EMPIRICAL FINDINGS ON MOTOR INSURANCE PRICING IN GERMANY, AUSTRIA, AND SWITZERLAND

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1 EMPIRICAL FINDINGS ON MOTOR INSURANCE PRICING IN GERMANY, AUSTRIA, AND SWITZERLAND DANIELA LAAS HATO SCHMEISER JOËL WAGNER WORKING PAPERS ON RISK MANAGEMENT AND INSURANCE NO. 155 EDITED BY HATO SCHMEISER CHAIR FOR RISK MANAGEMENT AND INSURANCE OCTOBER 2014

2 in Germany, Austria, and Switzerland Daniela Laas, Hato Schmeiser and Joël Wagner Abstract This paper focuses on recent developments in motor insurance pricing in Germany, Austria, and Switzerland. Through the analysis of responses to a recent comprehensive survey of industry representatives, we examine the various premium components and the processes involved in premiums adaptation. New findings on the use of different tariff criteria, on the tools used for market-based and customerspecific pricing, and on the information considered for customer valuation are reported. We also focus on the discounts and surcharges applied by insurers. With regard to pricing processes, we report data on the frequency, time required, and costs incurred for premium reviews, premium adjustments, and the introduction of new tariffs. Our findings are intended to be relevant for academics as well as practitioners: On the one hand, we contribute to a strand of literature where little academic research has been done so far. On the other hand, our results entail managerial implications for improving industry practices in insurance pricing. Keywords insurance pricing motor insurance tariff criteria pricing process industry survey 1 Introduction One consequence of the deregulation of European insurance markets is an increase in competition among market players (see, e.g., Eling and Luhnen, 2008). In Germany in particular, motor insurance premium levels have been under pressure in recent years, leading to combined ratios of around 100% (see, e.g., Gesamtverband der Deutschen Versicherungswirtschaft, 2012). As competition between insurance companies intensifies, higher efficiency and greater focus on profitability are required. While the potential for cost reductions is limited, improvements in profitability and growth can be achieved through sophisticated pricing management mechansims (see, e.g., Schmidt-Gallas and Lauszus, 2005, and Pratt, 2010). The analyses in this paper are closely linked and refer to a recent comprehensive study on pricing strategies in motor insurance in Germany, Austria, and Switzerland, provided in Hartmann et al. (2014). In this survey, insurance industry practitioners are asked about the current shape of the market and trends Daniela Laas and Hato Schmeiser are with the Institute of Insurance Economics, University of St. Gallen, Tannenstrasse 19, CH-9000 St. Gallen. Joël Wagner is with the Department of Actuarial Science, University of Lausanne, Extranef, CH-1015 Lausanne. We are grateful to Christoph Nützenadel and Michael Hartmann from Solution Providers for their support in the survey part of this research project. 1

3 anticipated for coming years (see Hartmann et al., 2014, Figs. 47 and 48). With regard to the determination of the premium, experts make mention of increasing price differentiation according to individual risk profiles (T1) and based on alternative customer characteristics (T2). Furthermore, the average duration of customer relationships is expected to decrease (T3) and customers will be increasingly sensitive to price levels (T4). In the future, the market will also see heightened price competition (T5) and more rapid tariff adjustments (T6). The above trends signal challenges to the calculation of the various premium components and the processes for premiums adjustments and the introduction of new tariffs. For premium components, we distinguish between the traditional actuarial premium, cost loadings, adjustments for market conditions and the customer s profitability as well as any surcharges and discounts applied during the insurance distribution phase (see, e.g., Erdönmez et al., 2007, Fig. 4). Increased risk differentiation and the consideration of alternative risk characteristics (see trends T1 and T2 above) are posing challenges for actuarial pricing. Furthermore, shorter customer relationships and higher customer price sensitivity (trends T3 and T4) require improved customer-specific pricing. Insurers need to use variables that better describe the customer s value during the whole relationship, including future profits and value created in other business lines. This will have an impact on the individual loadings and discounts applied. In this area, insurance sales staff and distribution networks can often have a considerable influence on the final price. In fact, direct and personal contact with the customer often helps staff acquire better knowledge of the risk and thereby fine-tune the premium. As a result of the more competitive landscape (T5), the market-based component of the final price for insurance must be closely examined. The use of marketand segment-specific surcharges and discounts must be effective and needs to be included in the premium models to ensure profitable