Joy Agwunobi
Life insurers around the world are facing what experts describe as one of the most complex investment environments in decades, driven by volatile macroeconomic conditions, stubborn inflation, and an ever-widening spectrum of investable assets.
According to Ortec Finance, a global financial modelling and analytics firm, this evolving landscape is exposing deep structural weaknesses in how insurers approach Asset-Liability Management (ALM), particularly their continued reliance on spreadsheet-based models.
In a new industry insight, Ortec Finance cautioned that while the insurance industry has made progress in product innovation and risk-based capital management, its investment processes have not evolved at the same pace. Many insurers, the firm noted, still rely on simplistic Strategic Asset Allocation (SAA) models often built on spreadsheets that are ill-suited for today’s uncertain market realities.
“Insurers are operating in a world of growing balance sheets, shifting regulatory frameworks, and rising asset complexity. Yet, too many are still using tools designed for a simpler era,” Ortec Finance noted.
The firm argues that spreadsheets, though familiar and easy to use, lack the flexibility and precision required to navigate the non-linear risks, illiquidity constraints, and capital optimisation challenges now shaping insurance investment strategies.
Across the global insurance industry, firms are diversifying beyond traditional fixed income into private credit, infrastructure, and alternative assets in search of yield and portfolio diversification. Ortec Finance’s recent global survey reveals an upward trend in allocations to private markets ,a move that, while promising, brings new challenges such as irregular cash flows, default risk, and valuation uncertainty.
Without advanced ALM modelling, insurers risk misunderstanding how these alternative assets perform across economic cycles, potentially creating mismatches between assets and liabilities that only surface under stress scenarios.
“Spreadsheet-based tools are simply not built to capture the intricacies of alternative assets,” the firm stated, adding that the limitations of such methods can distort portfolio decisions and expose insurers to solvency risks during downturns.
Beyond returns, insurers are increasingly being judged on their ability to manage regulatory capital efficiently. Ortec Finance highlighted that under frameworks such as Solvency II and Solvency UK, insurers are required to hold additional capital to protect against investment risks. The amount held depends on the risk profile of their assets, a factor that can significantly shape investment allocation choices.
Global markets today are driven by deep uncertainty from the debate over whether inflation is structural or temporary to diverging expectations on how long high interest rates will persist. For insurers, these macroeconomic disagreements are not academic exercises; they directly impact solvency, return expectations, and the sustainability of policy guarantees.
Ortec Finance said advanced ALM models now enable insurers to “systematically test competing worldviews” comparing how their balance sheets might evolve under different economic regimes. This includes designing bespoke scenarios that go beyond historical data, such as prolonged stagflation, trade wars, or climate-related shocks.
“Scenario testing is no longer just about risk management; it’s a strategic decision-making tool,” the firm explained, “It helps insurers see beyond a single economic narrative and position competitively for an uncertain future.”
One of the most critical limitations of traditional SAA, according to the firm, is its deterministic nature projecting a single “best-guess” path for returns and liabilities. This can create a false sense of precision.
Stochastic modelling, in contrast, simulates thousands of possible economic paths, providing a full distribution of potential outcomes. This approach reveals not just expected returns but also downside risks, solvency pressures, and liquidity shortfalls offering a much richer view of balance sheet resilience.
The firm, however, pointed out that even traditional stochastic approaches have limits, particularly in capturing non-linear interactions in modern insurance portfolios. This, it said, is where its innovation, Scenario-Based Machine Learning (SBML) comes in.
As insurers embrace increasingly complex portfolios and objectives such as liquidity management, Risk-Adjusted Return on Capital (RAROC), and distributable earnings, Ortec Finance believes Scenario-Based Machine Learning (SBML) represents a breakthrough in investment optimisation.
By integrating machine learning algorithms with advanced scenario generation, SBML can uncover hidden patterns in data, adapt to new market conditions, and identify optimal strategies that balance multiple objectives simultaneously.
“SBML moves insurers beyond point-estimate planning into probabilistic, adaptive decision-making, a capability essential for long-term resilience,” Ortec Finance noted.
The report also highlighted how emerging product structures, such as Indexed Universal Life (IUL), are redefining ALM requirements. These products combine fixed income components with equity-linked returns, often managed through derivatives or structured products.
Managing such dual exposures effectively, Ortec Finance said, requires holistic modelling that captures how index returns, hedge costs, and disintermediation risks evolve under different macroeconomic regimes. Traditional risk-neutral models, while useful for pricing guarantees, do not suffice for long-term asset allocation and capital management.
“Insurers who fail to align their ALM with product complexity risk underestimating embedded risks or eroding solvency margins over time,” the firm warned.
Another global trend reshaping the sector is asset-intensive reinsurance (AIR) where reinsurers assume not only liabilities but also the underlying assets and reinvestment risks. In these transactions, competitive advantage increasingly depends on demonstrating deep expertise on the asset side.
Robust stochastic modelling, Ortec Finance explained, enables reinsurers to capture the interaction between credit spreads, inflation shocks, and liability dynamics, while optimising portfolios for capital efficiency and regulatory transparency.
Traditional SAA frameworks, which categorise portfolios into rigid asset classes, are also being reimagined. Ortec Finance promotes a Total Portfolio Approach (TPA) that evaluates each investment based on its contribution to the fund’s overall objectives — risk, return, liquidity, and solvency.
Under this framework, risk budgets can be distributed across factor exposures (such as credit duration or inflation sensitivity) rather than strict asset class limits. This allows Chief Investment Officers to manage portfolios dynamically, shifting between public and private assets based on true economic exposure rather than accounting classifications.
“Advanced ALM systems can bridge the regulatory-driven SAA process with forward-looking, TPA-style decision-making,” the firm said, adding that insurers that master this flexibility will be better positioned for resilience and growth.
The firm concludes that insurers who embrace sophisticated ALM frameworks from stochastic simulations to machine-learning optimisation will gain decisive advantages: stronger solvency, improved capital efficiency, and the confidence to innovate safely in an uncertain world.
