November 14, 2023
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For Execution Excellence Purpose-Built AI is Essential
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For Execution Excellence Purpose-Built AI is Essential

The key to success is having a vision of what the future holds and a plan that takes an enterprise view that brings that vision to life in years to come.

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In the commercial insurance industry and others, conversations about artificial intelligence (AI) proliferate. So too does discussion around digitization and optical character recognition (OCR). Many insurance organizations have started their journeys, but others are just getting started. 

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What’s the key to success? 

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Like with so many things, the key to success is having a vision of the future – a plan that takes an enterprise view. In building that vision of the future, insurance organizations need to recognize that the immediate possibilities are not the endgame. Solutions must be extensible and purpose-built to be best-in-class and stand the test of time. 

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For the past seven years, Convr AI has taken generic large language models (LLM) and converted them to Industry-specific LLM to address the particular requirements of commercial p&c, specialty lines and workers’ comp insurance. This industry specificity is particularly important for processing speed and the avoidance of drift – one of the newly acknowledged risks associated with AI.* Our trained and tuned assistive AI best predicts which data meets extraction objectives, best answers underwriting questions, scores submissions and exposes the right data to expose risk characteristics – all to streamline the submission-to-quote process in commercial insurance.  

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To date, Convr AI has processed more than 2.3 million submissions through its purpose-built platform. Since January 2021 Convr has classified 25,965,229 assets, including ACORD, loss runs, emails, statement of values (SOVs), etc.)  And with each submission and classification Convr AI algorithms become more sophisticated and precise in meeting the required search outcomes. This is how companies using Convr see a 130% increase in efficiency with meaningful increases over time. 

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Partnering with machines to focus on data need vs. data completeness. 

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Convr’s focus on commercial insurance has led to an industry domain expertise that allows us to address the specific processes and needs of the industry. We have developed an understanding of underwriting efficiency like few others and understand the tradeoffs such as: Humans processing excellence achieves a roughly 97% accuracy rate; 90% accuracy from machines, and nearly 100% when digital assistants support human-in-the-loop (HITL). The tradeoff lies in processing time where humans are the slowest. Recognizing, as we do that machine capability increases with repetition and human assistance and processing time is maximized with machines alone, best practice at Convr optimizes the human to machine interaction for the benefit of human experience and efficiency.  

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Beyond the human to machine interaction, Convr has documented that not all information on emails and documents is helpful to the underwriting process. Machines do an excellent job of scanning data fields for purpose. In fact, machines do this better and faster than humans. Studies have proven that if you provide a PDF file attachment to an email, recipients will open it, even when it’s not useful. Machines can be trained to do so only when it’s beneficial to the outcome. Convr, for instance can complete the essential underwriting data gathering from its built-for-purpose data lake with only a company name and address – completing the generally 51 out of 94 fields on a standard ACORD to assess only the useful underwriting exposure information that supports appropriate pricing. 

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*Drift describes the phenomenon when the accuracy of AI models can drift (degrade) when production data differs from training data.  

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Industry Specific Use Cases Mater – Consider the Following Three: 

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1. Digitizing Submissions for Purpose 

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In the submission digitization process, multiple documents are split, extracted and digitized into a single view with lineage from insured and broker data inputs. Information is typically received from submission emails including ACORD and other forms, loss runs, etc. Optical character recognition (OCR) is a foundational capability to extract information off submission materials, though, as stated earlier, it's important to recognize that not all fields are relevant for the underwriting process.  

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For this reason, purpose-built AI scans the submission documents for only the relevant fields and data. The goal is to capture only the relevant information to maximize both machine and human efficiency. In fact, underwriters tell us that only 10-12 fields on some ACORD forms are required for clearance and just 15-20 additional fields are required for rating. By extending the digitization process to automate rules and decision-making for file preparation and clearance insurance organizations drive faster clearance times, improved accuracy and reduced costs. 