underwriting. In our study, we will consider these different trends and present our findings with regard to risk-specific, customer-specific and market-based pricing. We also consider the integration of the sales channel in price determination. In a second batch of results, we illustrate our findings relating to pricing processes. In order to evaluate the ability of insurers to adjust tariffs more rapidly (T6), we analyze the frequency of pricing reviews and premium adjustments, the time needed and costs incurred for premium adjustments and the introduction of new tariffs. Our paper extends the work of Hartmann et al. (2014) in several ways: First, while the study of Hartmann et al. (2014) mainly focuses on practitioners in the three German-speaking countries (and is therefore also written in German), our paper is more research oriented and takes into account numerous previous academic publications on motor insurance pricing. Thus, in contrast to Hartmann et al. (2014), who compare their results only with those of Erdönmez et al. (2007), we contrast our findings with those of previous academic research. Second, we do not restrict our analyses merely to descriptive evaluations of the survey (as done in Hartmann et al., 2014). Instead, we use several statistical methods (e.g., ANOVA, ordered logit regressions, correlation analyses) in order to attain a more comprehensive view. Our analyses not only permit a better identification of differences between firms of different sizes and in different countries, but also the characterization of relations between a number of factors involved in pricing. Finally, we also derive some practical implications and suggestions for improvements of interest to the insurance industry. The results from our paper highlight differences among the three countries and provide insights about the dimensions considered. In particular, we find strong discrepancies in the sophistication of risk segmentation 2

4 and thus in the complexity of tariff schemes. Furthermore, we note a considerable backlog in the development of the components of market- and customer-specific pricing and the integration of the sales channel in price determination. Concerning the processes for premium adjustments and the introduction of new tariffs, our results indicate substantial performance differences, with Swiss companies having the most time- and cost-efficient processes. Our contribution is intended to be relevant for both academics and practitioners in the insurance industry. As there are substantial shortcomings in the non-traditional fields of pricing and academic work in these areas is scarce (see the next section), our results emphasize the need for further research and the development of approaches that integrate all these factors in the calculation of premiums. Practitioners may benefit from our analysis as they can identify possible shortcomings in their pricing processes and assess their performance in comparison to their competitors. The paper also offers several suggestions for improvements. In addition, knowledge of country-specific differences is important when better performing foreign motor insurers enter a company s domestic market or when the company in question expands into another country. The remainder of this paper is structured as follows. In Section 2, we provide a short summary of the literature on motor insurance pricing. In Section 3, we briefly describe current market developments and discuss our analysis methodology. Section 3.1 offers an overview of the current market situation, Section 3.2 provides an outline of the questionnaire, Section 3.3 describes the data set, and Section 3.4 presents the statistical methods used. In Section 4, we show and discuss the results of the study. First, results relating to actuarial pricing (Section 4.1) and pricing components (Section 4.2) are discussed. Processes for premium adjustments and the introduction of new tariffs are analyzed in Section 4.3. Section 5 suggests several practical implications and Section 6 presents our conclusions. 2 Literature Review In order to place our paper in the context of research on motor insurance pricing, we provide a short overview of the academic literature and industrial studies on this subject. As there is an abundance of related academic papers, we focus on the main topics and some examples. With respect to the studies aimed at practitioners, we concentrate on work from recent years with a strong focus on motor insurance pricing. Academic Papers By far, most of the research has been done in the area of actuarial pricing. A substantial body of literature exists about the theory of risk classification, especially its effects on adverse selection, its profitability, costs, fairness, and efficiency (see, e.g., Doherty, 1981, Hoy, 1982, Abraham, 1985, Crocker and Snow, 1986). In this context, many articles also analyze the effects of regulatory restrictions on risk differentiation (see, e.g., Harrington and Doerpinghaus, 1993, Hoy, 2006, Thomas, 2007, Thomas, 2008). Moreover, a series of criteria for risk classification schemes and rating factors are formulated (see, e.g., Finger, 1996). Numerous papers develop and analyze approaches for the selection of classification criteria and the calculation of the actuarial price. One of the predominant methods is the minimum-bias approach proposed by Bailey and Simon (1960) for multiplicative tariff models. This approach was further developed by Bailey 3

