Advanced Mortality Modeling in Life Settlements and Digital Twins in Healthcare
I was listening to Garry Breckers podcasts, and like many millions of people enjoyed hearing his perspectives on what can be improved within Human biology. Despite not being a licensed medical doctor, his background of biology and working as an actuary for over a decade at one of the most succesful Life Settlement companies in the US makes him a very credible and authentic voice.
Life settlement refers to the sale of an existing life insurance policy to a third party for more than its cash surrender value but less than its net death benefit. The buyer becomes the new beneficiary and pays the premiums, profiting upon the insured’s death. Mean mortality typically refers to the average expected age of death in a given population, often based on actuarial data.
Life Settlement Industry vs. Healthcare Digital Twins:?The life settlement industry and healthcare both grapple with predicting human longevity and health outcomes, albeit for different purposes. Life settlements (the sale of life insurance policies by seniors) rely on accurate?mortality modeling?to value policies and assess risk. Healthcare’s emerging?digital twin (DT)?diagnostics create virtual patient models for personalized treatment planning. This investigation examines how advanced analytics, including AI-driven longevity modeling, biometric data, and multi-omics, are used in life settlements, and how these methods could?cross-pollinate?to enhance predictive modeling in healthcare digital twins.
Mortality Modeling in the Life Settlement Industry
Actuarial Foundations:?Life settlement investors essentially “bet on longevity” and thus depend on rigorous actuarial models. Insurers and actuaries use the?law of large numbers?and mortality tables to predict life expectancy across populations. Mortality risk models assume that larger, more?homogeneous groups?yield more accurate predictions, as individual randomness averages out. In practice, underwriters stratify individuals by key risk factors (e.g. age, gender, smoking status) to create comparable cohorts for analysis. Traditional life expectancy estimates are derived from medical records and population mortality rates, then adjusted for an individual’s health impairments. Actuaries validate their models by comparing?actual vs. expected?outcomes, for example, tracking if observed mortality aligns with predictions, and refine models when discrepancies arise.
Longevity Modeling and Risk Assessment:?Classic actuarial approaches like survival curves or the Lee-Carter model forecast mortality rates over time. These models are increasingly augmented with?stochastic simulations?(Monte Carlo methods) to capture variability. Investors in life settlements often run?scenario analyses?with random mortality events to gauge return volatility. A narrow range of simulated outcomes indicates a stable, lower-risk portfolio, whereas wide dispersion signals higher “mortality volatility” that demands a risk premium. Such longevity modeling not only forecasts mean life expectancy but also assesses the uncertainty (risk) around that estimate, crucial for pricing life settlement investments. In summary, the life settlement industry has built a framework of actuarial science and longevity modeling to quantitatively assess mortality risk over long horizons.
AI and Machine Learning in Mortality Prediction
Enhancing Life Expectancy Estimates:?Artificial intelligence is transforming how life expectancy is predicted in life settlements.?AI-driven models?can analyze vast datasets – medical histories, demographic information, lifestyle factors, and broader mortality trends – to estimate individual life expectancy with greater precision. Unlike static actuarial tables, machine learning models continuously?refine predictions?as new health data or medical advances emerge. For example, if a breakthrough treatment extends survival for certain conditions, an AI model can quickly incorporate that effect, whereas traditional models might lag. These data-driven techniques have led to faster and more personalized underwriting decisions, moving beyond one-size-fits-all mortality curves.
Predictive Analytics in Underwriting:?Life settlement underwriters increasingly leverage?predictive analytics?to evaluate policy sellers. Machine learning algorithms ingest electronic health records (EHRs), prescription drug histories, lab results, and even data from wearables to gauge an individual’s health trajectory. This?data-driven health analysis?can flag subtle risk factors (like medication patterns or activity levels) that correlate with mortality risk, enabling more nuanced life expectancy assessments. Moreover, AI models predict?policy lapse ratesand financial sustainability by considering not just health, but also factors like premium costs and the policyholder’s financial situation. By crunching thousands of data points, AI helps determine if a policy is a viable long-term asset for investors or at risk of lapsing before payoff.
Dynamic Risk Adjustment:?Another advantage of AI in life settlements is real-time adaptability.?AI-powered toolsmonitor financial and population health indicators – interest rates, market fluctuations, emerging longevity trends – and adjust pricing models accordingly. For instance, if general life expectancy in the population improves or falls (as seen with medical advances or pandemics), the AI model recalibrates life settlement valuations on the fly. This agility ensures that investors’ pricing reflects the?latest conditions, something manual actuarial methods would update only periodically. Additionally, machine learning aids in?fraud detection, spotting anomalies in medical records or applications that might indicate misrepresentation. By flagging inconsistent data (e.g. a claimed illness that doesn’t match medical evidence), AI protects both providers and investors from fraudulent cases.
