This underwriter shows how advanced data can separate perceived risk from real risk—making it possible to extend vital coverage to those who need it most.
Someday you might look way, way up at a distant figure working hundreds of feet above the ground on a high-voltage transmission line and wonder: Could that guy’s cholesterol levels actually influence his life expectancy?
Melissa Kuecker ponders unlikely questions like that all day long. As Senior Life Insurance Underwriter at The Union Labor Life Insurance Company—a subsidiary of Ullico Inc. that insures union labor members whose work can make mainstream carriers nervous—it’s her job to assess risk from a distinctly different vantage point.
Some on-the-job risks may seem downright death defying (at least to us deskbound type), but they’re all in a day’s work to that guy on the transmission tower, and Melissa really wants him to have life insurance. Union Labor Life looks past the perils of the job so that the workers who build and maintain our infrastructure and keep our communities safe can keep their own families financially secure, too. She cares very much about things like cholesterol levels and the conditions they may signal, as well as their severity and nuance, because that makes all the difference in issuing more policies to this cohort.
That’s why Union Labor Life begins its supplemental life underwriting procedure with Irix Risk Score and treats Prescription Data and Medical Data as foundational to risk assessment—an approach that allows Melissa to quickly triage applicants while also satisfying her inner data nerd.
Many underwriters are still a bit ill at ease with predictive models for risk assessment, but Melissa is completely at home with them and uses Risk Score like a champ. We were intrigued by her fearless use of this insurtech tool and Union Labor Life’s perspective on risk, so we pressed her with some questions that may give confidence to others, too.
Given your organization’s union heritage, is there a sense of solidarity—that you’re looking out for your union brothers and sisters?
Absolutely. Our life insurance products are specialized for union members. We don’t discriminate against them because of the risks inherent in their work—we understand and knowingly take on that risk.
Union Labor Life was founded out of a passion for providing coverage to union members who do hard work that matters—even when that work puts them in harm’s way. That has been our number-one priority for almost a century. These families deserve financial protection, and we help them get it.
Underwriting has traditionally been focused on understanding causality. For example, smoking isn’t merely associated with higher mortality; we understand the causal mechanism behind many of smoking’s harms. That causality is blurred when using predictive models. Did you have to overcome any of your own skepticism when it came to trusting a predictive model?
It was easier for me because I evaluated Social Security disability claims before becoming an underwriter, so I’ve reviewed a lot of medical records and been exposed to a wide range of medical conditions. With that experience, I was already comfortable with the billing, procedure, and diagnosis codes that serve as key inputs to Risk Score.
Since that was a language I already spoke, I really valued sitting down with the Irix team to review medical codes and time frames and assign risk levels based on both their expertise and my own. That helped ease any concerns I might have had.
By the time I start underwriting a case now, I already have a model score—but I can also dig into the Rules Engine and review the flagged rules to ensure the system is working the way I intend. It needs to align with our underwriting and reinsurance guidelines and reflect anything in the data that might warrant a different decision.
Traditionally, underwriters started from an assumption of best class; no matter what new information came in, that applicant’s insurability could stay the same, but if it changed, it only got worse. A model like Risk Score starts with an assumption of average mortality; the data that comes in can move the score up or down. Have you found that using the model allows you to insure some people who would previously have been declined?
Yes. We used to rely on a blanket medical question in our simplified issue business. It was just one question, but it listed numerous conditions—diabetes, uncontrolled high blood pressure, kidney disease, and others spanning nearly every body system. A “yes” to any of them meant an automatic decline.
Now, even if an applicant answers yes, we still run them through the model. If the score comes back red, we decline. If it’s yellow or green, I review the medical data. There’s always a reason for the “yes,” but often I find conditions like diabetes or hypertension that are actually well controlled.
The last time I checked, I had 52 applications where applicants answered yes to the medical question but received acceptable Risk Score results—and I issued policies for 41 of them.
The sheer volume of data available on many applicants can be intimidating, which is one reason many underwriters prefer the clarity of a score, or the repeatability of a rules engine calibrated to their guidelines. You use those tools but seem happy to get into those weeds, too.
The more data, the better. When I’m underwriting, I love how easy it is to sort medical and prescription information by date, frequency, and claim count. It’s a game changer—it makes chronic or persistent conditions stand out quickly.
Each medical provider is clearly listed, which also makes ordering an attending physician statement (APS) far more efficient.
Is knowing what you’re looking for useful, even in times when you need additional information such as EHR or an APS?
Yes. Before we used the Irix tools, I might have asked an applicant for their primary care physician, ordered an APS, and then discovered they’d accidentally given me the name of their eye doctor.
Now, because we order Medical Data for everyone, I can see whether they’ve visited a cardiologist, gastroenterologist, or been hospitalized. If it’s a cardiac issue I’m concerned about, I know exactly what records to order.
When an applicant receives a yellow Risk Score and I order an APS, I also reflect afterward: Was I already leaning one way? Did the APS change my decision? That reflection is valuable. And sometimes, yes—you review the APS and think, “We dodged a bullet there.” But even when all the relevant information was available from the start, it’s still reassuring to confirm it.
Your approach seems very methodical and analytical, but it almost feels as if you’re going after some deeper truth about the applicant—almost the way an investigative reporter or detective might.
Sometimes it does feel like detective work. I’ve had applicants say they’ve never been arrested, only for their motor vehicle records to show DUIs, license revocations, or even jail time. That’s why it’s so important to assemble the full picture before evaluating risk.
We’re not scared off just because someone’s job involves a hard hat, safety harness, steel-toed boots, or even body armor. We understand the work they do. Smart underwriting is our protective gear.
Our goal is to consider all relevant factors—so we can help people get coverage while also protecting our company and making sound, responsible decisions.
