Published by Wanda Rich
Posted on December 9, 2025
6 min readLast updated: February 26, 2026
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Alternative investments have long carried an aura of complexity, specialized models, idiosyncratic processes, and scarce disclosure. That opacity has kept many institutions on the sidelines and left retail investors almost entirely out of the conversation.
Alternative investments have long carried an aura of complexity, specialized models, idiosyncratic processes, and scarce disclosure. That opacity has kept many institutions on the sidelines and left retail investors almost entirely out of the conversation. Yaniv Bertele, founder of EverOak Innovations, is working to change that. His thesis is straightforward: use technology to translate specialist risk into investor‑grade metrics, so the market can evaluate alternatives with the same rigor it applies to traditional securities.
"Insurance markets and capital markets speak different languages," Bertele says. "You don't fix that with more education alone, you build a translation layer that makes complex assets legible to investors."
The Transparency Gap in Alternatives
Many alternative strategies, especially those measured at fair value using unobservable inputs, are, by definition, Level 3 assets under IFRS. That includes certain insurance‑linked exposures and life settlements (the secondary market for life insurance policies). Pricing often depends on proprietary models and specialist judgment, which makes consistent underwriting and independent validation difficult.
Life settlements illustrate the challenge well. The legal foundation dates to 1911, yet the market matured slowly, with legacy processes that Bertele characterizes as "spreadsheets and expert intuition." Methodologies for assessing longevity have historically varied across providers, leading to valuation dispersion and investor skepticism. The result: despite offering uncorrelated, actuarially driven cash flows, the asset class has remained underutilized by institutions that demand transparency and comparability.
The same structural issue appears across other corners of alternatives: model opacity, inconsistent inputs, and limited portability of analysis across managers. Without a common language, due‑diligence teams struggle to evaluate risk and allocate at scale.
Building the Translation Layer: AI With Audit Trails
Bertele's solution is to translate specialist inputs into the financial outputs that investment committees underwrite. The core idea: deploy explainable, auditable AI to evaluate policy‑ and portfolio‑level risk, then present the results in capital‑markets terms, expected IRR and dispersion, duration, drawdown sensitivity, and correlation across regimes.
"Technology should act like a 'Google Translate' between insurance and investing," he explains. "It takes medical and policy data on one side and outputs investor‑grade metrics on the other."
At EverOak Innovations, that translation starts with the medical record and policy file. Machine‑learning pipelines structure the data, estimate life expectancy with confidence intervals, and generate reproducible outputs. Crucially, every material driver is traceable. Model inventories, version control, out‑of‑sample testing, challenger models, drift monitoring, and periodic Actual‑to‑Expected (A/E) calibration turn underwriting from a black box into a measured process. The objective isn't a perfect forecast; it's a validated, explainable estimate that supports consistent valuation and portfolio construction.
The transparency extends beyond underwriting. Side‑by‑side analytics show how life‑settlement portfolios would have behaved across market regimes and how they compare with traditional sleeves. When CIOs can see performance, variability, and correlation in a format they already use, the mysterious becomes measurable.
From Institutional Comfort to Broader Access
Clarity unlocks more than diligence, it enables fits for purpose packaging. Bertele is careful to match structure to the physics of the asset: For more liquid insurance‑linked exposures (e.g., catastrophe risk), listed fund wrappers can be appropriate where daily dealing, disclosure, and the liquidity‑risk program can be satisfied. For illiquid, model‑priced exposures such as diversified life‑settlement portfolios, interval funds or listed closed‑end vehicles are typically the right on‑ramp. These structures align redemption terms with cash‑flow reality and still provide standardized reporting to a broader investor base. "Democratization isn't about forcing daily liquidity where it doesn't belong," Bertele notes. "It's about delivering transparency and access through the structures that fit the underlying cash flows."
On the supply side, transparency also benefits policyholders. Digital marketplaces that present multiple bids and embed licensing and disclosures can reduce friction and lift outcomes for seniors exploring a sale. Technology lowers search costs for buyers and sellers alike, accelerating price discovery while preserving regulatory guardrails.
Regulation and Ethics: Foundations, Not Afterthoughts
Regulation in life settlements is state‑based and mature across most of the US. A majority of states (and Puerto Rico) have specific life‑settlement statutes; two additional states regulate viaticals only; a smaller group has no specific statutes. These frameworks establish licensing, disclosures, eligibility, and oversight, exactly the prerequisites for transparency at scale.
Ethical questions often stem from misunderstanding. Mortality risk underpins everyday financial products, pensions, annuities, traditional life insurance. Life settlements provide another option for policyholders, potentially offering greater value than surrender depending on individual circumstances. When investors can see the data, understand the methodology, and trace how value is created for all parties, the discussion moves from taboo to informed consent.
The Convergence Tailwind: Longevity × Data Science
The "why now" is twofold. First, demographics: aging populations are re‑shaping balance sheets globally. Second, tooling: we finally have the data pipelines, compute, and governance frameworks to make actuarial cash flows auditable and comparable at scale.
"Ten years ago, parts of this market were running on legacy processes," Bertele says. "Today we can apply algorithms, predictive analytics, and automation, paired with rigorous governance, to make those same cash flows legible to capital."
As transparency improves, market structure follows: tighter spreads, deeper participation, and more consistent reporting. Institutions gain a credible source of diversification; policyholders gain better pricing; and the entire market benefits from clearer standards.
The Road Ahead
Bertele's vision extends beyond life settlements. The same translation approach, explainable AI + standardized reporting, can illuminate other historically opaque corners of alternatives, from certain insurance‑linked assets to fractional real estate and asset‑backed credit. The common denominator is not the asset itself but the operating system around it: evidence, comparability, and fit‑for‑purpose structures.
"Transparency will be the price of admission," he says. "Firms that deliver clarity, without sacrificing performance, will earn the trust to scale."
Bottom line: Making alternatives investable at scale is less about marketing and more about method. By turning specialist inputs into decision‑useful outputs, and by aligning wrappers, governance, and reporting with the underlying risk, Yaniv Bertele and EverOak are showing how technology can bring light to the opaque, and access to the overlooked.
For informational purposes only; not investment, legal, or tax advice.
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