Example – Wearables and AI:?Modern predictive models incorporate?biometric sensor data?to enhance mortality predictions. A recent analysis by insurers found that lifestyle metrics from wearables (like daily step count, sleep patterns, heart rate, and grip strength) strongly segment mortality risk. Surprisingly, smokers who log over 7,000 steps a day had better mortality outcomes than non-smokers with sedentary habits. Likewise, an obese person who stays active can outlive a “normal” BMI individual who is inactive. These findings show that?behavioral data?can mitigate or exacerbate traditional risk factors. AI models ingesting such data can re-stratify risk more finely than conventional underwriting. As Will Cooper of an AI-insurtech firm noted, combining predictive models with ubiquitous smartphone and wearable data allows insurers to?integrate new data streams alongside cutting-edge analytics, yielding more precise risk estimates. In short, machine learning is revolutionizing mortality prediction by using diverse real-world datapoints to personalize life expectancy far beyond age and medical diagnoses alone.
Real-World Data and Longitudinal Health Records
Value of RWD:?Both insurance and healthcare increasingly recognize the importance of?real-world data (RWD), information derived from actual patient experiences outside of controlled trials. In life settlements, RWD comes from years of longitudinal medical records, claims data, and even personal health tracking. Rather than relying solely on one-time snapshots (e.g. a paramedical exam), underwriters are beginning to incorporate longitudinal?health records?to see how a person’s health metrics evolve over time. This can include tracking?chronic conditions?progression, hospitalizations, or responses to treatments, which all inform mortality risk more dynamically than a static underwriting exam.
Longitudinal Modeling:?Access to long-term health histories allows analysts to detect trajectories, for instance, a patient whose cardiac markers steadily worsen year-over-year may have a different outlook than one who’s stable or improving.?Advanced analytics can mine these longitudinal records?to identify patterns (like accelerating frailty or recovery after interventions) that influence life expectancy. The integration of EHR data into predictive models means life expectancy estimates can be updated as new labs, imaging, or doctor’s notes become available. Essentially, each patient’s?timeline?of health becomes a dataset for machine learning to interpret, rather than basing predictions only on static factors like age or diagnosis at one point in time.
Continuous Data Feeds:?Wearables and remote monitoring devices now supply a stream of real-world health data, daily steps, heart rate variability, sleep quality, glucose levels, etc. Insurers have started using this?real-time data?to refine underwriting. For example, if an insured consistently has high activity levels and good sleep duration, algorithms may lower their risk profile accordingly. This?real-world evidence?aligns insurance risk models more closely with current lifestyle behaviors, not just historical medical events. As Dr. Gina Guzman of a leading reinsurer observed, wearable data offers a?window into real-time health habits?and can enable more accurate and inclusive underwriting, while even encouraging healthier behavior through feedback loops.
Application to Digital Twins:?In healthcare,?digital twin diagnostics?thrive on RWD and longitudinal data. A patient’s digital twin is continuously fed by that patient’s real-world data, new lab results, ongoing vitals from IoT devices, follow-up imaging, etc. to keep the virtual model up to date. Real-world patient data can also?benchmark and validate?the twin’s predictions: if the twin’s simulated outcome diverges from actual real-world outcomes, the model can be recalibrated. This is analogous to how life insurers use emerging claims experience to adjust mortality assumptions. Furthermore, because any given patient can only experience one treatment path in reality, digital twins must learn from the?longitudinal outcomes of prior similar patients?to predict what might happen with different interventions. In other words, historical real-world data from patients with comparable profiles (e.g. same cancer type and genetics) becomes the knowledge base for simulating a new patient’s prognosis. Both industries thus heavily rely on aggregating and learning from longitudinal real-world datasets to improve predictive accuracy.
Biometrics and Multi-Omics Integration
Biometric Data:?“Biometrics” in this context refers to measurable biological signals and physical metrics. Insurers have traditionally collected some biometric data (blood pressure, BMI, cholesterol from medical exams). Now, with wearables, they can get far more: daily activity levels, heart rate trends, sleep duration, even gait or grip strength. These?high-frequency biometric inputs?are proving to be powerful mortality predictors. For instance, resting heart rate and grip strength (markers of cardiovascular and muscular health) can segment mortality risk even among people of the same age. By integrating such biometrics, predictive models move toward individualized?health fingerprints?rather than coarse categories. This mirrors healthcare’s push to monitor patients continuously (e.g. via smartwatches detecting arrhythmias). Both fields see biometrics as key to early risk detection, an abnormal change in a biometric might flag health deterioration before a formal diagnosis.
