Volume 5 Issue 8 - September 19, 2008
Significant Factors of Aviation Insurance and Risk Management Strategy: An Empirical Study of Taiwanese Airline Carriers
Yu-Hern Chang* and and Yi-Hsin Lin


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Aviation insurance premiums have become a heavy burden for the airline industry since September 11, 2001. Although the industry must constantly balance its operations between profitability and safety, the reality is that airlines are in the business of making money. Therefore, their ability to reduce cost and manage risk is a key factor for success. Historically, insurance theory was based on “the law of large numbers”, which can be defined as a device for reducing risk by combining a sufficient number of homogeneous exposure units to make individual losses collectively predictable. Due to the inherent risk and the large insurance premiums paid by the airlines, insurers consider the pricing increasingly difficult and volatile.

Aviation insurance is a unique field based on business, legal, and regulatory standpoints (El-Kasaby, 2003). After September 11, aviation insurance, which is essential to airline operations, became a key factor in airline management. Aviation risks are very complex and costly; thus, the risks are usually shared by several insurers with a specific aviation insurance market. Each insurer is liable for that part of the risk it agrees to cover. Aviation insurance is an ideal risk-management tool from the airline’s financial standpoint. Its objective is to manage pure risks in order to assure the financial solvency of the firm at the lowest possible cost (Wells & Chadbourne, 2000). Even though advanced technology provides extremely capable machines, aircraft accidents still happen. Hence, risk transfer through insurance is essential, and risk evaluation is required.

Based on these arguments, we used a two-dimensional risk analysis matrix to analyze exposures. The degree of the loss is displayed on the vertical axis, and the frequency is displayed on the horizontal axis (Fig. 1). The resulting four quadrants are Avoidance and Controlled Loss, Transferred Risk, Undertaken or Ignored Risk, and Prevention-Reduction Risk. In the Avoidance and Controlled Loss quadrant, the high frequency and high severity characteristics of the exposure mean that these are probably not transferable. Measures to avoid loss completely or the use of crisis management to control loss should be implemented. The characteristics of the losses in the Transferred Risk quadrant are important to airlines with large assets under risk management. Because firms should avoid paying losses of this magnitude using company resources, they should attempt to transfer these exposures to others. The high costs of an accident can be reduced through insurance coverage. In the Undertaken or Ignored Risk quadrant, airlines usually handle adequate funds, utilize deductibles and self-insurance programs, or establish a captive insurance company to manage these risks or ignore them altogether because they would result in only a minor drain on the firm’s resources. The Prevention-Reduction Risk quadrant contains exposures with low severity but relatively high frequency. As discussed above, airlines are currently developing and using new safety management techniques to prevent large disasters associated with human errors.
Fig. 1. Frequency-severity analysis matrix

Because the degree of external environmental influence on each airline is the same, this study focuses specifically on internal operational factors at six Taiwanese airlines. To difficult in obtaining aviation-insurance premium data and individual airline operational information, we selected twenty-two relational factors and classified them into five dimensions: (a) fleet profile, (b) operations, (c) losses, (d) flight crew performance, and (e) financial stability. The relational factors within each dimension are shown in Table І.
Table І. Dimensions and Factors Affecting Aviation Insurance Premium Rates

This study used grey relation analysis as its analytical tool, which is appropriate when the sample size is small or the distribution of residuals is unknown (Feng & Wang, 2000). Grey relation analysis is a useful mathematical method used to sort out the correlation extent of effect factors in a system with uncertain information (Deng, 1982, 1989; Fu et al., 2001). The advantages of this method of analysis include (a) simple and easy calculation, (b) a limited amount of samples required, and (c) that quantified outcomes from a grey relational grade do not contradict conclusions from qualitative analysis (Deng, 1982; Shi, 1990).

The results of the grey relation analysis (Table II) explicitly capture the top 11 influential factors (r ≥ 0.7), which are the number of fatalities and claims in the previous year, RPKs, number of accidents in the previous year, the ratio of defects of flight crews to number of total checks, the available seats of the fleet, the number of aircraft, the risk index of the geographical regions of major air routes, the number of incidents in the previous year, the average age of the fleet, and the annual flight hours of the fleet.
Table II. The Grey Relation Coefficient (r) and Rankings of Factors and Dimensions with Aviation Insurance Premiums (A0)

No matter what type of insurance the underwriters provide, the most important factor for premium ratemaking is an individual client’s previous claims. The major finding of this research is that loss experience is the most critical dimension in determining premium level. The number of fatalities and claims in the previous year are ranked the first and second most influential factors.The relevant literature showed that most aviation passenger liability insurance premiums are based on RPKs and the number of passenger seats. In this study, RPKs is ranked third. Also, because flight crews are a key determinant of flight safety, the ratio of flight-crew defects to the number of total checks is also influential and was ranked fifth in importance. Accidents and incidents are typically caused by a combination of multiple interrelated sequential events and failures (Chang & Yeh, 2004). Undoubtedly, these events and failures will become the primary factors used to determine the potential risks for unanticipated future losses. Moreover, insurance premium rates also depend on the previous accumulation of numerous small claims. Therefore, the number of accidents and incidents in the previous year are ranked fourth and ninth, respectively.

The fleet profile is another major dimension that affects aviation hull premiums. The premiums are relatively high if the airline owns a large fleet with a variety of sizes of aircraft. Therefore, the influential factors are the available seats, the number of aircraft, the average age, and the annual flight hours of the fleet. In the operational dimension, statistical reports show that aircraft take-offs and landings are the most crucial periods during the flight. Therefore, the number of flights per year is another important factor. In terms of the risk index of geographical regions of major air routes, the burn rates and safety multipliers of different regions were considered, and this factor is ranked eighth in importance.

This study presents a general framework for advancing the knowledge of underwriting aviation insurance. The results of this study provide airlines with information about the overall risk management map as well as individual influential factors that aviation insurers consider when determining insurance premiums. It may help airlines improve their operations and safety management strategies, and, we hope, lead to reduced aviation insurance premiums. In particular, these factors may help underwriters focus on the major operational and managerial weaknesses of individual airlines in specific divisions. Furthermore, airline insurers also must ensure greater transparency and consistency in pricing the risks, and provide more detailed analyses of each airline so that individual airlines might obtain more accurate risk profiles of their business.
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