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Extraction and normalization of information from a variety of insurance documents (structured and unstructured) requires the machine capability to transform the data fields into a standardized structure, irrespective of document format. Part of digitization excellence is the application of artificial intelligence and machine learning to support rapid integration of new source documents and then to identify the most credible source of data. 
Importantly, it’s then added to the knowledge graph – the corpus of knowledge already known for continuously enriching information. Essential fields are those that drive the best decisions and build the foundation of an organization’s future decision-making. When digitization is purpose-specific, the captured data fields are far more likely to contain accurate, meaningful and complete information. In the best case, accuracy checks can be completed with side-by-side validation. That’s what we do at Convr AI. 

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2. Enriching Applications & Submissions 

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Another strength of purpose-built AI is its efficiency in analyzing a large number of data sources to serve up the best data for an accurate identification the business – pre-filling business class/industry code (Business Identity, DBA's, NAICS, SIC, WCC code.) 

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Submission enrichment for commercial insurance enhances the insurance application data provided by an applicant or producer. This process involves scanning potentially thousands of data sources to collect and analyze additional information about the applicant’s business operations, risk exposures, and claims history. The valuable and relevant data is then appended to the application to provide a more complete and accurate picture of the risk. 

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The result is less manual fact gathering, greater clarity around potential exposures, and better-informed decisions about coverage needs, pricing, as well as necessary mitigation. Examples of data that may be collected and analyzed include financial statements, inspections, loss runs, claims history, and regulatory requirements. Convr AI pulls from the extensive resources in our data lake comprised of thousands of sources and our assistive AI then facilitates in-line enrichment and evaluation. 

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3. Prioritizing Submissions 

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Another important use case to consider when reimagining a future state of operational excellence is managing the ebbs and flows of submission volume. Most insurance organizations are well-aware of the July and January first annual production overloads but not everyone considers the periodic deluges that might be tied to factors other than the most common renewal dates.

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Large scale cancelations, new books of business andnew producer appointments can all result in surges in applications. When these situations arise, teams often turn to overtime, outsourcing or manual prioritization which is fraught with inconsistencies and inefficiency.   

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Even when there is a steady volume of submissions, colleagues and customers benefit from a reliable methodology for prioritizing incoming submissions in line with pre-set underwriting rules specific to risk appetite, completeness and winnability.  

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Using machine learning models (MLM), Convr assesses data extracted from intake and/or a business's digital footprint to better manage the submission prioritization process with digital assistance that is both timesaving and reliably consistent in implementing your business rules. Customers benefit from a reliable methodology for prioritizing incoming submissions in line with pre-set underwriting rules specific to risk appetite, completeness and winnability.  

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Built for Purpose – Built for the Future  

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When you embed purpose-built AI into your long-term plan for executional excellence you establish an extensible foundation for layering on future use cases – foreseen and not. While you likely have a vision for the future – part of that vision must recognize that the immediate best opportunities may not reflect those of the future. For that reason, flexibility to incorporate new data, solutions and insights is critical for a future-proof insurance platform. 

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For insurance organizations it is a universal truth that top resources must be focused on risk identification, mitigation and protecting the viability of business insured operations. This requires a sustained focus on efficiency and accuracy in data gathering, data management and analysis. The best business partners are those directly in sync with these operational requirements; those with that focus and expertise and built on modern foundations like AI and LLM specifically trained for the benefit and particular requirements of commercial property and casualty, workers’ compensation and specialty insurance. This industry specificity is particularly important today for processing speed and ongoing accuracy and tomorrow for platform and industry changes to come.  

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At Convr AI, our trained and continuously tuned machine learning models deliver the best sources of data for extraction, best answers to underwriting questions, best data to expose risk characteristics, and best scores submissions to meet guidelines – all to streamline and drive valuable insights into the submission to quote process in commercial insurance. For the commercial insurance industry Convr delivers an end-to-end resource for lasting competitive advantage. 

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Keep Reading

More articles on AI, underwriting and the future of commercial P&C.

