Insurance Solvency Research Articles

A Bayesian Non-Linear Model for Forecasting Insurance Loss Payments

SummaryWe propose a Bayesian non-linear hierarchical model that addresses some of the major challenges that non-life insurance companies face when forecasting the outstanding claim amounts for which they will ultimately be liable. This approach is distinctive in several ways. First, data from individual companies are treated as repeated measurements of various cohorts of claims, thus respecting the correlation between successive observations. Second, non-linear growth curves are used to model the loss development process in a way that is intuitively appealing and facilitates prediction and extrapolation beyond the range of the available data. Third, a hierarchical structure is employed to reflect the natural variation of major parameters between the claim cohorts, accounting for their heterogeneity. This approach enables us to carry out inference at the level of industry, company and/or accident year, based on the full posterior distribution of all quantities of interest. In addition, prior experience and expert opinion can be incorporated in the analyses through judgementally selected prior probability distributions. The ability of the Bayesian framework to carry out simultaneous inference based on the joint posterior is of great importance for insurance solvency monitoring and industry decision making.

Risk Management and the Global Banking Crisis: Lessons for Insurance Solvency Regulation

This paper investigates the causes of the banking crisis and the resulting lessons that need to be learned for insurance regulation. The paper argues that the banking crisis was predominantly caused by weaknesses in the management and regulation of banks, weaknesses that lead to problems such as flawed compensation schemes, poor risk management communication and an over-reliance on mathematical risk models. On the basis of these findings, doubts are expressed about the direction of certain insurance regulatory reforms—such as the focus on capital requirements and quantitative risk assessment (the so-called “Pillar I” of most reforms). It is also recommended that a more balanced approach to insurance regulation should be implemented, which places much greater emphasis on enhancing risk management guidance and supervisory tools (Pillar II) and improving disclosure rules (Pillar III).

Stochastic Mortality, Macroeconomic Risks and Life Insurer Solvency

Motivated by a recent demographic study establishing a link between macroeconomic fluctuations and the mortality index k t in the Lee–Carter model, we develop a dynamic asset-liability model to assess the impact of macroeconomic fluctuations on the solvency of a life insurance company. Liabilities in this stochastic simulation framework are driven by a GDP-linked variant of the Lee–Carter mortality model. Furthermore, interest rates and stock prices react to changes in GDP, which itself is modelled as a stochastic process. Our simulation results show that insolvency probabilities are significantly higher when the reaction of mortality rates to changes in GDP is incorporated.

Derivative Hedging and Insurer Solvency: Evidence from Taiwan

Using company-level panel data (2001–2003), this paper empirically examines whether Taiwanese insurers' use of derivatives for hedging purposes is significantly related to their solvency (as measured by solvency ratio). Contrary to the public's perception that firms with derivative programmes have a higher level of solvency if derivatives are employed for hedging purposes, our results indicate that life insurers' derivative hedging generally is not associated with solvency, while non-life insurers using derivative hedging have a lower level of solvency.

Detecting fuzzy relationships in regression models: The case of insurer solvency surveillance in Germany

We develop a test for the fuzziness of regression coefficients based on the Tanaka et al. (1982) and He et al. (2007) possibilistic fuzzy regression models. We interpret the spread of the regression coefficients as a statistic measuring the fuzziness of the relationship between the corresponding independent variable and the dependent variable. We derive test distributions based on the null hypothesis that such spreads could have been obtained by estimating a possibilistic regression with data generated by a classical regression model with random errors. As an example, we show how our test detects a fuzzy regression coefficient in a solvency prediction model for German property–liability insurance companies.