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Genetics and Multi-Omics in Life Risk Modeling:?A frontier area for the life settlement and insurance industry is using?genetic data?and other “omics” (like proteomics or metabolomics) to refine risk assessment. Traditionally, underwriting avoided genetics due to ethical and regulatory issues, but interest is growing in whether?DNA markers can improve mortality prediction. Some life settlement underwriters have explored genetic tests in hopes of finding clear evidence that a person’s genetic makeup can indicate longevity or susceptibility to disease. For example, certain gene variants might predispose to long life or, conversely, to early serious illnesses, incorporating such information could adjust life expectancy estimates. However, individual genetics can be probabilistic and must be handled cautiously, especially given randomness and environmental interactions. Still, as genomic data becomes more available, insurers may begin to include it alongside medical records and biometrics, carefully?balancing accuracy with privacy and anti-discrimination concerns.
Multi-Omics and Personalized Medicine:?In healthcare, integrating?multi-omics data?is central to precision medicine and by extension to digital twins. A comprehensive digital twin model might include a patient’s genomics (DNA sequence, key mutations), transcriptomics (gene expression levels), proteomics (protein biomarkers), metabolomics (metabolic profiles), and more. These layers of data capture the biological individuality of a patient. For example, genomic data can reveal inherited risks or drug sensitivities; proteomic and metabolic profiles can indicate disease state or progression. By?modeling the complex interactions?among these omics layers and environmental factors, digital twins can more accurately predict disease trajectories and treatment responses. Indeed, the?“correct creation of a digital twin requires multi-omics data”?and other inputs to truly represent a patient’s unique biology. This multi-omic integration is already being used to guide personalized therapies, for instance, in oncology, genetic mutations of a tumor guide which targeted drug a virtual simulation predicts to be most effective. The life settlement industry may not yet use full multi-omics, but its exploration of genetics is a step in that direction, and it could eventually draw on other omics (like epigenetic age markers) to enhance underwriting?if ethical and legal frameworks allow. Conversely, the discipline and statistical rigor actuarial science brings to handling multi-factorial data could aid healthcare researchers in interpreting multi-omic information without overfitting or false correlations.
Actuarial Techniques Informing Digital Twin Diagnostics
Probabilistic Risk Modeling:?Actuarial science excels at translating data into?probability-based forecasts. Each digital twin in healthcare could benefit from actuarial methods to quantify uncertainties in predictions. For example, rather than a single “predicted outcome,” a twin could produce a?distribution of possible outcomes?(with probabilities), similar to how actuaries produce a distribution of survival times for an insured cohort. Actuaries use survival models and hazard rates to estimate the likelihood of death at each time interval, a concept that can extend to estimating probabilities of various health events (disease progression, complication, recovery) in a patient’s digital twin. By applying survival analysis or even actuarial life-table approaches to clinical events, digital twin diagnostics might better communicate risks (e.g. a 20% chance of complication A, 5% of complication B by 1-year, etc.), improving clinical decision-making under uncertainty.
Cohort Analysis and “Patients Like Me”:?Actuarial projections often rely on?experience data from similar individuals, for instance, pricing life insurance for a 70-year-old diabetic by examining outcomes of thousands of comparable diabetics. Digital twins mirror this approach by drawing on data from cohorts of past patients who share characteristics with the current patient. Essentially, the twin leverages a?“virtual cohort”?to predict the target patient’s course. Actuarial insight can strengthen this by ensuring the cohort is large and homogeneous enough to be predictive. Actuaries might also contribute techniques for credibility weighting, blending population data with patient-specific data. For instance, early in a patient’s journey (little personal data), the twin relies more on population averages; as more personal longitudinal data accumulates, the twin shifts weight toward the individual’s observed trend, much like an actuary blending base mortality tables with an insured’s emerging experience.
Stress Testing and Scenario Planning:?Insurers routinely perform?scenario tests?(e.g. what if a pandemic hits or a new cure emerges?) on mortality models. Similarly, digital twins can simulate “what-if” scenarios for a patient: What if we start Drug A vs. Drug B? What if lifestyle changes are made? In insurance, scenarios are used to estimate financial impact; in healthcare, scenario simulation can estimate health outcomes. The methodology overlap is clear, both use models to project consequences of hypothetical changes. Actuarial simulation techniques could ensure that digital twin simulations properly account for extreme but plausible scenarios, and quantify the?confidence intervals?of predictions. In life settlements, a poorly performing model can cost investors; in healthcare, an inaccurate model could misguide treatment, so both fields stress?model validation. Actuaries’ practice of back-testing models against actual outcomes and calculating?error margins (actual-to-expected ratios) could translate to healthcare by continually comparing twin predictions with real patient outcomes to gauge accuracy.
