Optimizing Insurance ALM and Investments: A Market Survey
Optimizing Insurance ALM and Investments: A Market Survey
by Jessica Burke
Benchmarking is a great way to measure ALM success.
As the insurance industry evolves and matures, greater focus is being placed on the management of insurance assets. This shift is largely driven by increased industry competition, which has increased product optionality even as premium prices have been driven down. The former consequence has increased the complexity of liability cash–flow projections, and lower policy premiums have shrunk the size of the pool of assets that previously provided a margin of safety. In addition to these indirect factors, at many insurers the investment department has felt direct pressure to generate additional profits. This has resulted in an industry migration toward riskier investment strategies with spreads that are often passed along to policyholders.
This increased risk profile has provided investment departments, traditionally siloed divisions within insurance companies, with increased corporate attention. Those departments not fully integrating liability cash flow models into their asset management will inevitably make investments that significantly increase corporate risk.
To better understand the state of the industry, Patpatia & Associates undertook a survey of insurance general account investing, speaking with over 50 firms. These insurers varied by size, type of insurance and business line.
The survey attempted to address numerous issues. Overall, we sought to more deeply understand the best practices of insurance asset management, which in turn drove our two sub–focuses: asset–liability integration and benchmarking.
Asset–Liability modeling is the cornerstone of any successful investment department. Yet even the most accurate of liability cash–flow models is futile if not well integrated into the investment strategy used to manage and allocate assets. In design, asset–liability management (ALM) departments are supposed to bridge the disjointed relationship between product management and the investment department. However, in most organizations, ALM departments often seem to focus efforts on reporting to regulatory entities and rating agencies rather than managing the liability and asset exposures for product and investment management.
Equally critical, in our opinion, is the role of benchmarks within the investment process. Because internal benchmarks are the yardstick by which investment managers are measured, by default if not by design, they become the guide with which portfolio managers make investment decisions. If current and future liability considerations are not incorporated into these benchmarks, a mismatch between available assets and required liability payments is nearly inevitable. Within a system of integrated investment management, the investment benchmark acts as the tangible incarnation of the asset–liability strategy. It provides a common language for product management and the investment department to communicate their respective needs in the process.
At the forefront of insurance investment management were 16 of the 53 firms surveyed by Patpatia & Associates. These firms have already developed investment management techniques that incorporate liability metrics into their investment policy and have created meaningful benchmarks. Profiling these firms, six broad components were identified as core to a successful investment policy. It is the implementation of these six processes that are at the core of what we have loosely termed "liability–driven benchmarking."
This process begins with its most crucial element, modeling of asset and liability cash flows. Individual insurance contracts are modeled for their anticipated cash–flow requirements based on product features (surrenders, lapses, crediting rates) and policy holder characteristics (demographic assumptions, policy behavior, mortality and morbidity tables).
Of critical importance are the assumptions used for asset allocation. Best practice firms coordinate so that the assumptions used are comparable to those for product pricing and valuation. Where they differ, there should be logical and transparent reasons why.
Rather than formally developing cash flow profiles driven by the liability product mix, many insurers today continue to develop investment targets (frequently duration or average life alone) based on the intuitive experience of the investment department. However, over half of all survey respondents indicated that duration is an insufficient ALM metric and that quarter–by–quarter cash flow projections are also needed to develop an effective investment strategy.
Of the insurers actively integrating liabilities into their investment strategy, most possessed 100–150 discrete liability portfolios. These are aggregated at the product group and business line level rather than on an individual portfolio basis based on three criteria: size, legal jurisdictions and liability characteristics. This allows the law of large numbers to work, for example, across credit risk. A small portfolio might have an inordinate number of defaults, or none at all. A large portfolio is more likely to reflect current market conditions.
Across the full breadth of insurers surveyed, the average number of unique liability models was 20–25. The number of liability models differed significantly based on the specificity of liability modeling–individual portfolio, product group or business line levels–as well as the business mix and size of the insurer. Among life respondents, liability models were generated for whole life, universal life and multiple term life criteria. For annuity products, many insurers maintained unique groups due to varying surrender periods, crediting rates and other optionalities. Commercial business lines maintained discrete product groups for each product line (e.g., fire, earthquake, worker's compensation, general). Personal business was grouped into auto, as well as several types of property insurance (e.g., homeowners, renters).
The frequency of liability modeling depended primarily on the product features of the particular liabilities. Among the most accurate insurers, models for interest sensitive liabilities were reviewed and updated on a quarterly basis; however, insurers agreed that irrespective of modeling frequency, quarterly cash flow projections were necessary.
For non–interest sensitive portfolios, insurers generally agreed that yearly extracts were sufficient due to the static nature of the liability characteristics.
After liability cash flows have been modeled, insurers adopting liability–driven benchmarking translate cash flows into an optimized maturity term structure that maximizes free cash flow (step B, Table 4). Assumptions are then stress tested to model potential downside risks and set liquidity requirements (step C, Table 4). Based upon these inputs, strategic and tactical asset allocations may be developed (step D, Table 4).
Differences in these steps within Liability–Driven–Benchmarking processes are apparent across insurance lines. Life insurers have focused their efforts on liability modeling, asset maturity profiling and asset allocation (steps A, B and D, Table 4) when developing investment policies due to predictability of liability portfolios. P&C insurers hold a bias toward liquidity modeling (step C, Table 4) driving the asset allocation process, as their respective liability portfolios generally lacked the predictability required to set complicated maturity term structures.
For all insurers, the final step in this process is formalizing the strategy into explicit benchmarks (step E, Table 4) against which performance can be measured and results attributed (step F, Table 4). While insurers varied in their approaches to steps A–D in Table 4 they differed even more widely in their methods–if any–for evaluating investment performance and the accuracy of modeling assumptions.
Several of the smallest firms, and surprisingly a few mid–tier players with as much as $15 Billion in assets under management (AUM), lacked any formal benchmarking program. Equally surprising, mutual insurers, with a few notable exceptions, lag the marketplace in benchmarking standards due to their reduced need to focus on stable quarterly earnings. Only a short list of the largest insurers incorporated the necessary elements to make their benchmarks into true representations of the asset–liability management strategy.
The wide spectrum of benchmarks used reflects significant inconsistencies in the functional role of investment benchmarking.
To maximize the utility of investment benchmarks, and to ensure that their role as a guidepost is acknowledged (if not formalized), benchmarks should incent portfolio managers and the broader investment department to do three things: 1. direct portfolio investments for spread and return enhancement; 2. manage asset–liability risks to mitigate volatility of earnings; 3. encourage the maximization of income and total returns.
Investing the time and effort to leverage financial models that already exist within an insurance company is required to be a best practices firm. By integrating the asset and liability cash flows to optimize the overall result, transparent attribution is possible, and firm value is increased.
Jessica Burke is senior business analyst for Patpatia & Associates, a strategic consulting firm currently working with a number of insurance companies to enhance their investment management practices. For a copy of the full survey please contact Jessica Burke at 510.559.7140 or firstname.lastname@example.org.