Exploring ALM in Life Insurance: Strategy, Risks, and Innovations

In our recent interview, Hans Sterte, a seasoned economist, and Senior Partner at House of Reach, shares his rich insights from a career spanning over three decades in the realms of economics, asset management, and strategic investment. Tracing his journey from government institutions to leadership roles in major Swedish pension funds, Hans delves into the intricacies of Asset and Liability Management (ALM) within life insurance companies, highlighting its evolution and current challenges.

Thank you for agreeing to our interview. Can you briefly tell our readers about yourself? 

I am an economist by training, and began my professional career some 35 years ago at central government institutions like the Central Bank and the Ministry of Finance. During the last 20 years, I have been the Chief Investment Officer on large pension funds in Sweden. Besides economics and financial markets, my interests include geopolitics, history, and sports.

In your experience within asset management, particularly in life insurance companies, could you tell us more about how you viewed the primary objective of ALM?

Asset and Liability Management (ALM) is a practice used by financial institutions to find and mitigate financial risks resulting from a mismatch between assets and liabilities.

ALM strategies take a view of the total balance sheet and are thus a combination of risk management and strategic asset allocation. It also includes the modelling of liabilities as well as the regulatory framework and capital requirements.

To me, the primary objective of ALM is that it helps the life insurance institution in question to cover its liabilities in an effective way and with a high degree of confidence. By doing so it will create a larger surplus that you could manage to maximize the future expected return. More realistically than maximizing, it helps the institution produce a “robust” portfolio which could be expected to deliver good returns at various probable future scenarios.

Asset management has seen numerous tools and methodologies evolve over the years. In your perspective, which tools have been the most critical for effective ALM in a life insurance company?

It is hard to specify a specific “tool” that has been most critical to creating an effective ALM process in a life insurance company. It has more to do with the mindset. Instead of managing each risk separately based on the type of risk involved, ALM is a coordinated process that looks at an institution's entire balance sheet. Since there are stark differences between different life insurance companies it is hard to specify a particular tool as most critical for all organizations.

However, if I could pick one tool that is important to all ALM work in a life insurance/pension company it would be a good functioning economic scenario generator (ESG). Since ALM is a long-term strategy, including forward-looking projections and datasets, the ESG is the heart of the ALM process. 

On an aggregate level, what are the main risks in life insurance companies?

This could be an extensive list, depending on what level of aggregation you choose to answer the question. At the highest level of aggregation, I would like to point out three main risks. These are the mismatch in duration between assets and liabilities, the total market risk in the asset portfolio and the currency risk stemming from holdings both on the asset and liability side. 

Life insurance companies diversify their portfolios across various asset classes. From your experience, what are typical asset classes that are considered?

The typical asset classes are government bonds, credits, and public equities. Other common asset classes are real estate, private equity, infrastructure, and catastrophe bonds.

Could you elaborate on the specific risks and benefits associated with capital market risk? 

The risk and benefits of asset classes usually go hand in hand. Higher expected risk gives higher expected returns for an asset class. For instance, the expected risk for government bonds is lower than for equities. However, that extra risk in equities is compensated for with a higher expected return.

On a portfolio level, one would try to choose a mix of asset classes that maximize expected return given a certain desired risk level in the balance sheet. If you mix assets that have low correlation with each other, the total return becomes more stable over time. 

Are there any types of investments you feel have been overlooked by life insurance companies and might be more relevant in the future?

There are asset classes that are not dependent on financial markets. Due to that, these asset classes have a low correlation to the rest of the portfolio and could help you to increase the expected return for a given risk level. Examples of these types of asset classes are catastrophic bonds and mortality risk.

Every financial model or strategy has its set of constraints. In ALM for a life insurance company, what would you say are the most pressing constraints?

The most pressing constraints depend on the insurer in question. Since there is an immense difference between life insurance companies, no general framework could apply to all. The regulatory constraints must always be fulfilled, and the capital constraints must be met.

In the context of Solvency II, what would determine the choice of building an internal model compared to the standard models? Is your view that an internal model for market risks can be more precise and lead to lower capital requirements?

The choice of building an internal model or not depends on what you would gain in terms of lower capital requirements. If the benefits are low, you should use the standard model since it is much easier to implement. On the other hand, if the benefits are considerable, it is worthwhile building your own internal model. An internal model for market risk is more precise, but the extent of this precision varies significantly between different life insurers.  

Given the increasing focus on sustainability and ESG (Environmental, Social, and Governance) factors, how do you see these elements being integrated into ALM strategies, especially within the insurance industry?

The increased demand from the regulatory side, as well as from the customers, has made it necessary for life insurance companies to integrate ESG factors in their ALM process. Up until now, it has been a challenging task to do due to lack of data, but that is about to change. New regulations, like the EU Taxonomy, will increase the data available on environmental issues and thus enable it to be used in ALM modelling. In the future, I expect that much more data will be produced which can be used to better model all ESG factors in an ALM model.