Search results

To examine whether the properties of the combined‐ratio series, an indicator of underwriting profitability in property‐liability insurance, have changed over time.

Using the autocorrelation function (ACF) and partial autocorrelation function (PACF), we check whether combined ratios are stationary.

Underwriting profit has worsened in recent years, and combined ratios are non‐stationary. This characteristic of combined ratios needs further analysis for its impact on underwriting cycles.

Traditional concepts of underwriting cycles, such as predictable cycle lengths and trends, may have changed.

The possibility of a non‐stationary combined‐ratio series is recognized, and the possible existence of non‐stationarity and breaks in combined ratios is introduced.

The purpose of this paper is to investigate first, the consumer buying behaviour and claims pattern of medical and health insurance (MHI)/medical and health takaful (MHT) policies and second, to determine whether moral hazard exists among policyholders at the time of application for the product and during claiming for compensation.

The study was conducted on respondents from the insurance industry in Malaysia.

It was found that most claims were rejected due to the discovery of some irregularities by the managed care organizations (MCO) while the Islamic insurer's claims experience, was otherwise. During the buying behaviour stage of MHT, there are fewer tendencies to withhold information but during the claiming stage, due to the generous level of compensation and their awareness of the coverage available naturally influence them to submit excessive claims. To a certain extent moral hazard is present when claims are made for longer disability durations than necessary, and having high average claims per person even for shorter duration disabilities.

The paper concentrates only on the MHI/MHT in Malaysia.

The results provide insights on how the Malaysian insurance industry and other organizations of a similar structure could improve on their business performance.

This paper is perhaps one of the first to address adverse selection and its consequences on MHI/MHT in Malaysia.

To examine the existence of underwriting cycles for the property‐liability insurance industry as a whole, and by line of business. Specifically to consider whether the combined ratio is stationary and stable.

The augmented Dickey‐Fuller (ADF) test is employed for unit roots, while dummy variable methods, the Chow test, and switching regression are used for stability.

Underwriting profits of most lines of business and all lines combined are not stationary and have structural changes. For the whole property‐liability industry, a structural change occurred in 1981. Before the change, underwriting cycles existed since combined ratios followed an AR(2) process. After the change, combined ratios are non‐stationary.

Without clear underwriting cycles, there is more difficulty for the insurance industry in pricing and reserving, for regulators in monitoring the financial strength of insurers, and for customers in terms of the affordability and availability of insurance.

The paper recognizes the non‐stationarity of combined‐ratio series, years of structural changes in the insurance industry and specific lines of business, and the possibility that underwriting profit is cointegrated with investment income.

The purpose of this paper is to examine the relationship between intellectual capital (IC) and profitability of insurance companies in Ghana.

Data on 36 life and non-life insurance companies from 2007 to 2011 are employed to estimate the value added intellectual coefficient of Pulic (2004, 2008). Using return on assets and underwriting profit as indicators of profitability, the ordinary least squares panel corrected standard errors of Beck and Katz (2005) is used in estimating the relationship in the presence of serial correlation and heteroskedasticity. Leverage, underwriting risk and insurers’ size are used as control variables.

Non-life insurers have high IC performance comparative to life insurers. This study finds a significant positive relationship between IC and profitability of insurers in Ghana while human capital efficiency is the main driver of insurers’ IC performance.

The study discusses relevance of IC for management of insurance companies in Ghana and other emerging insurance markets in Africa.

This appears to be the first study to examine the impact of IC on profitability of a developing insurance market in Africa.

The purpose of this paper is to examine the risk management practices of life assurance firms and non‐life insurance firms.

Through a comparative case study methodology, the study assesses the state of risk management in both life assurance companies and non‐life insurance firms to determine whether they exhibit different or similar risk management practices. The results of the survey were also analyzed and compared to the principles of good practices in financial risk management.

The findings of the study revealed some differences and similarities in the risk management practices of life and non‐life insurance firms. Almost all the life companies have stated their risk appetite levels, which enable them to identify which risks to absorb and which ones to transfer. But non‐life insurance firms have not laid down their risk tolerance levels explicitly. The results further revealed that the industry lacks sufficient personnel with the requisite risk management skills and that the sector does not manage risks proactively, rather they do so in a reactive response to regulatory directives.

