risk Enrichment and Loss Review

Commercial underwriting often requires context beyond the original application. Underwriters may need to investigate business activities, locations, classifications, property characteristics, or historical losses.

An agent can help coordinate approved research and connect the findings to the account being evaluated. The information must match the correct business, location, and period.

For loss review, a workflow could organize claim information, identify missing periods, summarize patterns, and flag issues for investigation. The underwriter then evaluates what those findings mean for the risk.

This approach makes research more useful because the supporting evidence remains connected to the underwriting question. It also helps prevent an apparently relevant result from being applied to the wrong entity or exposure.

Referral Preparation and Authority Checks

Referrals can involve substantial preparation before a senior underwriter is able to act. Someone must identify the trigger, locate the relevant guideline, gather supporting information, and explain the requested exception.

An agentic workflow could assemble that material and route it to the appropriate reviewer. For example, a requested limit outside an underwriter's authority could trigger a referral package containing the exposure details, requested terms, applicable threshold, and source documents.

Authority checks should use explicit business rules and permission controls. The agent should never infer that it can approve an exception because similar accounts were approved previously.

The benefit is a more complete handoff, with the required decision still assigned to the authorized person.

Renewal Review and Material Change Detection

At renewal, underwriters need to understand what has changed since the previous policy period.

An agentic workflow could compare current submission information with historical account data and identify changes in locations, operations, exposures, requested limits, or loss activity. It could then direct attention to changes that meet defined review criteria.

For example, a business that previously operated from one location may have added a warehouse. That change could require additional property information and a fresh review of the exposure.

Organizing the comparison helps underwriters focus their investigation. An account that appears unchanged still needs to follow the insurer's renewal requirements.

Why Does Agentic Underwriting Need Insurance-Specific Risk Context?

AI needs to understand how facts relate to the insured risk before those facts can support a useful action.

An address could represent a mailing address, corporate headquarters, insured property, or temporary job site. A revenue figure might apply to one subsidiary or an entire organization. A loss amount may refer to paid losses, reserves, or total incurred losses.

These distinctions influence how information should be interpreted. Extracting a value accurately is only part of the task; the system also needs to preserve its meaning.

Insurance-specific data structures help connect businesses, locations, exposures, coverages, limits, and losses. They give AI a more reliable basis for retrieving relevant information and applying the correct workflow.

Convr's Risk Context Engine uses commercial P&C insurance semantics, a structured schema, and a knowledge graph to preserve meaning and relationships across underwriting data. These capabilities support the insurance context behind its AI Underwriting Workbench. Explore Convr's data and AI foundation.

For underwriting leaders, the practical evaluation question is: can the system explain which risk a fact belongs to, where it came from, and why it matters?

What Role Does an AI Underwriting Workbench Play?

An AI underwriting workbench brings account information, insights, tasks, and collaboration into a shared environment. Agentic capabilities can help advance work within that environment, subject to the organization's rules and permissions.

This matters because underwriting delays often occur during handoffs. A document arrives, but nobody notices it. A referral is prepared, but ownership is unclear. An account is ready for review, but it remains in the wrong queue.

A useful workbench should make the current status, responsible person, outstanding requirements, and next action visible. It should also connect with the systems needed to complete the process.

Convr combines submission processing and risk intelligence with configurable workflow automation. Its Workflow capabilities include routing, escalation controls, activity trails, and human review points where users can correct or override AI-assisted actions. Explore Convr Workflow.

When evaluating a workbench, ask to see a representative submission move through the process. Include a missing document or conflicting value. That reveals how the system handles the exceptions that determine whether automation is useful in daily underwriting.

How Should Insurers Control Agentic Underwriting Workflows?

Insurers should define an agent's permitted information sources, actions, escalation conditions, and approval requirements before expanding its autonomy.

Different activities warrant different controls. Gathering approved data or creating an internal task may be suitable for automation. Pricing exceptions, complex coverage judgments, and decisions outside delegated authority require the appropriate human review.

Four controls deserve particular attention:

• Approved information: Specify which documents, guidelines, internal records, and external sources the agent may use. Keep applicable rules current.

• Action permissions: Define what the agent can recommend, prepare, or execute, and enforce those permissions in the connected systems.

• Escalation conditions: Pause or refer work when information is missing, contradictory, outside scope, or insufficiently reliable.

• Traceability: Record the evidence considered, applicable rule, action taken, and any human review or correction.