5 (1963), Jung (1968), and Ajne (1975), among others, and is also a topic in current research (see, e.g., Ismail and Jemain, 2006). A second approach that attracted a great deal of attention is based on experience rating and credibility theory. The most famous credibility model was introduced by Bühlmann (1967) and Bühlmann and Straub (1970). This model, the estimation of its parameters, as well as possible enhancements were examined in a large number of subsequent papers, including Bichsel and Straub (1970), Sundt (1988), De Vylder and Goovaerts (1992), Dannenburg (1994), and Young and De Vylder (2000). A third category of models frequently proposed and analyzed for the modeling of motor insurance claims is that of (generalized) linear models (see, e.g., Baxter et al., 1980, and Stroinski and Currie, 1989). However, the three types of models are not mutually exclusive. Mildenhall (1999) and Nelder and Verrall (1997) show that generalized linear models can be related to the approach used by Bailey and Simon (1960) and Bailey (1963) as well as Bühlmann s credibility model. In addition, Ohlsson (2008) proposes a method that combines generalized linear models and the model discussed by Bühlmann and Straub (1970). While the aforementioned papers mainly consider risk classifications and calculation methods in general, many articles from different research fields (actuarial science, transportation research, accident analysis) examine specific tariff criteria from different perspectives (e.g., predictive power, social acceptance, discrimination). For example, various analyses consider the driver s age (see, e.g., Kelly and Nielson, 2006, Brown et al., 2007, Oxera, 2012), the gender criterion (see, e.g., Oxera, 2010, and Schmeiser et al., 2014), or the vehicle s characteristics (see, e.g., Wenzel and Ross, 2005, and Kim et al., 2006). Furthermore, several authors investigate the effect of annual mileage on the frequency and severity of accidents, especially following the introduction of pay-as-you-drive tariffs (see, e.g., Janke, 1991, Litman, 2011, Boucher et al., 2013). The influence of driving behavior on crash risk is studied by Klauer et al. (2009) and Paefgen et al. (2013b), among others. The latter also develop an approach for integrating driving behavior in the pricing of payhow-you-drive tariffs. With regard to pay-how-you-drive tariffs, some analyses also focus on the effect of these tariff schemes on driving behavior (see, e.g., Bolderdijk et al., 2011, and Greaves et al., 2013). In contrast to the field of actuarial pricing, the areas of customer-specific and market-based pricing and the integration of the sales channel are scarcely considered in the academic literature. 1 To the best of the authors knowledge, only some aspects of the customer-specific component have been addressed. These include the identification of influence factors on customer loyalty (see, e.g., Schlesinger and von der Schulenberg, 1993, and Brockett et al., 2008), and the development of methods for the prediction of the customer retention and its dependence on the premium level (see, e.g., Smith et al., 2000, Yeo et al., 2001, Guelman and Guillén, 2014). In addition, a series of papers deals with the estimation of the customer lifetime value (see, e.g., Ryals and Knox, 2005, and Donkers et al., 2007) or the customer s potential (see Verhoef and Donkers, 2001). Thuring et al. (2012) and Kaishev et al. (2013) further propose methods to identify profitable clients with cross-selling potential, which is important for decisions about premium discounts (see Thuring et al., 2012). 1 We focus on papers on pricing in insurance companies. In the general marketing literature, more research has been done. For a comprehensive article on the different components, see, e.g., Hinterhuber (2004). 4

6 Industrial Studies Besides the above mentioned academic papers, numerous studies aimed at practitioners in the insurance industry can be found. As it uses the same data set, the analysis with the closest link to our paper is the work of Hartmann et al. (2014). This is a follow-up study to the study by Erdönmez et al. (2007). The survey by Earnix (2011a) examines some similar aspects on pricing in the motor insurance sector (as well as other business lines), especially concerning the process for premium adjustments. However, its focus is on the European market. Analyses of the development of market-based and customer-specific pricing are, among others, provided by Schmidt-Gallas and Lauszus (2005) and Schmidt-Gallas and Beeck (2009). Moreover, Schmidt-Gallas and Beeck (2009) and Schmidt-Gallas et al. (2010) comprehensively describe current shortcomings and necessary improvements in the integration of the sales channel in Germany. Requirements for the pricing organization and pricing process are set out, e.g., in the paper by Niessen (2013) and