Insurers are increasing allocations to private credit and equity in search of yield, yet many are discovering that their risk modeling frameworks and data infrastructure were not built for this level of opacity. As allocations grow, the gap between how insurers invest and how they measure risk is widening in ways that are often difficult to detect until market conditions change.
Where traditional risk models break down
At the most foundational level, the disconnect begins with how risk models are built. Most commercial risk models are designed for investors focused on price sensitivity and portfolio returns. A typical scenario might examine the impact of a 20% decline in the S&P 500, which works well for portfolios dominated by public market assets that are marked to market. Insurers, however, are focused on projected cash flows and how those cash flows support projected liabilities, rather than price movements, and this is where there are far fewer commercial solutions available.
There has been meaningful progress in expanding risk models to include private assets, yet even if those models perfectly captured private holdings, insurers still face a structural challenge. They do not mark private assets to market, and what matters most is not short-term price movement but the cash flows those assets will generate over time. Insurers want to model how those cash flows evolve across different economic scenarios, and that capability remains limited.
Regulatory requirements have driven the development of tools that model interest rate changes and their impact on projected asset cash flows, yet insurers increasingly want to go beyond a simple interest rate shock. A 100-basis point shift in rates offers a useful reference point, particularly for portfolios dominated by bonds that primary risk comes from interest rates, but firms also want to understand the economic factors driving that shift and how those factors influence both assets and liabilities. For example, is that interest rate change associated with a change in GDP, inflation, or employment that drives the repayment rate or probability of default for their holdings. This level of dynamic scenario analysis remains difficult to achieve with many existing solutions.
The challenge becomes more complex for insurers that source private assets directly or via separately managed accounts (SMAs). When exposure is gained through fund structures, vendors may provide more standardized data, yet direct sourcing requires insurers to build internal processes to capture the inputs needed for modeling and SMAs require a third-party manager to deliver deal-level reporting to support internal risk modeling. Those inputs must then be incorporated into economic risk models that reflect how portfolios behave across different scenarios. None of this is impossible, and both in-house and vendor solutions are evolving to close the gap, but there are fewer plug-and-play solutions aligned to this style of investing compared with the many options available to traditional benchmark relative managers.
The growing data gap behind private asset allocations
These limitations shape how insurers operate day to day.
Building in-house requires teams with both technology and quantitative expertise, which is not an option available to every insurer. Additionally, gathering the necessary inputs for private assets requires thoughtful process analysis to gather the right inputs from the right sources. Many firms instead rely on a combination of regulatory risk models centered on interest rate scenarios and commercial risk models aligned with price risk. While neither approach perfectly reflects how insurers invest, each provides useful information that can help identify areas requiring further analysis.
Building the data foundation for private assets
Closing the gap begins with strengthening the data foundation around projected asset cash flows. Insurers need to define the inputs their models require, collect and store those inputs consistently, and ensure that outputs align with broader data strategies. When projected cash flow data is treated as a core input rather than an afterthought, firms gain greater flexibility in how they analyze risk.
Commercial risk models also continue to play an important role. Even when they do not perfectly align with an insurer's investment approach, they can provide helpful signals around portfolio exposures, and coverage of private instruments continues to improve. Used alongside internal modeling and regulatory frameworks, they contribute to a more comprehensive view of risk.
Engaging technology vendors is another important step. Insurance investment strategies are evolving quickly, and vendors are motivated to build capabilities tailored to the sector's needs. By working closely with partners, insurers can help shape solutions that reflect how they invest while giving vendors the perspective needed to refine their offerings.
The shift toward private assets reflects a broader transformation in insurance investment strategy. As firms pursue yield and diversification, visibility into portfolio behavior becomes increasingly important. Strengthening data infrastructure and risk modeling capabilities now can help insurers better understand emerging risks and align investment strategy with liability management. Otherwise, the gap between allocation strategy and risk visibility may continue to widen, making concentration risks harder to detect until they become more difficult to manage.
