Holistic Cashflow Matching Group Strategic Asset Allocation Framework for Multi-Line Insurers
By Gautam Devarashetty, Ruian Chen, Joy Chen and Seong-Weon Park
Risks & Rewards, September 2024
There has been significant volatility in interest rates over the past few years due to monetary policies implemented in response to the COVID-19 pandemic and inflationary pressures. As a result, asset-liability management (ALM) has become increasingly important and companies lacking a strong ALM framework are facing significant challenges in achieving financial targets.
Recently, rating agencies have renewed their focus on assessing companies’ ability to manage interest rate risk, which is crucial for insurers’ long-term financial stability. In 2023, S&P Global implemented revisions to its risk-based capital model which can result in increased capital requirements if a company’s ALM mismatch materially differs from their previously assumed one-year duration mismatch for all US life insurers. Conversely, insurers with tighter ALM duration mismatches may be assessed lower capital requirements by S&P, subject to a mismatch floor of 0.5 years.
Regulators are also highly focused on improved ALM practices. For example, under the Bermuda Monetary Authority (BMA)’s Economic Balance Sheet (EBS) framework, insurers can either use the standard approach or the scenario-based approach for calculating technical provisions. Under the standard approach, liability cash flows are discounted at BMA-prescribed standard yield curves. Under the scenario-based approach, insurers must demonstrate initial asset sufficiency and robustness of its ALM framework across nine BMA-prescribed economic scenarios. Furthermore, the scenario-based approach allows insurers to reflect the illiquidity premium embedded in portfolio asset yields if cash flows are well matched. However, a poor cashflow match will lead to a higher reserve requirement as greater amounts of assets are needed to cover liability cashflows across all nine scenarios.
Our previous article, “Introduction of Cashflow Matching Strategic Asset Allocation (SAA) Framework,”[1] introduced the concept and included a real-world case study that developed a liability-driven investment strategy for a single line of business (LOB) for a life insurer. This article expands the framework and the case study from a single LOB to simultaneously optimize asset allocation across multiple LOBs. It discusses how this multi-line SAA framework can enable an insurance company to efficiently tag its asset holdings to each LOB, identify opportunities in the public and private asset markets to enhance risk-adjusted returns, and develop a deeper understanding of the trade-off between the degree of cashflow matching and risk-adjusted returns across LOBs.
Introduction of Multi-line Cashflow Matching SAA Framework and its Benefits
A multi-line SAA framework allows for systematic asset allocations when considering the risk and return profile of each LOB as well as the aggregate entity. This not only improves risk-adjusted asset returns, but also enhances pricing results and lowers economic reserves, such as principle-based reserves (PBR), cashflow testing (CFT) reserves and BMA technical reserves. To allocate assets from the entity to individual LOBs, companies often use simplified approaches, such as pro-rata allocation. These approaches are sub-optimal but may not have a material financial impact when interest rates are stable. However, as rates rise and become more volatile, simplistic approaches may present challenges in a company’s ALM, and more sophisticated methods of asset allocation are required to optimize financial performance.
A key consideration under a multi-line SAA framework is to what extent “offsetting” impacts across LOBs are recognized. Capitalizing on interdependencies and potential synergies across LOBs within the company requires a multi-line SAA framework. For example, if investment guidelines are set at both the individual LOB and aggregate entity levels, a multi-line optimization is required to maximize risk-adjusted returns while maintaining compliance with the limits contained in the guidelines.
To address these challenges, a robust group SAA framework should follow the principles below:
- Holistic optimization: The key objective of a group SAA framework is holistic optimization across all LOBs, with implicit weighting based on each LOB’s materiality and structural challenges in matching cashflows and duration. By considering constraints across all LOBs simultaneously, one can obtain a comprehensive and balanced asset allocation that achieves overall business objectives while being within the risk appetite and limits at both the LOB and total company levels.
- LOB level pricing and in-force/performance management: Asset performance can directly affect insurance liabilities, such as crediting rates. A multi-line strategic asset allocation framework allows companies to manage investment portfolios more effectively across different products and origination cohorts. Constraints such as setting minimum target yields to ensure that the investment portfolio produces sufficient income to meet guaranteed crediting rates should be used for optimal in-force management.
- Quantitative trade-off analysis: There are many ways of allocating assets, and a robust group SAA framework enables the quantification of the trade-off between different alternatives. This enables management to make informed asset allocation decisions that strike the right balance between cashflow matching and risk-adjusted returns.
- Insights into liability risk profiles: Unlike the traditional SAA framework, which relies on simplified liability characteristics (e.g., duration, volatility, etc.), this framework utilizes actual liability cashflows under various economic scenarios and enables companies to gain valuable insights into their liability risk profiles.