the report by Schmidt-Gallas and Lauszus (2009b). Furthermore, in recent years, numerous studies and consultative papers have been written that focus on usage-based pricing by means of telematics-based tariffs. A very comprehensive global study on insurance telematics, its development, its drivers and barriers, as well as many other aspects was published by Bruneteau et al. (2013). Other reports on telematics in motor insurance, associated opportunities and risks are, e.g., described by Reddy (2012), Brat et al. (2013), and Jubraj et al. (2014). Contributions of our Analysis This paper contributes to the academic literature in several ways. First, the usage of various tariff criteria in the field of actuarial pricing is analyzed from a new perspective, as motor insurers are directly asked for the relevance of different tariff factors. Earlier papers, in contrast, mostly try to identify tariff characteristics with significant influence by means of analyses of premiums observed in the market. This procedure may lead to imprecise results as market premiums differ from the pure risk-based price. Second, we provide a structured analysis of the different premium components and the developments in these fields. As far as the authors are aware, to date this has only been done in some of the industrial studies mentioned above, but not in a research-oriented academic paper. Existing research is therefore mostly limited to descriptive analyses and merely capitalizes on the results of earlier academic articles. However, our work shows that a comprehensive examination in the context of an academic research project is long overdue, as the results emphasize the need for further research in the nontraditional fields of pricing and the development of integrated pricing models. Third, comprehensive analyses of processes for premium adjustments and the introduction of new tariffs are necessary in order to explain performance differences between countries and companies. 2 2 Fast and frequent premium adjustments are an essential competitive factor (see, e.g., Earnix, 2011b). However, there are numerous other factors that influence a company s performance. 5

7 3 Market Overview and Research Description The various factors entering into the determination of insurance rates depend on the market conditions. In Section 3.1, we outline the regulatory framework and the competitive landscape in a brief market overview. In Sections 3.2 and 3.3, and we describe our questionnaire and data set. Finally, the methods used in our analyses are reviewed in Section Market Overview Currently 99 insurance companies offer motor insurance in Germany. 3 For the Swiss and Austrian markets, there are 21 and 24 such companies, respectively. The gross premium volume for the German motor insurance market (including all lines) for 2012 is EUR billion (Swiss market: CHF 5.65 billion; Austrian market: EUR 2.95 billion). Loss ratios for the motor insurance sector based on book values given by the annual reports of the relevant insurance companies are around 92% in Germany (65% in Switzerland; 72% in Austria). There are wide differences in market concentration across the three insurance markets: In 2011, the market share of the top ten players in Germany is around 50% (Switzerland: 99%; Austria: 86%). The market share of the top five motor insurance players is 34% for Germany, 78% for Switzerland, and 66% for Austria. Even though only Germany and Austria belong to the European Union, regulatory requirements within the three countries considered in our analysis are quite similar. Hence, we believe a comparison of motor insurance pricing management mechanisms within the three countries is entirely feasible. In contrast to the US market in general, there is no form of rate regulation in the three analyzed markets. In the third party liability sector and similar to many other insurance markets, we face compulsory lower limits for the sum insured. In addition to a general regulatory framework regarding concession requirements in order to act as an insurer, admissible legal forms for insurance companies or regulations in respect to the asset allocation, general risk-based capital requirements for insurers are in force. For the three insurance markets, Solvency I is still the relevant framework to derive minimum capital standards for insurance companies. However, Solvency I is mainly based on the volume of the insurer s underwriting book and not on its risk. To overcome this shortcoming, Switzerland introduced the Swiss Solvency Test SST in 2006 (in addition to Solvency I) in order to obtain target capital requirements for primary insurance and reinsurance companies. In the European Union and therefore as regards the Austrian and German market, we expect new minimum and target capital requirements (Solvency II) to be at least partly in force by early Description of the Questionnaire The questionnaire was originally designed for a comprehensive study on motor insurance pricing aimed at practitioners in the insurance and consulting industry (see Hartmann et al., 2014). The questionnaire was designed to obtain a comprehensive perspective on motor insurance pricing, both the current context and significant trends. It is based on the questionnaire used for a similar survey (see Erdönmez et al., 2007), but 3 For the market data provided in this section, see Gesamtverband der Deutschen Versicherungswirtschaft (2013), Versicherungsverband Österreich (2013), Swiss Insurance Association (2014), and Hartmann et al. (2014). 6

