The life settlement market is arguably in a strong position. The 2025 ELSA–Conning Investor Sentiment Study confirms what market participants already sense: over half of investors rated satisfaction at nine or ten out of ten, more than a third plan to increase exposure, and life settlements are increasingly classified within institutional “resilience buckets” alongside private credit and real assets. The diversification case is well understood, the secondary market has matured significantly, and investor demand continues to grow.
The question facing the industry now is not whether the proposition works, but whether the analytical tools are keeping pace with the capital flowing into it. Mortality modelling, valuation methodology and experience reporting have evolved more slowly than the market itself. That creates an opportunity: participants who invest in better analytics now will be better placed to attract and retain institutional capital over the next decade.
The Constant Mortality Multiplier: Where It Works and Where It Falls Short
The dominant approach to modelling impaired mortality in life settlements is the constant mortality multiplier (CMM). A base mortality table, typically the 2015 VBT, is scaled by a flat multiplier to match a third-party life expectancy estimate. A multiplier of 200%, for example, assumes the insured will die at twice the base rate at every future age. The appeal is simplicity, and for many applications, the CMM has served the industry well as a workable approximation.
Where the CMM falls short is in capturing how impaired mortality behaves over time. For many impairments, excess mortality is highest following diagnosis and then decays over time as the individual either succumbs to or survives the acute phase of risk (this is particularly pronounced in cancer and cardiovascular disease). A flat multiplier cannot capture this dynamic because it applies the same scaling factor at every future age and therefore copies the mortality shape of the underlying VBT table (built for standard health insureds). It cannot produce a pattern where excess mortality is high initially and then fades over time.
The 2015 VBT compounds this effect. It embeds a strong select effect (a period of lower mortality rates in the first few years after policy issue underwriting, reflecting the health screening that took place) which is appropriate for newly underwritten lives on the primary market but is less appropriate for policies that have been in force for decades with insureds having high levels of impairment. There may be a select effect for policies transacted on the secondary market, but it is not comparable in magnitude. Applying a constant multiplier to this base produces curves that tend to overstate mortality in later durations and understate it in early durations relative to what the data shows. This is not a flaw that invalidates the CMM for all purposes, but it does create predictable patterns in fund performance that are worth understanding.
Understanding the Fund Performance Lifecycle
A recognisable pattern occurs across the life settlement market which is a mechanical consequence of how the CMM interacts with a maturing portfolio and understanding it is the first step to managing it.
Early Years: Strong Reported Performance
The CMM’s early duration mortality profile expects insureds to survive, so monthly survival probability sits close to 100%, which has the impact of reducing life expectancies at the rate of close to one month for every month elapsed. This means that policy values appreciate at close to the maximum rate, which supports higher portfolio valuations. And with very low expected mortality in early durations, even modest actual deaths produce actual-to-expected (A/E) ratios well above 100%. These are features of the modelling framework.
Mid Life: Performance Moderates
As the portfolio matures, these dynamics shift. Because the CMM now produces expected mortality that is higher in later durations, monthly survival probability drops significantly below 100%, so aged LEs reduce by less than a month per month, and therefore, policy appreciation slows. A/E ratios can fall below 100% as actual mortality falls short of the model’s higher expectations.
The LE Refresh Challenge
This is where the CMM creates its most counterintuitive dynamic. Life settlement portfolio managers might want or need to refresh the life expectancy estimates on the insureds in their portfolio because they may be several years old. Under the CMM framework, this creates a compounding effect.
First, because the model consumed LEs aggressively in early durations (close to one month per month), fresh estimates will, on average, come in longer than the current aged LEs, all else being equal, because market underwriters don’t use the 2015 VBT in their estimates and apply life settlement-based tables which don’t share the same steep slope for impaired mortality.
For example, as life expectancy is calculated as the sum of all future probabilities of survival plus a half, if a month with very close to one survival probability has elapsed, this reduces the life expectancy by almost a whole month. Aggregated across several years, this produces a material difference and write down: longer LEs mean longer expected time to maturity, higher premium payments and lower present values.
Second, the fresh LE requires solving for a new constant multiplier, which resets the mortality curve, and so expected mortality is once again lower in the near term.
The practical consequence is that LE refreshes under the CMM can produce valuation hits that are disproportionate to the actual change in the insured’s health. This discourages regular refreshes, which in turn means some funds carry LE estimates that are several years old. Moving to a modelling framework that reduces this effect would allow fund managers to refresh LEs more regularly, which benefits transparency and investor confidence.