Risk Stratification and Scoring:?The concept of stratifying individuals into risk classes is second nature to insurers (preferred, standard, substandard rates, etc.). Medicine also classifies patients by risk (e.g. risk scores for surgical mortality or disease recurrence). Actuarial risk scoring models – possibly enhanced with AI – can provide more?granular risk tiers. For example, an insurer’s ML model might score a life settlement prospect as having a 12-year life expectancy with certain confidence, using dozens of variables. A digital twin could similarly output a?personalized risk score?for a patient’s 5-year survival or complication risk, based on myriad inputs (genetics, vitals, imaging, lifestyle). Cross-industry sharing of how these scores are constructed and validated (ensuring they’re interpretable and fair) would benefit healthcare. Notably,?explainability?is important in both domains: insurers must explain to investors what drives mortality risk, and doctors must explain to patients what drives health risk. AI models used in both can incorporate techniques to highlight key factors (e.g. a twin might show that genomic marker X and blood pressure are driving a prediction, akin to an insurer’s model highlighting smoking and activity level as top factors).
Digital Twin Diagnostics and Multi-Omics in Healthcare
What is a Healthcare Digital Twin??A digital twin in healthcare is essentially a?virtual replica of a patient, continuously synchronized with the patient’s data. It incorporates a wide array of data, medical history, diagnoses, lab results, imaging, treatments, and real-time sensor readings, along with genetic and molecular profiles. This comprehensive data integration creates a rich, multidimensional representation of the person. Advanced analytics (often AI and machine learning models) then use this data to simulate biological processes and disease progression within the twin. The goal is to enable highly personalized diagnostics and predictive modeling: clinicians can test interventions on the twin (in silico) to predict how the real patient might respond, much like a flight simulator for human health.
AI-Driven Predictions:?AI plays a pivotal role in making sense of the digital twin’s complex data. Deep learning and other AI techniques can model non-linear relationships in the data, for example, how a combination of genetic risk, past exposures, and current vital signs might converge to cause a certain outcome. These models can reveal patterns not evident to human clinicians, thus providing?decision support. For instance, an AI might predict a patient’s risk of a cardiac event in the next year by comparing their twin to thousands of prior cases with similar profiles, picking up subtle warning signs. The?bi-directional data flow?of a true digital twin means the AI model continuously updates its predictions as new data comes in. In essence, each check-up, new lab result, or even data from a patient’s smartwatch can recalibrate the twin’s forecast, keeping it current.
Personalized Medicine via DT:?Digital twins are seen as a pathway to?truly personalized medicine. By accounting for a patient’s unique genetic makeup?and?real-world lifestyle, a twin can help tailor prevention and treatment. For example, in oncology, a digital twin of a cancer patient can simulate how that patient’s specific tumor (with its genomic mutations) responds to various chemo drugs or immunotherapies. This can identify the most effective treatment plan without subjecting the patient to trial-and-error. In chronic disease management, a twin could test how aggressive blood pressure control vs. standard care might impact an individual’s kidney disease progression. Such fine-tuned modeling could lead to?optimized treatment plans?that improve outcomes and reduce side effects. Moreover, digital twins might enable early warning: if the virtual model forecasts a high likelihood of, say, a complication developing, doctors can intervene sooner in the real patient. This predictive power comes directly from integrating diverse data (clinical, behavioral, omics) and learning from prior cases, exactly the strengths that advanced actuarial models and AI bring in the life settlement context.
Cross-Industry Synergies and Insights
Shared Goals, Different Data:?The life settlement industry’s methods and healthcare digital twins share a common goal, accurate prediction of future health events, but draw on different data domains. Life insurers historically focused on demographics and medical impairments; healthcare focuses on clinical and biological details. With AI and big data, these domains are converging: insurers now ingest detailed health and lifestyle data, while healthcare models are starting to incorporate population-level outcomes (essentially insurance-style cohort data) for validation. This convergence creates opportunities for cross-industry learning. For instance, the?machine learning models that insurers use to predict mortality can be repurposed to predict clinical outcomes?(like risk of hospitalization or complication), since many health predictors overlap (comorbid conditions, vital signs, etc.). Conversely, clinical digital twins that successfully simulate disease progression could inform insurers about the subtleties of certain conditions, improving underwriting for those diseases.