- Flexible return target and real-world constraints: The proposed framework allows companies to adjust their asset allocation strategies to align with return targets and real-world constraints. Return targets can vary by LOB to meet liability guarantees and accommodate crediting rate management philosophy. Real world constraints, such as investment limits by asset class and issuer rating, should be reflected in the framework to derive investment portfolios that comply with the company’s investment guidelines and risk appetite.
- Integrated reserve sufficiency assessment: By integrating reserve sufficiency assessment into the asset optimization process, insurance companies can ensure that the portfolio derived demonstrates compliance with regulatory asset adequacy guidelines. If this check was not performed, additional assets would potentially have to be added after the optimization on an ad hoc basis, resulting in a sub-optimal allocation.
Multi-Line and Group SAA Framework: Top-down or Bottom-up Approaches
To minimize cashflow mismatches across multiple LOBs, it is essential to incorporate all LOBs into a single optimization problem. There are two ways to approach this: 1) top-down and 2) bottom-up.
The top-down approach uses single-line optimization on aggregate company liability cashflows to derive an optimal asset portfolio for the entire company. Once the company level SAA is determined, it is allocated to the individual LOBs by minimizing the sum of each LOB’s cashflow mismatch.
The bottom-up approach derives an optimized asset portfolio for each LOB by minimizing the sum of the LOB cashflow mismatch. These individual LOB portfolios are consolidated to derive the total company asset portfolio.
Both approaches utilize the investable asset universe, which includes the company’s current holdings and assets that the investment team can acquire from the open market.
Figure 1 illustrates the steps for the top-down and bottom-up approaches.
Figure 1
Multi-line optimization: 1) Top-down and 2) Bottom-up approach
Key Considerations in Implementing Multi-line Cashflow Matching SAA Framework
When implementing a multi-line cashflow matching SAA framework, there are several considerations that improve the effectiveness of asset allocation across LOBs:
- Fungibility of assets: The fungibility of assets refers to the ability to transfer assets across different LOBs and geographies within the legal entity. Corporate structure and regulatory requirements or reinsurance treaty structures can affect this. It is important to verify whether assets are fungible across each individual liability profile used in the optimization exercise. If assets are non-fungible, constraints should be added to ensure that these assets remain in a designated LOB.
- Financial impact of asset transfer: When assets are transferred or rebalanced between geographies, or across LOBs, any realized capital gains and losses, taxes, and transaction costs should be reflected. By considering these impacts, the optimization results yield a more accurate assessment of potential financial outcomes.
Conclusion
Recent interest rate volatility has underscored the critical importance of a robust ALM framework for multi-line insurers. Traditional methods of asset allocation, such as pro-rata allocation of the aggregate portfolio across a company’s LOBs, are increasingly inadequate in such an economic environment. A holistic cashflow matching SAA framework can achieve a comprehensive and balanced asset allocation that enhances risk-adjusted returns and improves ALM positioning, resulting in positive and stable financial results. Ultimately, adopting a sophisticated, multi-line SAA framework enables insurers to optimize their asset allocation strategies, improve financial stability, and better navigate the complexities of the modern financial landscape.
Case Study
Company ABC is an insurance company that manages three blocks of business: Universal life with secondary guarantees (ULSG), whole life (WL), and fixed indexed annuity (FIA). Company ABC manages assets at the aggregate entity-level.
Traditionally, Company ABC used a simplified approach by allocating assets to its LOBs on a pro-rata basis. This approach resulted in sub-optimal cashflow matching at the LOB level. Company ABC now wants to allocate specific assets to each individual LOB to track ALM performance, improve in-force management and reduce economic reserve levels across all LOBs.
Figure 2 shows the graphs of projected asset and liability cashflows using the pro-rata asset allocation approach. For FIA, the pro-rata approach leads to a significant cashflow shortfall in year six and excess cashflows in years 11+. For ULSG, there are cashflow shortfalls in the first 20 years and in years 30+, and excess cashflows in years 21-30. For WL, there are excess cashflows in the first 20 years, and cashflow shortfalls in years 30+.
Figure 2
Asset and Liability Cashflow Profile for Pro-rata Approach
Company ABC plans to implement asset tagging to allocate entity-level assets to the individual LOBs, aiming to improve the degree of cashflow matching and lower the economic reserve[1] for each LOB compared to the current pro-rata method. To further enhance risk-adjusted returns, Company ABC will explore bottom-up portfolio optimization to identify asset rotation opportunities that increase yield and further reduce cashflow mismatches.
In the first step, Company ABC allocates entity-level assets to each LOB by performing asset tagging to minimize cashflow mismatch across the LOBs without setting specific target yields at the LOB-level.