Stochastic Mortality, Macroeconomic Risks, and Life Insurer Solvency

Stochastic Mortality, Macroeconomic Risks, and Life Insurer Solvency

Minimum standards for investment performance: A new perspective on non-life insurer solvency

The aim of this paper is to develop an alternative approach for assessing an insurer’s solvency as a proposal for a standard model for Solvency II. Instead of deriving minimum capital requirements–as is done in solvency regulation–our model provides company-specific minimum standards for risk and return of investment performance, given the distribution structure of liabilities and a predefined safety level. The idea behind this approach is that in a situation of weak solvency, an insurer’s asset allocation can be adjusted much more easily in the short term than can, for example, claims cost distributions, operating expenses, or equity capital. Hence, instead of using separate models for capital regulation and solvency regulation–as is typically done in most insurance markets–our single model will reduce the complexity and costs for insurers as well as for regulators. In this paper, we first develop the model framework and second test its applicability using data from a German non-life insurer.

Derivative Hedging and Insurer Solvency: Evidence from Taiwan

Derivative Hedging and Insurer Solvency: Evidence from Taiwan

Stochastic Mortality, Macroeconomic Risks, and Life Insurer Solvency

Motivated by a recent demographic study establishing a link between macroeconomic fluctuations and the mortality index kt in the Lee-Carter model, we assess the impact of macroeconomic fluctuations on the solvency of a life insurance company. Liabilities in our stochastic simulation framework are driven by a GDP-linked variant of the Lee-Carter mortality model. Furthermore, interest rates and stock prices are allowed to react to changes in GDP, which itself is modeled as a stochastic process. Our results show that insolvency probabilities are significantly higher when the reaction of mortality rates to changes in GDP is incorporated.

Can Independent Underwriters Benefit Insurers in High-Risk Lines? A Cournot Market-Game Analysis

One of the greatest dangers to the solvency of property-liability insurers is writing large amounts of new business in a high-risk line (i.e., a line of insurance in which a substantial portion of buyers consists of high-risk insureds). This practice is problematic because of both potentially inadequate pricing and potentially lax underwriting. A prominent example of the latter phenomenon was the collapse of many captive insurers in the early to mid-1980s, in which the insurers relied too heavily on independent underwriters motivated solely by increasing premium volume. In this article, we employ a Cournot market-game model to study the financial impact of informed independent underwriters (i.e., unaffiliated underwriters with private information regarding the risk characteristics of insureds) on insurers in high-risk property-liability lines. In a market with a risk-neutral insurer and CARA insureds, we find that the insurer will always do worse by using a risk-neutral underwriter than by operating on a direct-writing basis. However, for an insurer employing mean-variance optimization, the proper combination of underwriter-compensation and capital allocation may lead to better outcomes than direct writing.

Regulatory Competition and Life Insurance Solvency Regulation in the European Union and United States

The economic reasons for life insurance regulation have not been well developed in the finance literature. In this paper we discuss some justifications that have been advanced for regulation and argue that they are not persuasive. The most rigorous arguments in favor of the regulation of life insurance companies are as follows. First, regulation can prevent the adverse affects of information asymmetries in markets for illiquid contracts. Second, regulation can be used to ensure that insurers commit to contracts. In the case of life insurers these contracts may be incomplete, and it may be difficult to determine the terms of the contracts objectively; this is particularly so with U.K. with-profit contracts, for example. These justifications for regulation, combined with a public choice analysis of regulation, lead us to conclude that regulation should be voluntary and provided by competing private and government agencies. Finally we propose a method of moving toward such a regulatory framework starting from the current regulatory institutions in the United States and the European Union (EU). An approach based on the “mutual recognition” concept used, at least in theory, in the EU would provide an approximation to the regulatory approach we believe can be justified by economic principles.

International Solvency Requirements – Towards more Risk-based Regimes

Insurance regulation and supervision is aimed at maintaining efficient, fair, safe and stable insurance markets for the benefit and protection of policyholders. So ensuring an insurer remains solvent and thus able to fulfil its obligations under all insurance contracts under all reasonably foreseeable circumstances is crucial. Insurer solvency therefore takes a central position in not only risk management by insurers, but also in effective insurance supervision, which must include a solvency regime which lays down solvency requirements.

