Mortality is one of the biggest predictors for profitability in life insurance…
Mortality is one of the biggest predictors for profitability in life insurance. Yet, many underwriting models continue to rely heavily on outdated or incomplete inputs. Medical records and credit histories only tell part of the story, and it’s easy for fraudsters to slip through the cracks.
Criminal history and identity integrity are often overlooked data sets, but can be among the most highly predictive methods of determining mortality expectations. An individual with a history of violent crime or drug offenses, for instance, carries a mortality risk multiple times higher than someone with a clean record. Similarly, identity fraud enables high-risk individuals to conceal their true identities, potentially leading to early and unexpected claims.
We’ll lay out a framework for quantifying these overlooked risks and explain how Prodigi’s solution enables insurers to refine mortality models, flag fraud, and improve bottom-line results.
Underwriting the traditional way leaves plenty of room for gaps and fraud. These mistakes can smear your brand’s integrity and leave you behind the competition.
In order to stay ahead of competitors, insurers must incorporate proven, non-medical risk indicators. Criminal behavior is one of them. The writing is on the wall with clear data and consistent patterns that spell out the undeniable impact on life expectancy for people with a criminal past.
Applicants with violent crime records present a dramatically heightened mortality profile, with a homicide rate between five and ten times greater than that of the general population. This is more than a troubling statistic; it represents a measurable and recurring pattern of loss in life insurance portfolios.
Violent behavior and dangerous environments often go hand-in-hand. Environments involving high-conflict relationships and increased law enforcement interaction are bad for the insurance business.
Not only do they raise the risk of homicide; they also elevate the likelihood of injury-related deaths, retaliatory violence, and fatal encounters with authorities.
For concerned underwriters, even a single prior violent offense can shift the probability curve for premature death. Failure to incorporate this data in underwriting can lead to true risk, underpriced policies, and adverse selection.
Drug-related crimes, especially in the era of widespread opioid and fentanyl abuse, have become one of the most severe mortality accelerators in modern underwriting. Offenders with documented drug charges face a 12.7x greater likelihood of overdose death; a rate that can erase decades of expected premium value in a single claim event.
The risk extends beyond overdose. Chronic drug use is tied to infectious diseases such as hepatitis C and HIV, organ damage, and violent or reckless behavior that further reduces life expectancy.
The current overdose crisis has created clusters of geographic and demographic risk that can be modeled, but only if the underlying criminal and arrest data are visible at the underwriting stage.
Failing to account for this profile leaves insurers exposed to concentrated pockets of high-severity, early claims.
Time served behind bars has a measurable and long-lasting effect on mortality outcomes. Former inmates, even years after release, have a life expectancy reduced by five to ten years compared to non-incarcerated peers.
The drivers are multi-layered: untreated or poorly managed chronic illnesses, high rates of substance abuse relapse, limited access to consistent healthcare, and elevated suicide rates.
Social instability is another factor. Difficulty securing employment, fractured family relationships, and unsafe living conditions all contribute to the decline.
Social risks grow over time and lead to a drag on survivorship that standard medical underwriting can’t catch.
By incorporating incarceration history, insurers can more accurately assess the residual impact on mortality long after the sentence has been served.
Applicants with a criminal history are three to four times more likely to die by suicide than the general population. This is not simply a mental health consideration; it’s a quantifiable and immediate threat to the insurer’s risk pool. Criminal records often coincide with risk factors such as depression, post-traumatic stress, substance abuse, and social isolation.
These issues can escalate following legal troubles, imprisonment, or ongoing social stigma. From an underwriting perspective, suicide represents a unique challenge because it can result in large, immediate payouts, often within the contestability period of the policy.
Recognizing and scoring this risk at application can help insurers adjust pricing, impose appropriate waiting periods, or implement targeted case management strategies. Without this step, the portfolio remains vulnerable to a category of claims that are both unpredictable in timing and severe in financial impact.
Identity fraud is more than paperwork. It’s a deliberate strategy used to distort underwriting. When you can’t trust the name, you can’t trust the risk.
