Enterprise Risk Management for Property/Casualty Insurance Companies
Shaun Wang, CEO/Executive Director, ERM Institute International, Ltd.
T he Casualty Actuarial Society, the ERM Institute International, Ltd. and the CAS/SOA Risk Management Section have released their jointly commissioned research report titled “Enterprise Risk Management for Property-Casualty Insurance Companies.” I coauthored the report with Robert Faber (executive, underwriter), and several highly regarded CAS members contributed valuable comments. The research report proposes a new conceptual framework for enterprise risk management (ERM) and applies it to property/casualty insurance companies.
The report defines ERM as the discipline of studying the risk dynamics of the enterprise, the interactions of internal/external players and forces, and how players’ actions (including the risk management practices) influence the behaviors of the risk dynamics, with the ultimate goal of improving the performance and resiliency of the system. This definition takes an engineering-like approach and paves the way for a “scientific” approach. The authors believe that risk dynamics modeling holds great promises when combined with a true understanding of the dominant risk drivers.
The report advocates that an actionable ERM should be embedded in each step of the company’s decision-making processes. ERM should start with an analysis of the business model and the company’s strategic position in relation to the external environment, followed by an examination of the company’s internal operational processes and how they have affected the company’s financial performance.
An enterprise risk model for a property/casualty insurer must give due consideration to (at least) the following dominant risk dynamics:
Inherent risks associated with the product design, risk origination, risk selection, and risk valuation as embedded in the marketing, underwriting, pricing, claims handling, and reserving processes;
Constraints imposed by rating agencies and regulators;
Actions and behaviors of competitors (market leaders and participants);
Exposures to catastrophic or correlated losses (on both asset and liability sides of the balance sheet); and
Impacts of market valuation fluctuations and accounting conventions on company balance sheets and earnings.
The report highlights a basic truth that risk dynamics cannot be known completely due to the multiple forces at work, but knowledge about the risk dynamics can be gained through experience, insights, and modeling. One should try to objectively evaluate the knowledge level of the risk dynamics and the competitive edge relative to competitors. A common pitfall is that when one has little knowledge (or less than a competitor’s knowledge) about the risk dynamics of a line of business, for example, or fails to identify the underlying trends, one tends to perceive the risk dynamics as “pure volatility,” and put his or her faith in diversification. Although diversifying a portfolio of risks is usually beneficial, such diversification has to be weighed against the increased risk due to the reduced knowledge one has for each risk. Lack of knowledge of the underlying risks often shows up in the form of inadequate reserves, which is a lagging indicator of poor enterprise performance.
The report documented empirical findings that, for commercial lines (including workers compensation and general liability), large national insurers tend to show worse underwriting results than the small regional companies. For general liability and workers compensation, the inherent loss reporting delay provides a backdrop for the varying company behaviors in underwriting, pricing, and reserving practices. Differences in underwriting/pricing behaviors (e.g., average number of years of experience on the book, underwriter turnover, extent of reliance on experience rating modification, etc.) in small companies versus large companies provide explanations for the differing underwriting results.
The report recognizes that an enterprise has multiple risk dynamics at multiple levels (e.g., company, business segment, and product levels) with multiple forces (e.g., financial rating concerns at company level, competition at local business segment level, and contract terms at product level). To gain an overall picture we need to understand the interactions of risk dynamics at different levels and to reconcile the multiple perspectives. While traditional actuarial analysis focuses more on the individual risk level, ERM advocates a high-level analysis that incorporates the macro risk drivers such as market competition, natural catastrophes, the cost fluctuation of hedging (through reinsurance), and regulatory constraints on profitability.
The value proposition of ERM is self-evident in the premise that actions taken by key participants (for example, insurance company executives, underwriters, actuaries, rating agencies, and regulators) can exert great influence on the behaviors of risk dynamics. Indeed, underwriting and pricing of the current book is a critical first line of defense in risk management, and is the first area that the insurer should consider in altering its future objectives and risk profile.
Properly constructed risk metrics and valuation models can shed light on the behavior of risk dynamics; they are powerful forces and essential tools for taking a structured and disciplined approach that aligns business strategies with the processes, people, technology, and knowledge within the organization. In the meantime, risk modeling itself introduces an inherent risk, namely the model risk, which is not random by nature. The report analyzes the drivers of pricing and reserving cycles and develops risk valuation models for loss ratio volatility, reserve development volatility, and risk capital requirements.
The research report advocates the use of “leading indicators,” rather than “trailing indicators,” in guiding business decisions. Actuaries have been predominantly relying on experience-based trailing indicators that are subject to estimation bias due to information lag and incentive problems such as tying bonuses with top-line premium growth. What the insurance industry needs are leading indicators that can be developed by closely monitoring rating level changes per unit of exposure, emerging trends, potential impacts of new regulation or new technology, actions by key competitors, and changes in competition due to the entry or exit of other insurers.
In the past, the property/casualty insurance industry has focused much time and energy on the prediction of the loss component of the loss ratio. The problem with so much emphasis on this component is that it is a trailing indicator. Only after several years can one effectively draw conclusions on the longer tail lines. Going forward, we must focus more attention on the denominator in the loss ratio calculation, namely the effect of rate levels on exposure. Rate levels, which are generally known at the inception date of the policy, can be considered leading indicators that are more timely and effective in predicting loss ratios, and therefore pricing cycles.
ERM is a journey and an ongoing learning process that requires a humble attitude and a disciplined approach. Implementation of an ERM framework should enable a property/casualty insurer to accomplish the following:
A clearly-defined business model that includes focusing the business, enhancing the competitive edge, and establishing a risk tolerance level;
A well-articulated risk appetite and risk strategy, risk exposure accumulation;
A well-integrated business process for sales, marketing, underwriting, pricing, claims handling, reserving, and investment functions and processes; and
A developed and tested robust risk valuation and risk model that are operational for day-to-day business management.
Although the specific contexts are pertinent to property/casualty insurers, the risk dynamics concept and the risk valuation methodology presented in this paper are universal and applicable to other industry sectors such as life and health insurers.
AR readers can address their comments to Dr. Shaun Wang at firstname.lastname@example.org.