XX MIN READ

The Hidden Cost of Manual Data Entry in Commercial Lines Underwriting

Ask a commercial lines insurance underwriting leader where their team's time goes, and the honest answer often isn't underwriting at all. It's data entry: keying in values from an SOV, cross-referencing a loss run against a submission, retyping limits and named insureds from a PDF into a rating system.

On paper, this looks like a minor operational cost, the price of doing business with documents that don't come in clean formats.  

The visible cost of manual data entry is time. A commercial property submission with a large SOV can take an underwriter or their support staff hours to process by hand, checking property values, occupancy types, and construction details against what's on the application. Multiply that across a full pipeline of submissions, and the hours add up fast.

While time is the cost that's easiest to see, it is often the least significant one. The hidden costs are the ones that don't show up until later.

‍The following are four hidden costs underwriting leaders need to consider:


Cost one: decision quality

Every hour an underwriter spends transcribing data is an hour not spent evaluating it. When manual entry eats into the day, the analysis that should happen around a submission -- spotting a concerning trend in loss history, questioning whether a stated property value is realistic, comparing an account against appetite -- gets compressed into whatever time is left.

Underwriting quality doesn't erode all at once. It erodes in small increments, submission by submission, as the ratio of time spent on data handling to time spent on judgment tips further out of balance. See why this is a hidden cost that cannot be overlooked?

Cost two: accuracy risk

Manual entry is also where errors creep in. A transposed limit, a missed COPE field, an incorrectly keyed TIV, these mistakes are easy to make and hard to catch, especially under volume pressure.

In commercial lines, where pricing and terms often hinge on the accuracy of property and exposure data, a small entry error can compound into a meaningfully mispriced risk. The cost of that error rarely surfaces immediately. It surfaces later, at claim time or renewal, when it's far more expensive to fix.

Cost three: inconsistent turnaround time

Manual processes don't scale evenly. When submission volume spikes, whether from a hard market, a new distribution partnership, or seasonal patterns, teams reliant on manual data entry hit a ceiling fast. Turnaround times stretch, brokers wait longer for quotes, and the accounts that move fastest aren't necessarily the best risks. In fact they're often the ones with the simplest paperwork. That's not a formula for disciplined underwriting; it's a formula for favoring ease over quality.  

Cost four: talent and turnover

There's also a cost that's harder to quantify but increasingly difficult to ignore: the toll manual entry takes on the people who are doing it.

Underwriters and underwriting assistants who spend a disproportionate share of their day on repetitive transcription rather than analysis tend to disengage from work that should be intellectually demanding. In a competitive labor market for underwriting talent, that's a retention risk hiding in plain sight.

Why this is solvable now

None of this is a new problem. What's changed is the availability of tools built specifically to solve it. Convr’s structured data ingestion, purpose-built for the ACORD forms, SOVs, and loss runs that make up commercial submissions, can take on the transcription work directly, pulling and validating data with a level of consistency manual entry can't match.

That data then feeds the Risk Context Engine, Convr’s ontology for commercial P&C risk, so a submission doesn’t sit as an orphaned record, it’s tied to the broader risk picture connected to prior submissions, relationships, and appetite history rather than evaluated on its own. That doesn't remove underwriters from the process. It removes the bottleneck standing between a submission arriving and a qualified underwriter evaluating it.

Rethinking where the real cost sits

The instinct to treat manual data entry as an operational cost is understandable.

It doesn't show up as a line item the way software or headcount does. But its true cost is distributed across decision quality, accuracy, turnaround times, and talent retention, all of which matter far more to a commercial lines book than the hours spent on submission entry alone.

The teams that recognize this are the ones rethinking where their underwriters' time really belongs, and building workflows that let judgment, not transcription, define how a submission gets handled.

If you’re ready for a conversation about re-envisioning how your team can improve the underwriting experience while avoiding some of these time sucking hidden costs, visit convr.com and book a demo today.

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XX MIN READ

Convr Prioritizes Communication in Underwriting Workbench

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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XX MIN READ

Agentic AI Doesn’t Just Assist, It Acts

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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