Fraudulent applicants often use aliases or synthetic identities to conceal red flags, thereby slipping through the cracks of conventional review processes.
When identity isn’t verified, the risk profile becomes a matter of guesswork. Medical and criminal histories vanish from view. The result? Life expectancy calculations collapse. These applicants often represent extreme cases of adverse selection, where the real risk is far greater than what appears on paper. Claims arrive early, quickly, and unexpectedly.
To turn hidden risk into a measurable threat, follow a structured approach:
First, model the financial impact. Assign an “imputed risk cost” to each unknown; multiplying the face value by fraud probability yields a real dollar figure. Then, analyze proxies. Inconsistent records, thin credit, or an empty public data footprint are warning signs. Build a risk score.
From there, use tiered workflows. Treat low-risk applications efficiently, flag uncertain ones for clarification, and escalate potential fraud. Most importantly, shift the burden of proof. If identity can’t be confirmed, the application shouldn’t proceed. Require official documents to move forward. This one step can drastically reduce fraud.
Most underwriting systems operate with a partial view of an applicant’s risk landscape. Medical data, credit history, and self-reported information form the core of traditional assessments; however, this approach leaves critical gaps.
Prodigi closes those gaps by introducing two data pillars often missing from the equation: comprehensive criminal history and verified identity.
These inputs are not incidental; they are central to understanding true mortality exposure. A violent offense, a series of drug charges, or a concealed incarceration record can significantly alter life expectancy projections.
Likewise, an unverified identity can render every other data point meaningless. By integrating both dimensions, Prodigi delivers a 360-degree mortality profile that merges medical, behavioral, and identity intelligence into a single, actionable risk picture.
This depth of insight gives underwriters the confidence to price accurately, decline strategically, and accept with precision.
In risk assessment, nuance is profitability. Treating all applicants with criminal history as equal is a blunt approach that leaves money on the table and increases exposure.
Prodigi’s solution enables insurers to differentiate with accuracy: a minor property crime may warrant a modest risk adjustment, while a violent felony can indicate a high probability of an early claim.
Drug offenses, depending on recency and severity, may signal overdose risk or chronic health deterioration. This level of granularity enables the alignment of pricing with the actual risk curve, thereby protecting loss ratios while remaining competitive in the market.
Our precision stratification also aids in capital allocation, reserving the most rigorous underwriting resources for applicants with the highest mortality variance. The end result? Fewer surprises, cleaner portfolios, and a pricing structure that rewards accuracy rather than averages.
It’s no surprise that fraudulent applications increase claims costs and distort underwriting data.
Prodigi’s technology integrates directly into the fraud prevention process, enabling early detection and helping your team make the right choices on who gains coverage.
Our system cross-references identity data with public and proprietary records to confirm authenticity before the ink dries on your policy contracts.
When Prodigi catches mismatched personal data, nonexistent public records, or duplicate identities, our system flags them for escalation and further review.
At the same time, the solution streamlines low-risk approvals by automating clearance for verified identities, reducing friction for legitimate applicants. This balance between vigilance and efficiency helps insurers maintain KYC and AML compliance without slowing down the sales cycle. By stopping fraud at the point of entry, Prodigi preserves portfolio integrity and protects profitability.
Insurers aren’t just missing data, they’re missing signals. Criminal history and identity fraud aren’t edge cases; they’re central threats to portfolio stability. Left unaddressed, they lead to early claims, policy mispricing, and long-term financial strain.
Now is the time to act.
Better data leads to better decisions. Better decisions lead to better outcomes.
Prodigi is a data intelligence software specializing in identity verification and criminal history analytics for the insurance industry.
Our mission is to help carriers stop fraud before it damages their reputation, build stronger risk models, and protect profitability. With deep expertise in identity science, fraud detection, and mortality risk, we equip underwriters with an application that helps them make confident, informed decisions during the underwriting process.
Ready to build a smarter, stronger mortality model? Contact us directly for a customized consultation. Let’s take control of risk together.