8 also takes the latest developments in the motor insurance sector and the recommendations of some pricing experts into account. Our survey comprises seven question sets labeled A to G, each one including several questions (or items) covering a range of motor insurance pricing dimensions. In the following, we give a detailed description of the question sets used in the analyses presented in this paper. Question Set A: Number of Customer Characteristics In order to determine the complexity of actuarial pricing, the companies are asked for the number of collected (A1) and used (A2) characteristics. While (A1) refers to the total number of characteristics collected on the policyholder, the vehicle, the drivers, etc. the insurers record in their database, (A2) only refers to those criteria that are actually used for rate determination. (A2) is typically lower than (A1), as some information might be collected for administrative purposes or in case it might be needed later. In the survey, the companies can choose any number between 1 and 50 and their answers are measured on an interval scale. Question Set B: Impact of Tariff Criteria on Premium In this question set, survey participants are asked to assess the impact of different tariff criteria on the actuarial price. For each characteristic, the degree of impact is measured by means of a five-point Likerttype scale ranging from 1 (very limited) to 5 (very strong). Thus the response variables have an ordinal scale level. Following Meyer (2005), the tariff criteria are grouped into three categories. The first category comprises five items that describe the policyholder or the most frequent driver. The second encompasses six criteria pertaining to the insured vehicle. Finally, the third contains four rating characteristics that provide information about the vehicle usage. Question Set C: Availability of Pricing Tools This question set is designed to investigate the degree of development of the non-actuarial pricing components. For each component, the insurers are asked to state the availability of several tools that can support the determination of a possible discount or surcharge. These tools include those for market-based and customer-specific pricing as well as tools to integrate the sales channel in price determination. For assessing availability, three answers can be chosen: not available, partially available, and available. A tool is considered to be partially available if the firm has already started implementation, but it has not been completed, meaning that the tool cannot be used to its full extent. As there is a natural ordering in the possible answers, the scale level is again ordinal. Question Set D: Discounts and Surcharges In order to figure out the influence of the factors market (D1), potential value of the customer (D2), and sales channel (D3) on the final premium, the participants are asked to specify the maximum allowed derivation from the actuarial price. They can either choose one of the intervals < 5%, 5% 10%, 10% 20%, > 20% or indicate that they do not have a fixed limit for the surcharges or discounts. This 7

9 last category is necessary as some insurers do not have fixed limits at all or at least not on the basis of an individual contract. For example, sometimes the distribution sales force only has to reach certain goals for its whole portfolio but not for single policies. While there is a natural order between the first four answer categories, it is not possible to establish a rank order when the last answer option ( no limit ) is taken into account. In the following, we call this type of scale level semi-ordinal. Question Set E: Information Used for Customer Valuation The aim of this question set is to get some further insights into the customer-specific pricing component by identifying the information the companies use for the valuation of their customers. The question set includes 15 characteristics that can be classified into three categories depending on whether they describe the policyholder himself, the current relationship between the insurance company and the customer, or the expected future relationship. As the value of a customer depends not only on his motor insurance contract but also on his relations with the other insurance lines, information with respect to these relations is taken into account. Question Set F: Number of Pricing Revisions This is the first of two question sets to evaluate the process for premium adjustments and thus the companies flexibility to react to changing market conditions. The insurers are asked to state how often they check the adequacy of their premiums per year (F1) and how many times prices are adjusted (F2). In both cases, five answers are possible: once per year, 2 3 times, 4 6 times, more than 6 times or as required, for the cases where there is no fixed number of reviews or adjustments. Owing to the last answer option, the response variables have a semi-ordinal scale level. Question Set G: Time and Costs for New Premiums Question set G investigates the efforts associated with the introduction of new or modified premiums and thus the efficiency of the pricing process. First, participants are asked about the time needed for the adjustment of existing premium models (G1) and the introduction of new premium schemes (G2). By means of a slider, the number of days needed is chosen. Second, respondents are asked to give information about the costs resulting from premium adjustments (G3) and the introduction of new premium models (G4). Here the number of person-days can be entered. 