Effective management of risks by insurers will increase the penetration of insurance in Ghana.

Risk management is a crucial issue, not only for the survival and profitability of the insurance industry, but also for the socio‐economic growth and development of the whole economy. As major risks underwriters, insurance companies need to adopt good practices or quality measures in the management of financial risk. This is important, more so, as the industry prepares to re‐position itself to underwrite the risks in the emerging oil and gas industry of Ghana.

Research into financial risk management in the insurance industry from the Ghanaian perspective is rare. This study is therefore timely and its findings are invaluable for the efficient management of financial risk in the insurance industry.

This study sets out to focus on understanding the linkage between product variety and performance of property/casualty insurers during underwriting cycles.

The study sample is based on 4,597 property/casualty (PC) insurers from the time period 2000‐2004. Insurers are categorized into two groups based on performance: insurers whose performance is above 0.1 standard deviation of the mean, and insurers whose performance is below 0.1 standard deviation of the mean. Later logistic regression models are used to determine the influence of product variety on the likelihood of an insurer falling into one of these groups.

Study findings indicate that insurers with greater product variety had lower performance than focused insurers during both phases of the cycle. Insurers with great product variety fared relatively better than focused insurers during the hard markets compared with soft markets.

Extant research has presented significant evidence for the existence of underwriting cycles and factors causing such cycles. However, very little is known about factors that influence performance differentials across property/casualty insurers during underwriting cycles. Using real options theory, the study initially develops a rationale explaining how product variety reduces performance during an underwriting cycle and tests for the same. Study findings show that product variety aids performance of insurance firms in hard markets and soft markets, though more so in the former. In this particular instance, for firms seeking to reduce performance, it could well be that “not putting all your eggs in one basket” may not be a bad idea after all.

Introduction: Big data is that disruptive force that affects businesses, industries, and the economy. In 2021, insurance analytics will include more than simply analysing statistics. According to current trends, new insurance big data analytics (BDA) methods will enable firms to do more with their data. The insurance business has traditionally been conservative, but adopting new technology is no longer only a current trend; it must be competitive. Big data technologies aid in processing a huge amount of data, improve workflow efficiency, and lower operating costs.

Purpose: Some of the most recent developments in big data for insurance and how insurers may use the information to stay ahead of their competitors are discussed in this chapter. This chapter’s prime purpose is to analyse how artificial intelligence (AI), blockchain, and mobile technology change the outlook and working of the insurance sector.

Methodology: To achieve our research purpose, we analyse case studies and literature that emphasise how BDA revolutionises the insurance market. For this purpose, various articles and studies on BDA in the insurance market will be selected and studied.

Findings: From the analysis, we find that the use of big data in the insurance business is growing. The development of BDA has proven to be a game-changing technology in insurance, with a slew of benefits. The insurance sector is now grappling with the risks and opportunities that modern technology presents. Big data offers opportunities that every company must avail of. We can safely argue that big data has transformed the insurance sector for the better. The BDA’s consequences have enabled insurers to target clients more accurately. This chapter highlights that new tools and technologies of big data in the insurance market are increasing. AI is emerging as a powerful technology that can alter the entire insurance value stream. The transmission of any type of digital proof for underwriting, including the use of digital health data, might be a blockchain use case (electronic health record (EHR)). As digital forensics becomes easier to include in underwriting, it must expect price and product design changes in the future. In the future, the internet of things (IoT) and AI will combine to automate insurance processes, causing our sector to transform dramatically. We highlight that these technologies transformed insurance practices and revolutionalised the insurance market.

The purpose of this research study is to determine whether flood-damaged residences located in the USA are remaining unrepaired because of the lack of flood insurance coverage. Unrepaired flooded dwellings are subsequently being foreclosed with mortgage-insurance claims being paid to lenders. This paper aims to examine if weather events that cause flooding impact the losses suffered by mortgage insurers and homeowners.

Two fully modified least squares regression models are done using losses experienced by two mortgage insurance companies. The AM Best insurance rating information for a 16-year period or years 2002–2017 is used to study whether the loss ratios experienced by two companies underwriting private mortgage insurance (PMI) are statistically correlated to National Flood Insurance Program (NFIP) claim levels. The assumption is that higher flood insurance claims are a proxy for more severe weather events during a particular year which results in flooding that damage residences.

