VM-22 as a catalyst for enterprise alignment and operational excellence: PwC

In our September 2025 article, “Navigating VM-22: Insights and Implementation Challenges,” we explored emerging interpretation questions and the early operational considerations surrounding VM-22. Since then, the industry has continued to exchange ideas at a rapid pace, reflecting the significant shift this framework represents for statutory reserving. These discussions highlight the need for close collaboration across actuarial, investment, pricing, finance, and risk management teams. This second article discusses practical enterprise-level considerations insurers may face as they refine their VM-22 strategies.

The transition to VM-22 marks a major change for companies writing non-variable annuities. It expands the scope of principle-based reserving and introduces more sophisticated modeling requirements, greater reliance on stochastic projections, and stronger dependencies among assumptions, assets, and liability mechanics. Implementation may involve coordination across the organization to align models, assumptions, governance structures, operational processes, and long-term strategic goals.

Strategic planning and enterprise readiness for VM-22

A key early phase of VM-22 implementation involves determining the timing and scope of adoption. Companies may want to evaluate which blocks may benefit most from early adoption and balance those benefits against operational constraints, including model readiness, assumption governance, hedging support, and data quality. Products such as pension risk transfer annuities and fixed indexed annuities (FIAs) with guaranteed withdrawal benefits may result in different reserve outcomes, especially when alternative reserve financing options such as reinsurance are not present.

Another important consideration centers on the reserving methodology. VM-22 uses deterministic reserves, stochastic reserves, or Commissioner’s Annuity Reserve Valuation Method (CARVM) depending on the results of the Stochastic Exclusion Test and the Single Scenario Test. These tests help determine whether economic volatility significantly affects reserve outcomes.

Aggregation represents another strategic consideration under VM-22. Companies may aggregate product blocks only when they use the same reserving method and are managed under an integrated asset liability management strategy. Aggregation can generate diversification benefits by recognizing offsetting liability profiles. An example may include when combining payout annuities with accumulation products such as single premium immediate annuities (SPIAs) and multi-year guaranteed annuities (MYGAs).

Cross-functional alignment on assumptions and modeling architecture

Assumption development involves policyholder behavior, mortality, and expense assumptions. Policyholder behavior can be dynamic when influenced by factors such as account value, market conditions, or guaranteed benefits. Mortality assumptions are often set by segment from applicable experience or industry tables, with reserve-increasing uncertainty margins, credibility blending, and VM-22 mortality-improvement adjustments. Expense assumptions may include direct and overhead components for completeness. Companies may apply margins individually or in aggregate while demonstrating that aggregate margins achieve appropriate conservatism.

VM-22 relies on the Generator of Economic Scenarios (NAIC GOES) economic scenario generators to produce yield curves and equity returns for long-duration projections. Companies may use non-prescribed scenario generators, provided they do not produce a reserve that is materially lower than that produced using the prescribed scenario generators. The large number of scenarios required often necessitates enhancements to data systems, modeling platforms, and integration processes. Scenario reduction is permitted, and some companies may seek to reduce resource demands by using fewer scenarios, so long as evidence shows the approach does not materially understate DR or SR. Additionally, access to the generator may require a paid subscription.

Integrating investment and hedging insights into holistic asset liability management

VM-22 marks a major change from previous statutory frameworks by explicitly integrating asset-liability interactions in reserve calculations. Insurers may consider aligning asset allocation by matching asset and liability durations or cash flows. The objectives need to be carefully considered, balancing efficient reserving, capital planning and forecasting. Modeling begins with starting assets valued consistently with annual statement values, including hedge instruments held for the contracts, general account assets approximately equal to statutory reserves net of those hedges, and the allocated pre-tax interest maintenance reserve (PIMR) attributable to the selected assets. This integration depends on detailed asset modeling, particularly for structured and alternative assets, dynamic crediting mechanisms and hedging programs, which need to be modeled alongside liability cash flows. Companies may need to enhance asset projection methods to accurately reflect the assets supporting VM-22 business.

Products with significant asset dependencies may require advanced asset modeling that includes derivatives such as interest rate swaps and floating-rate bonds. To address computational challenges, insurers can apply efficiency techniques like policy clustering and scenario reduction, supported by parallel computing or cloud platforms. In addition to evaluating vendor solutions, insurers may consider developing in-house modeling platforms leveraging open-source programming languages to meet VM-22's needs for flexibility, transparency, and automation.