3.3 Data Description Our analyses are based on data initially collected by Hartmann et al. (2014) in the three German-speaking countries: Germany, Austria, and Switzerland. The questionnaire presented in Section 3.2 is designed as an online survey and responses were collected between April and June In order to reach a high number of participating firms, access to the questionnaire was offered to 312 experts at 72 motor insurance companies in the three countries. 8

10 The questionnaire was completed by 47 respondents from 31 different companies. In the analyses presented in this paper, we only include responses from insurance companies registered as such with the respective local regulatory authority. 4 In cases where multiple responses were collected from the same company, the answer from the most senior motor pricing officer is selected. This process resulted in the selection of 29 responses from insurance companies 9 from Germany, 8 from Austria, and 12 from Switzerland. In order to identify possible response patterns for insurance companies of different sizes, we determine the gross written premiums (GWP) for the motor insurance business line. Using the statements of the local authorities, industry associations and the insurers financial reports, we calculate the companies 2012 premium volume in their motor liability and comprehensive cover business lines. 5 Basedonthepremium figures, we derive their individual market shares in the motor insurance markets of their respective countries. Taking the median of the GWP (about EUR 275 million) as the critical value, we divide the companies into small and large motor insurers. Table 1 summarizes the market shares represented by the survey respondents in the three countries, divided into small and large insurers. The 12 Swiss motor insurers in our sample have a market share above 99%. In Germany and Austria, we obtain coverage of about 27% and 58% of each country s motor insurance market, respectively. Small Large Total Germany Austria Switzerland Table 1: Market Shares of Survey Respondents This table shows the market shares of survey respondents in the three countries (Germany, Austria, and Switzerland) as a percentage of total gross written premiums. The division into small and large insurers is based on the overall median of the GWP in the motor insurance line as the critical value. 3.4 Statistical Methods Depending on the types and scales of the response variables (see Section 3.2), we evaluate the answers by means of different statistical measures and approaches. Besides descriptive analyses (see also the study by Hartmann et al., 2014), we also apply methods from multivariate data analysis. This allows us to identify possible differences with respect to the companies size while controlling for the country and vice versa. 6 However, due to the relatively small sample size, significance tests only have small power and conclusions with respect to the significance of the two factors are possible only to a limited extent. In other words, the country or size effects are significant only when the differences between the subsamples are very large. Therefore, the analyses are only of a complementary nature and can help to separate the country and size effects and to 4 It should be noted that companies acting as independent single players are included (e.g., the Swiss company smile.direct which, although a subsidiary of Nationale Suisse, determines pricing independently). Note that we also take into consideration one respondent who completed the survey only partially. 5 For currency conversions we apply the EUR/CHF exchange rate of as of December 31, As the proportions of small and large insurers differ in the three country-specific subsamples, this is an important issue. 9