The NFIP claims coefficient is positive and significant for both companies being examined. This indicates that the more serious the flooding event during a specific year, the higher the losses experienced by the private mortgage insurer. The R² results for the regression models were 0.673–0.695. The income variable has a negative coefficient which was significant. It indicates that falling income lead to rising mortgage insurer losses. The NFIP variable was significant with a positive coefficient.

The mortgage insurance industry is dominated by several companies at any point in time. During the 16-year study period, some companies have become insolvent, merged with other companies or recently started underwriting mortgage insurance. One company was diversified writing multiple lines of property insurance. There were only two insurers with complete financial information for the specified study period.

There are currently five mortgage insurers operating in the USA. A serious flood event could cause the insolvency of some of these companies. This would reduce the competition existing in the default insurance market. The financial markets for real estate loans price mortgages based on the availability and the ability to secure mortgage insurance for high loan-to-value properties. There is federal mortgage insurance available for certain types of residential loans.

There are a limited number of insurers writing flood insurance. These companies can pick or reject dwellings and/or commercial properties to underwrite for insurance. The goal of phasing out insurance through the NFIP may prove impossible to achieve. A flood event without insurance would cause serious financial consequences to property owners, loan delinquencies and could depress the local economy for years. Competition from private mortgage insurers may intensify the adverse selection already being experienced by the NFIP. Private insurers would select the lower risk flood applications leaving the more risky insurance to be covered by the NFIP.

Prior research focused on financial variables impacting PMI and weather factors affecting flood insurance claims. Financial ratios published in the AM Best rating guide for the USA and Canada were used to examine whether or not PMI losses are indirectly affected by flooding events as measured by NFIP variable. Comparing two separate lines of insurance and their impact on each other has not been studied by prior researchers.

Property and casualty (“P&C”) insurance companies rely on “risk capital” to absorb large losses that unexpectedly deplete claims‐paying resources and reduce underwriting capacity. The purpose of this paper is to review the similarities and differences between two different types of risk capital raised by insurers to cover losses arising from natural catastrophes: internal risk capital provided by investors in insurance company debt and equity; and external risk capital provided by third parties. The paper also explores the distinctions between four types of external catastrophe risk capital: reinsurance, industry loss warranties, catastrophe derivatives, and insurance‐linked securities. Finally, how the credit crisis has impacted alternative sources of catastrophe risk capital in different ways is considered.

The discussion is based on the conceptual framework for analyzing risk capital developed by Merton and Perold.

In 2008, the P&C insurance industry was adversely affected by significant natural catastrophe‐related losses, floundering investments, and limited access to capital markets, all of which put upward pressure on catastrophe reinsurance premiums. But the influx of new risk capital that generally accompanies hardening markets has been slower than usual to occur in the wake of the credit crisis. Meanwhile, disparities between the relative costs and benefits of alternative sources of catastrophe risk capital are even more pronounced than usual.

Although many insurance companies focus on how much reinsurance to buy, this paper emphasizes that a more important question is how much risk capital to acquire from external parties (and in what form) vis‐à‐vis investors in the insurance company's own securities.

The purpose of this article is to examine the existence of underwriting cycles in property‐liability insurance for Switzerland, the USA, Japan, and West Germany over a period of 40 years (1957‐1997), i.e. it looks at the question of whether the unit price of insurance coverage (given by the inverse of the loss ratio) fluctuates cyclically over time. The article serves as basis and starting point for Part II, where some of the limitations of the model presented here are dealt with.

Loss ratio data for the four countries are used for the recent period 1957‐1997. To test for the existence of cycles and calculate their length, the article applies autoregressive processes of second order, which were brought to a broader audience by a paper by Cummins and Outreville in 1987. The article also conducts a spectral analysis of the series.

For West Germany, much longer cycles than in earlier studies were found for the basic model. In general, the cycles get longer for the longer period, 1957‐1997. The article concludes that the hypothesis of cycles of six years in length no longer holds globally. It also finds cross‐country differences for the primary markets of the four countries.

Most empirical work on underwriting cycles has so far been carried out on US data. This study replicates a previous study for four countries on three continents and discusses the results and some limitations. It serves as the basis for Part II of this work.