An AI model's stated confidence should not, by itself, authorize a consequential underwriting action. Confidence measures need to be evaluated against actual performance and combined with explicit rules and review requirements.

Controls should also account for change. A revised appetite rule, updated authority limit, or new document format may affect the workflow. Assign responsibility for reviewing these changes and checking that the system continues to behave as intended.

How Can Insurers Start With Agentic AI?

Start with a narrow workflow where the objective, evidence, and escalation path are easy to define. Submission completeness or referral preparation can provide a more manageable starting point than broad autonomous underwriting.

First, document the current process. Identify where people copy information, search for evidence, wait for another team, or repeat work. Establish a baseline for handling time and errors.

Next, define the outcome the agent should achieve. “Prepare a submission for review using these required documents and checks” is more actionable than “improve underwriting productivity.”

Then test the workflow against representative accounts, including incomplete submissions, conflicting information, unusual business descriptions, and cases outside authority. Evaluate whether it recognizes when to stop as carefully as whether it completes routine cases.

Finally, introduce the workflow with a named owner and a review process. Use underwriter corrections and observed performance to decide whether to expand its scope.

This gives carriers and MGAs a practical way to build confidence while preserving accountability for underwriting decisions.

Which Metrics Show Whether Agentic AI Is Working?

Measure whether the workflow improves the quality and efficiency of underwriting work. Useful indicators include:

• Time from submission receipt to underwriting review.

• Manual handling time per account.

• Number of avoidable handoffs or repeated data entries.

• Completeness and accuracy of required information.

• Referral preparation and turnaround time.

• Incorrect routing, missed exceptions, and human overrides.

• Quote turnaround time for comparable submissions.

Review these measures together. Faster processing has limited value if reviewers must spend more time correcting the output. A lower referral rate may also be misleading if the workflow is missing legitimate escalation conditions.

Compare similar account types and investigate the reasons behind overrides. This helps separate useful automation from apparent efficiency that shifts work to another part of the process.

Frequently Asked Questions

What Is Agentic AI in Commercial Insurance Underwriting?

Agentic AI uses systems that coordinate multiple tasks toward an underwriting objective, such as preparing a submission for review. An agent can use approved information, tools, and configured rules to determine the next permitted step. Its access and authority remain controlled by the insurance organization.

How Is Agentic AI Different From Generative AI?

Generative AI creates or interprets information, such as a submission summary or referral draft. Agentic AI can use those capabilities as part of a sequence of actions. For example, it could identify that a summary is needed, prepare it, and route the account under approved workflow rules.

Will Agentic AI Replace Commercial Underwriters?

Agentic AI can take on administrative coordination, information gathering, and analysis preparation. Commercial underwriters remain responsible for the judgment and accountability assigned to their role, including complex risk assessment, exceptions, negotiation, and broker relationships. The division of work depends on the insurer's operating model and delegated authority.

Which Underwriting Workflows Are Best Suited to AI Agents?

Useful starting points include submission intake, completeness checks, appetite triage, risk enrichment, referral preparation, and renewal comparisons. The strongest candidates have a defined objective, reliable information, measurable outcomes, and clear escalation rules. Start where the workflow can be evaluated against an established human review process.

Does Agentic AI Mean Fully Autonomous Underwriting?

No. A workflow can allow an agent to gather information or route a submission while requiring human approval for consequential decisions. Autonomy can vary by task, line of business, and authority level. An insurer should define these boundaries explicitly and maintain a clear path for exceptions.

How Does Convr Support Agentic Underwriting?

Convr brings AI capabilities together with a commercial P&C data foundation and underwriting workflows. Its platform supports submission enrichment, proactive recommendations and actions, and access to risk information within a modular workbench. These capabilities help connect underwriting evidence with the work required to evaluate an account. Learn about Convr AI.

Modernize Commercial Underwriting With Convr

Agentic AI creates an opportunity to coordinate more of the work surrounding an underwriting decision. Its value depends on the quality of the information, the relevance of the insurance context, and the controls governing each action.

For carriers and MGAs, that means choosing technology that connects submission processing, risk intelligence, and workflow execution while keeping underwriters involved where their expertise is needed.

Convr's AI Underwriting Workbench brings these capabilities into a platform built for commercial P&C insurance. Teams can use it to structure fragmented submissions, surface relevant insights, and organize work around the accounts that need attention.

Book a demo with Convr to explore how its AI Underwriting Workbench can help your team reduce manual work, improve submission review, and take the next step in underwriting modernization.

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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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