Figure 3 shows a comparison of the asset cashflows for the pro-rata and top-down economic tagging asset allocation portfolios, along with the liability cashflows. The cashflow match based on the top-down economic tagging approach is significantly tighter than the pro-rata approach across all LOBs.
Figure 3
Asset and Liability Cashflow Profile for Top-down Approach versus Pro-rata Approach
The top-down asset tagging approach leads to improved cashflow matches for projection years 1 – 30 for all three products. Due to the lack of availability of assets with tenors greater than 30, there are significant cashflow shortfalls in projection years 30+. For WL, there are excess cashflows in years 1 – 30 which are reinvested to fund future shortfalls.
Figure 4 compares the results of the pro-rata and top-down asset tagging asset allocation approaches.
Figure 4
Results for Pro-rata and Top-down Asset Tagging
For all LOBs, cashflow mismatch is lower for the asset tagging approach than the pro-rata approach. The economic reserve is 1.4% lower for the asset tagging approach ($4,468M), compared to the pro-rata approach ($4,532M). FIA benefits the most from the asset tagging approach due to increased yield and reduction in cashflow mismatch.
Though the market yields at the entity level are the same under both approaches, the market yields for each LOB vary in the asset tagging approach. FIA has the highest yield (5.20%), followed by ULSG (4.65%), and WL has the lowest yield (3.95%). This order makes sense as the crediting rate level for FIA is the highest while WL has the lowest fixed lifetime crediting rate.
To further enhance risk-adjusted returns, Company ABC performs a bottom-up portfolio optimization to seek out opportunities to rotate its asset portfolio by purchasing available securities in its universe of investment alternatives. Target yields are set from 3.5% – 5.5% at the aggregate entity level while minimizing the sum of LOB cashflow mismatch.
Figure 5 shows the SAA efficient frontier formed by a set of optimized asset portfolios for the entity and each LOB. Each dot on the efficient frontier represents an optimized asset portfolio that provides the highest return for a given level of cashflow mismatch (i.e., risk) across the LOBs.
Figure 5
Bottom-up Asset Allocation Approach Efficient Frontiers: Market Yield versus Cashflow Mismatch
In the efficient frontier for the entity, cashflow mismatch increases in return for higher target yield as one moves up the efficient frontier. This relationship generally holds true for all LOBs. The WL optimized portfolios, given entity target yields of 3.5% – 5.0%, have almost identical market yields of 3.9%. Therefore, increases in entity yields are primarily driven by changes in the FIA and ULSG portfolios. This result suggests that increasing the yields of the FIA and ULSG portfolios results in the smallest increase in cash flow mismatch necessary to achieve the additional yield enhancements. When the entity target yield is set at 5.5%, the WL portfolio yield increases to 4.4%. Achieving this entity target yield by increasing the yields of all LOBs portfolios leads to a lower cashflow mismatch compared to increasing only the FIA and ULSG portfolio yields.
Figure 6 shows two bottom-up optimized portfolios on the efficient frontier with target yields of 5.0% and 5.5% compared to the current portfolio with a 4.5% yield allocated using top-down asset tagging. The bottom-up approach identifies opportunities to enhance yields and reduce economic reserves while still maintaining a similar level of cashflow match.
Figure 6
Results Comparing Asset Tagging versus Bottom-up Optimization
Statements of fact and opinions expressed herein are those of the individual authors and are not necessarily those of the Society of Actuaries, the newsletter editors, or the respective authors’ employers.
Gautam Devarashetty, FSA, FCIA, CERA, MBA, is a manager at Oliver Wyman. He can be reached at Gautam.Devarashetty@oliverwyman.com.
Ruian Chen, PhD, FSA, is a consultant at Oliver Wyman. He can be reached at Ruian.Chen@oliverwyman.com.
Joy Chen, FSA, MAAA, CERA, is a principal at Oliver Wyman. She can be reached at Joy.Chen@oliverwyman.com.
Seong-Weon Park, FSA, MAAA, is a senior principal at Oliver Wyman. He can be reached at SeongWeon.Park@oliverwyman.com.
Endnotes
[1] https://www.soa.org/sections/investment/investment-newsletter/2024/april/rr-2024-04-devarashetty/
[2] An ALM projection engine was used to ensure that the derived starting asset portfolio was sufficient to fund liability payments across the forward scenario and 8 shocked scenarios. Where projected asset cashflows exceeded projected liability cashflows, the excess funds were reinvested using an assumed corporate yield curve. Conversely, if projected net cashflows were negative, assets were sold to generate the necessary funds.
[3] Cashflow mismatch is the sum of the present value of the projected cashflow mismatch over time without offset.