Authored by John Bowers, Actuarial Product Director, RNA Analytics

Insurance company's exposure to asset-liability mismatching risk has always been complex, but recent developments from across the regulatory, macroeconomic and investment landscapes have compounded the challenge, demanding specialised tools expressly designed for asset-liability matching in a dynamic environment

Asset-liability matching (ALM) strategies are undergoing significant transformation in the insurance segment. Ever-evolving regulatory frameworks, changing risk management priorities, and volatile economic conditions are making this area of asset liability management one of the key strategic differentiators for insurers globally.

Amongst the key forces profoundly influencing ALM strategies has been the shift toward market-based solvency regulations. Europe’s ever-evolving Solvency II framework, which compelled insurers to focus on market-consistent valuations and dynamic ALM strategies to optimize their ALM in real time, places particular emphasis on liquidity risk management, duration matching and capital efficiency. Solvency II, and the UK’s more recently published version, Solvency UK, necessitates ALM strategies that are at the same time robust and flexible – with the ability to adapt to varying economic conditions.

The impact on interest rates of changing economic conditions poses specific challenges for insurers in relation to ALM. The recent uptick in interest rates has reshaped insurers’ approaches to liquidity and cash flow matching. While the shift towards the latter was driven by the need for liquidity in the prolonged low-interest-rate environment that followed the 2008 Global Financial Crisis, recent rising rates have shifted the balance from pure liquidity concerns towards a more integrated approach that combines cash flow matching with managing the risk of duration mismatches and potential realised losses from liquidating lower-yielding assets.

Interest rates and their impact on liquidity have caught the attention of regulators around the world, taking a somewhat stricter approach in urging market participants to manage the risks through stress testing.

At the global level, IFRS 17 has brought about sweeping changes to the way insurers measure their liabilities and revenue, demanding greater transparency and alignment between ALM and financial reporting practices. Further, regulatory developments in emerging markets are beginning to mirror international best practices in ALM.

There has also been a general trend for modern ALM strategies to become more integrated with broader Enterprise Risk Management (ERM) frameworks, with insurers now incorporating a wider range of risks into their ALM strategies. Increasingly, environmental, social and governance (ESG) factors have also been integrated, steered in part by regulators – particularly in Europe, where the Task Force on Climate-related Financial Disclosures (TCFD) and the European Union’s Sustainable Finance Disclosure Regulation (SFDR) are key drivers.

In addressing the many variables in the ongoing ALM challenge, insurers whose modelling can permit fully dynamic ALM behaviours benefit from a new perspective not available from traditional approaches to ALM that rely on static, periodic assessments. A fully dynamic ALM allows for continuous monitoring and adjustment of strategies that respond in near real-time to regulatory and market volatility, and interest rate fluctuations, as well as policyholder behaviour and other variable factors.

Dynamic ALM software plays an increasingly vital role in helping insurers manage their assets and liabilities in a more flexible, responsive manner – a critical factor in ensuring financial health and stability – by providing real-time analytics, modelling and risk management capabilities that traditional static ALM frameworks simply cannot offer.

Dynamic ALM solutions can also be extended to incorporate real-time analytics and forecasting that is necessary for insurers to continuously rebalance their portfolios to ensure optimal asset-liability alignment. They also support the modelling of any number of economic scenarios to conduct the necessary stress testing of carriers’ portfolios to understand how assets and liabilities will behave under different conditions.

Armed with these new tools, insurers can also access a far more granular cash flow analysis – particularly critical for those carriers with a high degree of unpredictability in their book. Advanced ALM software is also of course equipped to handle increased regulatory requirements, automating the reporting process for greater accuracy, and more timely filing.

The shift towards deploying more sophisticated ALM strategies and solutions will continue to support market participants as they navigate a wide array of ongoing financial and non-financial risks moving forward. This will require more sophisticated modelling approaches, leveraging artificial intelligence, cloud computing to develop real-time and adaptive ALM models that use continuous data feeds and real-time analytics to adjust asset allocations dynamically in response to market conditions, interest rates, and other factors, ensuring that their ALM strategies remain aligned with risk tolerance and liability profiles.