Solvency of Stock Versus Mutual Insurers: Evidence from Dutch Panel Data

We investigate the capital structure of 350 Dutch insurers during the period 1995-2005. Our main findings are: (1) a small company size, a mutual organisation, high profitability, large equity investments, or being a fire insurer, all contribute to higher solvency margins; (2) minimum solvency requirements from the supervisor are less easy to explain by firm characteristics and do not correlate positively with risk; (3) neither do insurers follow solvency requirements closely; (4) most insurers have surplus capital which, together with a large company size and high profitability, reduces the risk of insolvency.

Return smoothing mechanisms in life and pension insurance: Path-dependent contingent claims

Traditional with-profits pension saving schemes have been criticized for their opacity, plagued by embedded options and guarantees, and have recently created enormous problems for the solvency of the life insurance and pension industry. This has fueled creativity in the industry’s product development departments, and this paper analyzes a representative member of a family of new pension schemes that have been introduced in the new millennium to alleviate these problems. The complete transparency of the new scheme’s smoothing mechanism means that it can be analyzed using contingent claims pricing theory. We explore the properties of this pension scheme in detail and find that in terms of market value, smoothing is an illusion, but also that the return smoothing mechanism implies a dynamic asset allocation strategy which corresponds with traditional pension saving advice and the recommendations of state-of-the-art dynamic portfolio choice models.

Insurance Industry in India: Structure, Performance, and Future Challenges

With the liberalization and entry of private companies in insurance, the Indian insurance sector has started showing signs of significant change. Within a short span of time, private insurance has acquired 13 per cent of the life insurance market and 14 per cent of non-life market. However, there is still a huge untapped demand for insurance. Insurance companies have a pivotal role in offering insurance products which meet the requirements of the people and, at the same time, are affordable. Some of the challenges faced by the insurance sector pertain to the demand conditions, competition in the sector, product innovations, delivery and distribution systems, use of technology, and regulation. To understand the growth and development and the future prospects of this sector, this colloquium addresses the following issues: What will be the demand for insurance? What types of innovative strategies of insurance education and awareness will we require to encourage the Indian consumers? With the changes following bank participation in insurance, will the nature of competition in this sector intensify? What kind of competitive and risk pressures will the insurance businesses experience? What are their implications for profitability, margins, and efficiency? The average size of the polices will continuously decline as the insurance companies increase the geographic coverage. As a result of this, the intermediation costs will go up. What are the implications of these on average costs? What will be the product market scenario? Has the insurance sector benefited from the knowledge base of global companies? To what extent have the technology gains in telecommunications, computer information, and data processing contributed to increased efficiency and productivity of insurance companies? The following key points emerged from the responses of the panelists: The future in life insurance will be determined by the increase in pure protection products, a refreshing look at unit-linked plans, launch of customized plans, and improved service levels. The insurance sector will grow steadily rather than rapidly. While the law and regulations are in place to ensure financial strength and solvency of insurers, the regulator's challenge lies in monitoring compliance. The opportunity for financial services is increasing all over the world. Big domestic companies with significant market shares in the local countries will have the opportunities to commence business in other markets. Keeping in mind the complexities of the industry, multi-product, multi-channel, and multisegment route needs to be followed for growth. The challenge of successfully implementing bancassurance lies in training the staff, integrating the insurance products, and ensuring best quality service. Agents in the insurance sector are critical for its success and, in order to gain competitive advantage, quality people are needed but attracting and retaining agents is a challenge.

Return Smoothing Mechanisms in Life and Pension Insurance: Path-Dependent Contingent Claims

Traditional with-profits pension saving schemes have been criticized for their opacity, plagued by embedded options and guarantees, and have recently created enormous problems for the solvency of the life insurance and pension industry. This has fueled creativity in the industry's product development departments, and this paper analyzes a representative member of a family of new pension schemes that have been introduced in the new millennium to alleviate these problems. The complete transparency of the new scheme's smoothing mechanism means that it can be analyzed using contingent claims pricing theory. We explore the properties of this pension scheme in detail and find that in terms of market value, smoothing is an illusion, but also that the return smoothing mechanism implies a dynamic asset allocation strategy which corresponds with traditional pension saving advice and the recommendations of state-of-the-art dynamic portfolio choice models.

