Picture a junior underwriter spending two hours re-keying a 12-page statement of values. Multiply that by 40 accounts a week and all of a sudden you have a giant chunk of their time taken by error prone manual processes. Not the best strategy if you ask me.
Insurance is a data based business and, for all of the technological advances built on data science, our industry still moves surprisingly slow. While digital underwriting workbenches and modern agency management systems have given us greater ability to leverage this data, the industry is still using manual workflows like the one described above. Not only is this time consuming, but it increases the likelihood of error. When data drives critical decision making, error prone processes are unacceptable. Over time, small inefficiencies compound into material financial, compliance, and reputational losses.
But fear not! Low code tools and AI driven document intelligence are here, and they are surprisingly easy to implement and operationalize.
First, let’s make sure we fully understand our manual data entry problem. Sure we all know that automating things is easier and faster, but seeing all of the areas where we are losing money can still come as a surprise.
|
Cost Center |
How It Starts Small |
How It Snowballs |
|
Labor Hours |
10–15 minutes per doc |
Thousands of hours annually; higher payroll or overtime |
|
Error Rates |
1% keystroke error |
Mispriced policies, reserve inaccuracies, rework costs |
|
Cycle-Time Delays |
One late quote |
Lost deals, broker churn, lower hit ratios |
|
Compliance & Audit Risk |
Misfiled PDFs |
Fines, restatements, damaged carrier/MGA relationships |
|
Opportunity Cost of Speed |
Quotes delayed for hours or days |
Slower quote times lower accounts won. Getting used to slow quotes means getting used to losing business. |
There are, of course, interim solutions that may seem less risky than fully transforming your data intake. Outsourcing or offshoring is one such example. While this reduces cost per keystroke, we still have the problem of error propagation, and the issue may even be made worse.
When it comes to documents and forms, optical character recognition tools may help with extracting data from documents, but still require manual input in the underwriting workflow and often break on semi-structured documents.
Maybe you have even considered building an in-house system to crawl form responses and documents, but the costs to maintain such systems is significant, especially as forms and templates evolve.
The ideal solution is thus one that can streamline the entire underwriting data intake workflow in a way customized to your firm. It can use AI powered document intelligence to accurately understand unstructured docs and extract the right data to the right places. And it can evolve over time, all while being easy to implement, adjust, and maintain on your end.
Implementing something like this in practice can be easier than you might think. First, start with your data intake. Two things need to be considered when updating your intake system; the user experience itself, and how the data is collected on the back end. We have all experienced clunky forms, so you should know that the look and feel of the form is surprisingly important. Don't make your customers use a user interface that feels like the DMV, use a modern form builder. Modern form builders like Typeform and Feathery are also easy to embed or can live on their own as a web application. They also tend to have out of the box integrations that make connecting the data to other systems a breeze.
While I like Typeform as a longtime user, a product like Feathery has an edge in that it features AI driven document intelligence. Sure, you could build a custom system integrating another document reading tool, but at that point you’re starting to overcomplicate things. Having the ability to pull data from unstructured documents within the same tool your forms and workflows are built on is just simple and effective.
With data being collected from form responses and documents, we arrive at the critical step; routing the data. Since we are looking to reduce or eliminate the process of manual data entry, connecting this data to the right places in your workflows is the real key to success. This might take some setup time, but don't be intimidated; it’s a lot more straightforward than it sounds.
Manual data entry is the slow leak no one notices until the floorboards warp—lost hours, hidden errors, deals that die on someone else’s desk. Low-code platforms with baked-in AI, like Feathery, patch the hole and lay fresh flooring in one move: sleek forms up front, clean structured data out back, and a workflow that actually keeps pace with your business ambitions. Start small, wire it tight, and watch those “tiny” inefficiencies stop snowballing into seven-figure headaches. Your underwriters—and your bottom line—will thank you.