Three Tools to Strengthen the Foundation
The challenges described above are not inevitable, however. They are largely a consequence of the use of the CMM, not of the asset class itself. Better analytical tools exist and adopting them would materially change the trajectory.
Durational Mortality Curves
Replacing the constant multiplier with durational mortality curves that model the actual behaviour of excess mortality over time changes each phase of the lifecycle of a policy, and therefore, portfolio. Higher assumed mortality in the earlier years means less aggressive LE ageing and policy value appreciation and easier LE refreshes, and lower assumed mortality in the later years means more sustainable A/E ratios and performance mid-term (the caveat to this is that the life expectancies need to be of an accurate length in the first place).
The result is a mortality profile that more closely reflects real experience. Early returns are more moderate, but mid-life performance is more sustainable. Most importantly, the LE refresh challenge is materially reduced: because the model does not artificially accelerate LE ageing, the gap between aged and refreshed estimates is smaller, often significantly so. Fund managers can update LEs more regularly, as good governance requires, without triggering disproportionate write downs.
A flatter curve shape also produces a higher policy valuation when LEs are relatively new, which could create additional buying opportunities and acquisition gains on the secondary and tertiary markets because the model expects a greater share of death benefits to arrive sooner, and as cashflows are discounted with time, this means less overall discounting of the death benefit.
Stochastic A/E Analysis
Adding stochastic simulation to A/E analysis transforms it from a reporting exercise into a diagnostic tool. Instead of a single A/E ratio, funds can provide confidence intervals that distinguish genuine mortality deviations from statistical noise. Instead of “our portfolio A/E ratio is 92%,” a fund can state, “our portfolio A/E ratio is 92%, and the 95% confidence interval includes 100%, which falls within the expected range for a portfolio of this size and composition.” This is grounded in statistical actuarial rigour rather than assertion and gives investors a framework for evaluating performance that goes beyond headline numbers.
Independent Mortality Assumption Audits
Neither curve shape analysis nor stochastic A/E achieves its full potential if produced internally because the team producing the analysis has a natural interest in the results supporting the fund’s reported position, even if that interest is entirely unconscious. What would strengthen investor confidence most is a culture of independent, annual mortality assumption audits, conducted by actuarial specialists with no stake in the fund’s reported performance, meaning no financial incentive to present the mortality picture in any favourable way.
This is distinct from, and more fundamental than, the valuation audit exercises that dominate current practice. Too often, mortality assumptions are assessed only in the context of whether a policy can be bought or sold at a given price, a transactional lens that overlooks a question that is fundamental to long term returns: are the mortality assumptions themselves sound?
Returns depend on many factors, including premium optimisation and deal selection, but mortality assumptions underpin all of them. A fund may report sound valuations while its underlying mortality model has not been independently tested against actual experience.
An annual mortality assumption audit, examining curve shapes, A/E experience by duration and impairment, and the statistical significance of any deviations, serves the same function as an independent financial audit: not a response to problems, but a routine discipline that builds confidence over time.
What This Means in Practice
For investors conducting due diligence, asking what mortality modelling approach the fund uses, how old the life expectancy estimates are, what would happen to valuations if LEs were refreshed and has the A/E ratio changed as the portfolio has matured are not adversarial questions; they are similar to the questions any sophisticated investor would ask about any long duration, illiquid asset class.
For fund managers, adopting better mortality analytics is both a challenge and a competitive opportunity. Moving to durational curves may moderate early reported returns relative to competitors still using the CMM. But a fund built on more accurate mortality analytics will deliver more consistent returns, encounter fewer LE refresh shocks, and therefore, build deeper investor trust. In a maturing market, the fund that can demonstrate stable, analytically defensible performance is well positioned to attract the next wave of institutional capital.
The Industry Is Ready
The life settlement asset class is fundamentally sound. Investor demand is growing, diversification benefits are real, and the resilience characteristics that attract institutional capital are genuine. The analytical tools to support the next phase of growth already exist. Durational mortality curves, stochastic A/E analysis and independent mortality assumption audits provide a path to stronger investor confidence and more sustainable fund performance. The trade-off is more moderate early returns and increased annual expenditure on independent review, but these are investments in long term credibility, not costs. The investors who allocate capital to the fund managers who move first will benefit most.
Liam Bodemeaid is Founder & Principal Actuarial Consultant at Paragon Longevity Analytics
Any views expressed in this article are those of the author(s) and may not necessarily represent those of Longevity & Mortality Investor or its publisher, the European Life Settlement Association
