Asset and Liability Management Modernization for Insurance: PwC

Richard de Haan

Principal and Global Risk Modeling Services Leader, PwC US

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Quintin Li

Partner, Risk Modeling Services, PwC US

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Poojan Shah

Director, Risk Modeling Services, PwC US

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Gregory S. Johnson

Director, Risk Modeling Services, PwC US

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The recent crisis in the banking sector has thrust the importance of asset and liability management (ALM) back into the spotlight. A key driver of the disruption in the banking sector was the market value losses on long-dated assets that backed liquid short-term liabilities.

Life insurers are well acquainted with the challenge of managing a fixed asset portfolio against liabilities driven by unpredictable customer behavior and parametric risk outcomes. As such, ALM is a core competency of life and annuity insurers. Most insurers have honed their ALM approaches by applying a wide range of strategies to manage their asset duration and convexity profiles, but they are certainly not immune to disintermediation liquidity risks and asset or liability mismatch risk.

While many insurers have a well-defined ALM mandate, we believe that a more holistic supporting process is needed to execute and monitor their strategy and protect against losses. The modern landscape demands management have access to dynamic, real-time asset and liability data to make informed risk management decisions in an effective manner. However, companies are often constrained by legacy processes that are resource and time-intensive.

Practical limitations on ALM

Operational constraints hamper the traditional ALM model. Asset and liability systems rely on disparate data sources, competing and often imprecise liability projection systems, and a large volume of time-consuming, manual back-end processes. Other limiting factors include:

Liability projections that require costly valuation runs with long run-times to generate projected cash flows
Asset systems that require significant simplifications and workarounds to handle increasingly complex asset classes
Differences in timing and methodology between asset and liability models, resulting in unreliable results

As a result, the ALM function too often resembles an exercise in bridging the gap between two disconnected sides of the balance sheet — as opposed to a holistic, integrated approach.

A shifting economic environment

Rapidly shifting economic environments amplify the importance of sound ALM practice. However, it is in these circumstances that the practical limitations are most pronounced.

Daily movements in interest rates, credit spreads, inflation and volatility can rapidly shift a company’s risk profile, and the current ALM models generally struggle to keep pace. Thorough ALM analysis requires days to prepare, while active monitoring solutions often require significant simplifying assumptions that reduce the precision of results.

Under the current model, companies are required to act on outdated information when making significant decisions that impact its long-term risk profile.

ALM Modernization with Advanced Finance Analytics

To address these limitations, modern technology solutions paired with the required domain experience can be leveraged to help implement a modernized ALM solution that enhances the overall capabilities of the tools used by decision-makers.

Overall, modernization of the ALM process should include several key tenets:

Successful ALM requires an integrated organizational structure. Finance, actuarial, investments, risk and overall leadership contribute unique perspectives to a successful ALM function.

The process constraints discussed in this paper limit the degree to which this diverse group of stakeholders can communicate effectively. Shared tools used across the organization can help bridge the gap. A real-time ALM monitoring solution can serve as a common ground through which key stakeholders can develop consensus.

Life and annuity portfolios include an increasingly diverse mix of assets, including structured products and private credit. This coincides with increased regulatory requirements such as NAIC Actuarial Guideline LIII (“AG53”) for companies that hold these assets. Companies should invest in their asset modeling capabilities to support their ALM function and fulfill increased regulatory requirements. Asset models should be enhanced to encompass the idiosyncratic risks associated with these more complex asset classes.

Disparate data sources within an organization can make it extremely challenging to form a complete picture of the interaction between assets and liabilities. As such, a dynamic and powerful ALM solution should be able to seamlessly transform and integrate data from various sources across an organization, and as a result, foster increased communication and collaboration between functional units.

Cloud computing is more accessible and cheaper than ever. It provides a significant opportunity to scale up and enhance ALM analytics, reduce run time and lower operational costs.

Holistic, digestible ALM monitoring should be at the fingertips of key stakeholders within the integrated ALM structure.

To demonstrate how to deliver these key capabilities and what is possible with modern technology, PwC has developed an Advanced Finance Analytics solution. The solution illustrates how to achieve a flexible, dynamic interface that can be easily customized to in-take market and organizational data in its current structure and formats, and produce holistic, digestible — as well as timelier — ALM metrics.

Takeaway

The end goal of modernized real-time ALM monitoring is to provide better decision-making information faster. Agility and responsiveness to key ALM issues are crucial to managing a life and annuity book. Companies that make the necessary investment in process and technology enhancements now can be well-equipped to better manage their risk going forward.