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Life insurance underwriting used to be a waiting game. Everyone accepted that getting a fully underwritten decision meant weeks of back-and-forth on medical records, prescription histories, and lab results.
That timeline is collapsing. AI-assisted underwriting is turning three-week processes into decisions that happen in days, sometimes hours.
Take a client with well-managed Type 2 diabetes applying for term life insurance. Five years ago, that case would have required multiple rounds of documentation requests, manual review of medical files from different providers, and careful coordination between the agent, the underwriting team, and the client's physicians.
Today, AI handles the data integration and preprocessing automatically. Medical records from multiple providers are merged, normalized, and flagged for review by underwriters without the manual work that used to bottleneck the process.
For agents and advisors, this means fewer clients dropping out during long underwriting periods, more competitive positioning against carriers with faster turnaround times, and significantly less time chasing down documentation that should have been in the file from the start.
But speed alone doesn't mean much if the decisions aren't accurate. And that's where the real complexity begins. AI models learn from historical data, which, in life insurance, means underwriting files, claims records, and death certificates. The assumption is that this data is clean, accurate, and reliable. It's not.
In 2017, researchers in Vermont studied 601 death certificates completed by physicians and other certifiers. Medical examiners, blinded to the original certificates, reviewed clinical summaries from medical records and generated their own versions based on the actual medical evidence.
When examiners compared the two types of summaries, 51% had major errors, and 60% required changes to the underlying cause-of-death code used in national mortality statistics.
Insurance carriers depend on death certificates to code the cause of death into their systems. We don't order autopsies or independent medical reviews for every claim. We take the death certificate at face value, code it, and move on.
When AI models are trained on that data — when they're asked to predict mortality risk based on underwriting evidence and eventual cause of death — they're learning from information that's wrong more than half the time.
AI can unintentionally amplify these errors. It identifies patterns in flawed data and applies those patterns with confidence to new cases. If the model was trained on death certificates that misattributed deaths to diabetes when the actual cause was cardiovascular disease, it will make assumptions about diabetic clients that aren't grounded in reality.
It's a data quality problem that AI makes solving more urgent.
Actuarial oversight makes speed sustainable.
Actuaries have been dealing with messy data long before AI became mainstream.
Actuarial Standard of Practice 23, which addresses data quality, was first written in 1993 and has been updated regularly ever since. It lays out a clear framework: Before using a data set for analysis, you assess whether it's complete, accurate, and whether it requires enhancement before you can rely on it for decision-making.
Those questions don't change just because AI is doing the heavy lifting.
If anything, those questions become more critical.
When a model processes data in hours instead of months, there's less time to catch errors manually. The model has to be designed to flag incomplete records, identify outliers, and surface inconsistencies that a human reviewer might miss under a deadline.
And someone has to set the parameters for what counts as an acceptable data point and what doesn't.
That's where actuarial oversight comes in. Actuaries ensure the model isn't hallucinating based on incomplete inputs. They stress-test assumptions before models go into production. They review edge cases to make sure the AI isn't overfitting to noise in the training data. And they validate that the faster decisions agents are delivering to clients are backed by rigorous analysis, not algorithmic confidence.
AI can help you spend more time on what matters.
AI tools are making underwriting faster, but they're not making the process simpler on the carrier side.
Carriers are integrating more data sources, running more sophisticated models, and dealing with regulatory frameworks that were written for earlier, less capable versions of AI. The work of ensuring those tools function correctly — that they're fair, accurate, and compliant — falls heavily on actuaries.
For agents and advisors, understanding this dynamic helps in a few ways.
When a client receives a rating or a decline that doesn't match their expectations, you can explain that the decision isn't arbitrary. It's based on a model that's been validated against rigorous standards for data quality and predictive accuracy.
When a competitor offers faster turnaround times, you can evaluate whether that speed is sustainable or whether corners are being cut in ways that could create problems down the line.
Human judgment should always guide the process when decisions carry significant impact. But AI can handle tedious tasks like integration, preprocessing, and pattern recognition across massive datasets, so agents can focus on building client relationships, explaining options, and helping people make informed decisions about coverage.
Advisors can spend more time on financial planning and less time on paperwork. And clients get answers faster without sacrificing accuracy.
AI is already embedded in life insurance underwriting, and adoption is accelerating across carriers. The ones getting it right are those who pair speed with rigor: investing in data quality, stress-testing their models, and ensuring actuarial oversight from the start.
Amanda Turcotte is a senior vice president and chief actuary at Amalgamated Life Insurance Company.
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