One of the earliest considerations for VM-22 implementation is determining whether a future hedging strategy supports a block of business. This decision influences both reserving methodology and modeling design. When a future hedge is planned, projection requirements differ depending on whether the hedge supports index credits or guarantees. Index-credit-only hedges may be incorporated subject to minimum margin requirements. More complex hedging programs, including those supporting guaranteed withdrawal benefits, involve explicit modeling of hedge effectiveness, cost structures, and back-testing evidence. VM-22 could involve calculating weighted average reserves under both best-effort assumptions and adjusted assumptions reflecting potential deviations in hedge performance.

Reserves will be sensitive to actual asset returns and reinvestment strategies; this sensitivity reinforces the linkage between asset and liability modeling, including private structures and other complex assets, and requires transparency and asset cash flow modeling expertise, potentially with stochastic processes. For complex or structured assets like Residential Mortgage-Backed Security (RMBS) and Commercial Mortgage-Backed Security (CMBS), sophisticated platforms or proprietary systems are typically required to dynamically model cash flows under varying economic scenarios. Emerging asset classes including private structured credit and equity-like instruments (e.g., limited partnerships), demand baseline cash flow models tailored to their unique characteristics, as traditional platforms often fall short. Furthermore, developing an understanding of complex assets such as private credits, real estate, and private equity, and accurately mapping them to prescribed indices within VM-22, adds another layer of complexity for insurers to address alongside hedge effectiveness and cost modeling.

Unifying pricing, product strategy, and reserve outcomes

Pricing and valuation teams may find benefit in working closely together to align between product profitability assessments and long-term reserve methodologies under VM-22. Pricing models often incorporate nested stochastic analysis to evaluate capital strain, profitability, and hurdle rate expectations. Although simplifications may be used for product development timelines, pricing assumptions and methodologies remain, in most cases, directionally aligned with valuation results. Additionally, depending on the aggregation decision made by valuation, pricing results for standalone products may differ; these differences may need to be acknowledged and, where possible, reconciled.

Valuation teams often rely on pricing assumptions when performing valuation for new products or innovative designs. Misalignment in assumptions can lead to inconsistent results across financial reporting, management reporting, and regulatory discussions. A unified approach may enhance credibility and potentially improve capital efficiency.

Optimizing reinsurance structures under VM-22

Reinsurance plays an increasingly important role under VM-22 business due to its intensive nature. The framework requires companies to calculate reserves both before and after reinsurance¹, which may offer additional insight into retained and transferred risks. A margin for counterparty default is required when the reinsurer is known to be financially impaired².

Reinsurance arrangements may introduce features that need to be explicitly modeled under different reinsurance arrangements. Treatment under VM-22 is more customized than under CARVM due to the explicit requirement of asset modeling. Retrospective adoption may require reevaluating existing treaties. Results of exclusion tests may also differ before and after reinsurance, especially for complex structures.

VM-22 reserves are generally lower than CARVM for certain products, such as FIAs with guaranteed withdrawal benefits, potentially reducing the reliance on reserve financing agreements with reinsurers operating under different reserving and capital frameworks. While VM-22 brings U.S. statutory reserves closer to an economic framework, it retains features like the cash surrender value (CSV) floor. The potential aggregation benefits introduce additional considerations as the fungibility of assets can be leveraged in the reserving process. Consequently, reserve financing from the U.S. to offshore reinsurers—including those in Bermuda and other jurisdictions—is expected to evolve.

¹ Section 3.B of VM-22

² Section 5.A.2.a.iii of VM-22

Governance, risk oversight and operational excellence in VM-22

In our view, VM-22 heightens the importance of governance, model validation, and operational processes. Stochastic modeling generally requires strong data systems, reproducible modeling processes, and comprehensive controls. Many companies may need to integrate asset and liability data environments to achieve consistency across valuation, pricing, ALM, and risk management. There may be calculation processes that directly feed into the VM-22 models that need to be clearly documented.

Documentation can be resource‑intensive for insurers, requiring substantial time and effort. The VM-31 report must describe assumptions, methodologies, hedging strategies, complex assets, scenario testing and reduction, governance frameworks, and aggregation decisions. As VM-22 evolves, companies will need to consider the likely need to continuously update documentation, controls, and governance processes to maintain compliance and model credibility.

Closing remarks

VM-22 represents a significant evolution in statutory reserving for non-variable annuities. Companies that invest early in modeling readiness, assumption governance, interdepartmental collaboration, and asset integration may well be positioned to achieve compliance and strengthen strategic decision-making at the organization level. Cross-functional coordination across actuarial, pricing, investment, finance, and risk functions is beneficial for successfully managing VM-22's complexity and considering its potential strategic value.

The comments expressed are personal to the authors and do not reflect the views of PwC US Group LLP, its subsidiaries or affiliates.

Gina Meng and Kenny Kwon contributed to this piece.