July 20, 2023
CASE STUDY
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xx min read

Three Hot Topics for Workers Compensation Executives

AI and Machine Learning methodologies are new partners to workers’ compensation automation for loss avoidance, and colleague recruitment and retention.

Workers Compensation organizations including carriers, state insurance funds and rating bureaus share a great deal in common. Notably, they are all directly impacted by regulatory, economic, societal, and environmental conditions while trying to protect American productivity.

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At a time when the pace of change continues to accelerate, Workers Compensation executives must be vigilant in their observance of emerging trends and the adoption of new ideas and methods to address them. Some of the hot topics include:

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  • The changing workplace – retention and the talent gap
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  • Data access and accuracy
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  • Advancing technology AI and technology enablement

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The Changing Workplace

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The pandemic wreaked painful havoc on nearly every workplace around the world. Suddenly, offices, hotels, restaurants and nearly every gathering place was emptied. Isolation and illness forced the adoption of new technologies and medical treatments. Three years later, workers are less tethered to their offices than ever before and in many cases, are insistent on more flexible work conditions. Additionally, nearly two-thirds of workers are looking for a new job, and 88% of executives are seeing higher turnover than normal, according to a survey by the consulting firm PwC.

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The workforce has become less experienced and less loyal, exposing the workers compensation industry to potentially higher exposure and pricing insecurity.

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Data Access & Accuracy

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Another of the trends that will increasingly affect workers compensation organizations is the proliferation of data and the essential requirement to understand data lineage, relevance, and accuracy. While analytics has long been an essential tool among workers compensation providers, trended data was considered reliable. With massive changes, that assumption is now highly questionable. The need has gone beyond a team of data scientists to the need for new and integrated data sources and models that can be updated and validated with increasing frequency.

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Internal teams can no longer manage the data deluge alone.

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Advancing Technology

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AI and Machine Learning methodology is essential to keep up with the pace of change. Chat GPT has received a lot of recent notoriety for good cause. We need to learn faster from experience as new workplace issues emerge at an ever-increasing pace. We need to collect and analyze the right data more quickly to prevent losses. And, we need to predict trends and losses

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to avoid poor performance. We also need to adapt technologies in support of our workers’ compensation colleagues to ensure that ours is an industry with the modern technology that fuels worker performance and satisfaction.

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AI and Machine Learning methodologies are essential, new partners to workers’ compensation automation for loss avoidance, claims management, and colleague recruitment and retention.

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Convr AI is all about helping workers’ compensation insurers, managing general underwriters (MGU) and producers, as well as state boards and rating organizations improve performance by incorporating the best data into advanced AI models. We begin by ingesting Experience Rating Mods (e-mods) and business classification codes into the Convr Underwriting Command Center platform.* From there, Convr AI combines the e-mods and classification codes with peta-bytes of continuously refreshed data housed in our proprietary data lake comprised of thousands of public and private data sources.

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A knowledge graph keeps the various data elements connected and machine learning models (MLM) pinpoint the best data surfacing key underwriting insights, business classification/DBAs and risk scores, enabling underwriting teams to make smarter risk decisions, faster.

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Using our d3 Risk 360TM data lake, teams find new efficiencies in segmenting business entities by classification code to identify and prioritize audits in high-hazard classes. Then, they benefit from simplified look-up of each company and their DBAs. Using d3 Risk 360, our customers can also identify material account changes. Convr’s data lake is like having a crystal ball—you see things others simply can’t. The ease of use and insights are unparalleled.

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Convr would welcome the opportunity to share more about our data ingestion and insights capabilities and how we can help you become more productive and more successfully manage workers compensation performance.

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Request a demo by scheduling time with Convr’s Business Development team.

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*Note: Affiliate agreements must be in place between the insurer rating board and Convr for Convr to ingest the e-mods.

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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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