Insolvency Risk in the Italian Non-life Insurance Companies. An Empirical Analysis Based on a Cash Flow Simulation Model

Policyholders do not accurately know the financial condition of insurers because of asymmetric information that makes prices unable in discriminating risk behaviour of insurance companies and provides rationale for prudential regulation. Regulators often imposed capital requirements to ensure solvency having some drawbacks in preventing the assumption of excessive risks. Hence, other methods should be taken into account in order to estimate their ability in identifying troubled insurers. This study analyses insolvency risk in the Italian non-life insurance market by adopting a deterministic cash flow simulation model in order to predict insolvencies over the period from 1990 through 1994. The model is based on future projections of principal cash inflows and outflows of insurance companies. Several hypotheses about the evolution both of insurance and economic variables such as premiums, losses for claims, stock prices, interest rates and inflation rates are implemented in the model. The results show a strong ability of the model in identifying insolvent insurers during the period examined here and suggest that cash flow simulation models may provide regulators and managers with useful information on insurance solvency.

Exposure Triggers and Allocation Methods: Learning Lessons From Prior Court Rulings

AbstractThis study provides a review of some of the major court rulings that have shaped and continue to shape the commercial general liability (CGL) market. The evolution of the concepts of “triggers” and allocations systems is examined to gain a perspective on the way in which courts reinterpret contract language to apply to new and emerging exposures. A review of the issues impacting the CGL provides valuable insights into the way court rulings can create a significant impact in the insurance market. A stream of court decisions provides the backdrop for today's challenges, including the reemergence of asbestos claims. The study also fills a gap in the literature related to the crisis in the CGL marketplace and changes in the pricing, regulation, and solvency of insurers operating in those lines. As old risks continue to evolve and new risks emerge, courts have begun to reinterpret liability contracts in much the same way as they reinterpreted contracts with regard to pollution and products in the 1970s and 1980s. Recent rulings related to asbestos and environmental liability underscore the importance of these issues in today's marketplace. By reviewing these events related to the CGL policy, insurers, insureds, and regulators may gain a new perspective on the importance of developing a clear standard wording that will be consistently interpreted in light of new exposures.

Institutions, Bureaucratic Decisions, and Policy Outcomes: State Insurance Solvency Regulation

Although over the past 2 decades several scholars have documented a link between institutional arrangements and policy choices, few studies have demonstrated how different institutional arrangements give rise to different policy outcomes. Further, although some studies have related bureaucratic resources to policy decisions, almost none have illustrated that the way state agencies actually deploy regulatory resources—policy outputs—significantly influences policy outcomes. Focusing specifically on the insurance industry in the United States, in this article we illustrate the impact of state regulatory activities on the solvency of firms in the insurance industry from 1987 to 1997, and in doing so we emphasize the differences between policy outputs and policy outcomes. In the case of policy outputs, for example, we find that elected insurance commissioners, divided state government, budgetary resources, and larger insurance sectors lead to more aggressive solvency examinations of insurers. In addition to examinations, however, we discern an important role for both regulatory and political institutions in subsequent insurer insolvencies.

The effect of ceded reinsurance on solvency of primary insurers

Primary insurance companies diversify their underwriting risk and thus improve their financial stability through buying reinsurance contracts. However, excessive use of reinsurance by an insurance company may signal the presence of financial difficulties. In fact, as research shows, a less solvent insurer tends to use more reinsurance because of its inability to raise needed capital in the financial market. Thus, regulators need to pay extra attention to insurers that overly use reinsurance since such behavior could signal an insurer's disproportionately high risk and its eventual probability of insolvency.