Incorporating Lifestyle Factors:?One clear synergy is the use of?lifestyle and behavioral data. Insurers have demonstrated that data from wearables, physical activity, sleep, heart rate, can significantly enhance risk stratification. Healthcare can leverage the same: a digital twin enriched with a patient’s daily activity and sleep patterns will yield a more holistic health model than one based only on clinic visits. This could improve preventive care (e.g. noticing sedentariness and intervening before diabetes worsens). Insurers’ experience in encouraging customers to share wearable data (often in exchange for wellness benefits or premium discounts) might also guide healthcare providers in how to motivate patients to contribute their personal sensor data to digital twin programs. Both sectors benefit from?real-world, continuous health monitoring?to paint a complete picture of an individual’s health status.
Multi-Omics Meets Actuarial Rigor:?The?multi-omics integration?in healthcare could get a boost from actuarial methodologies. Actuaries are skilled at dealing with incomplete data and making probabilistic inferences, useful when not every patient will have every omic dataset available. They also bring tools for?high-dimensional data?reduction (identifying which variables truly matter) and avoiding overfitting by requiring statistical credibility. On the flip side, the insurance industry can look to healthcare’s multi-omics advances as a preview of what might eventually inform human longevity prediction. For example, epigenetic clocks (patterns of DNA methylation that estimate biological age) could one day complement chronological age in underwriting. Early research in personalized medicine might identify, say, proteomic markers that indicate frailty or resilience; insurers could adopt those as underwriting criteria if validated.?Cross-industry research collaborations?could explore these biomarkers in both clinical outcomes and mortality risk contexts to see how strongly they predict long-term health.
Actuarial Standards in Healthcare AI:?As AI models proliferate in healthcare, there is a case for applying an?actuarial standard of practice?to them. The insurance industry requires models to be transparent, validated, and conservative in assumptions (with margins for adverse deviation). Applying similar diligence to digital twin models – documenting their validation against real outcomes, monitoring their calibration over time, and ensuring interpretability – can increase trust in these tools among clinicians. In fact, commentators have noted that while actuarial models in insurance are governed by strict standards, no analogous body yet oversees “actuarial AI” in healthcare. Establishing such standards, borrowing from insurance, would refine digital twin diagnostics to be both innovative and reliable.
Improving Predictive Healthcare Models:?Ultimately, blending the strengths of both industries can refine predictive healthcare models. Insurance offers?decades of experience quantifying risk and uncertainty?at a population level, while healthcare offers deep biological and clinical insight at an individual level. For example, an insurer’s model might tell us that, across 10,000 people with similar profiles, 5-year mortality is 8%, whereas a digital twin might simulate why a particular person is or isn’t in that 8%. Combining these approaches, a physician could get both a?probabilistic risk estimate?and a?personalized narrative?of a patient’s health trajectory. Cross-industry data sharing (with appropriate privacy safeguards) could also enlarge the datasets available for training AI models. Imagine if de-identified life insurance datasets (millions of lives with basic health info and outcomes) were combined with detailed clinical datasets; researchers could build extremely robust models that account for both macro-level trends and micro-level details.
Conclusion
Advanced predictive methodologies developed in the life settlement and insurance industry, from AI-driven longevity modeling to actuarial risk simulation, have strong potential to enhance healthcare’s digital twin diagnostics.?AI and machine learning?are improving mortality predictions by learning from real-world medical and lifestyle data, a principle equally valuable for predicting disease outcomes in patients. The use of?longitudinal real-world data?and continuous health monitoring has proven its worth in refining risk assessments, and porting these data streams into digital twins can make virtual patient models more accurate and responsive to change. Meanwhile,?multi-omics integration?enables a new level of personalized medicine in digital twins, and insurers’ tentative steps into genetics show a recognition that molecular data can enrich traditional models. Actuarial science offers a disciplined framework for dealing with uncertainty, techniques like cohort analysis, survival modeling, and scenario testing, which can lend rigor to digital twin development and validation.
In essence, what one industry learns about?human health risk?can inform the other. A future where an individual’s “health digital twin” might also estimate their insurance risk (and vice versa) is conceivable, given the overlapping analytics. By embracing cross-industry applications, we can accelerate the refinement of?predictive healthcare models, leading to better-informed clinical decisions, proactive interventions, and ultimately improved patient outcomes. The marriage of actuarial insight with biomedical innovation exemplifies a promising path toward?more accurate, personalized, and trustworthy predictive models?in both finance and medicine.