11 identify those variables where the discrepancies are considerable. If the tests do not show significant effects but the descriptive analysis indicates the existence of differences, the test results do not imply that there are no size or country differences. Instead, we conclude that for our sample size the discrepancies are not large enough to be declared significant with a small error probability. 7 Ordinal Scaled Variables With regard to the response variables with ordinal scale levels (that is, the answers from question sets B, C, and E), we consider the proportion of answers falling into the different categories. In addition, coding the answer categories with the numeric values 0, 1, and 2 for question sets C and E, and 1 to 5 for question set B, and assuming equal distances between the categories, we determine the mean and standard deviation of the answers. In order to identify possible linkages between the different variables, we calculate Kendall s rank correlation coefficient (Kendall, 1938, and Kendall, 1948). It is nonparametric and shows the extent of monotonic association between two variables (see, e.g., Sprent and Smeeton, 2007). Taking the existence of a large number of ties into account, we test the significance of the correlations (using the R-function of McLeod, 2011). With the aim of finding structural differences between the responses originating from different countries and firms of different sizes, we repeat the descriptive analyses at the country and size levels. Furthermore, we estimate cumulative ordered logit models as specified in Agresti (2013) to determine the significance of the country and size effects. Variables with Semi-Ordinal Scale Level As mentioned in Section 3.2 we use the term semi-ordinal scale level if there is a natural order between all except one answer categories, as in the case of question sets D and F. We analyze semi-ordinal scaled response variables using the following method: As a first step, we evaluate the companies answers descriptively by analyzing their percentage distribution across the different answer categories. We do this both for the whole sample as well as for the country- and size-specific subsamples. As a second step and in order to get a deeper understanding with respect to country- or size-specific effects, we run ordered logit regressions for the subsamples of companies with an answer in one of the ordered categories. This excludes the last category from the analysis, but as the use of multinomial logit models (see, e.g., Agresti, 2013) requires the estimation of considerably more parameters based on a small number of observations, we decided to choose this procedure. 8 Interval Scaled Variables With respect to the interval scaled response variables resulting from question sets A and G, we start by calculating a series of descriptive statistics. We determine the mean, standard deviation, minimum, maxi- 7 For a given significance level the critical region for rejecting a null hypothesis in a t- or F-test is larger for smaller samples. Thus there is a higher probability of not rejecting the null even though it is not correct and the test has a smaller power. 8 In an additional analysis, we included all observations that fall into one of the ordered categories in one group and the remaining in another group. Using logistic regression we then examined if there was a significant relationship between the country / firm size and the group membership. However, as the standard deviations of the estimated parameters were extremely high, we did not include this investigation in our examinations. 10

12 mum, as well as the quantiles for the levels 0.25, 0.5, and In addition, we determine Pearson s rank correlation coefficient for each pair of variables in order to identify possible linkages between the different variables (see, e.g., Pearson, 1896). As Pearson s correlation coefficient only measures the linear dependence of bivariate normally distributed random variables, we also calculate Kendall s tau. Subsequently, the mentioned descriptive statistics are evaluated at country and size levels. This provides initial insights into the existence of discrepancies between companies with portfolios of different sizes and from different countries. Furthermore, we perform two-way analyses of variance (ANOVA) (see, e.g., Miller, 1986). This permits the combined analysis of the country and size effects but has the drawback of requiring equal variances within each population as well as normally distributed error terms for the significance tests (see, e.g., Miller, 1986, and Glass et al., 1972). According to Glass et al. (1972) and other papers, the significance level of the ANOVA F-test is only slightly affected in the case of non-normality (in most cases). In contrast, heterogeneity may lead to substantial deviations from the intended type I error. As these assumptions are difficult to verify for a small data set, we apply standard ANOVA independent of the results of tests for normality or variance homogeneity and interpret the test results very cautiously. Moreover, alternative methods such as the Kruksal-Wallis-Test or Welch s ANOVA require variance homogeneity or normality as well (see, e.g., Shen, 2008) and the calculation of robust standard errors may lead to erratic results due to the small sample size (see, e.g., MacKinnon, 2013). 4 Results and Discussion We report the results from our study along three key dimensions. In Section 4.1, we focus on actuarial pricing. Next, we discuss the results for other pricing components (Section 4.2). Finally, Section 4.3 concentrates on processes for premium adjustments and the introduction of new tariffs. The descriptive analysis results for the entire sample are summarized in Tables 2 to 8. These tables also show the significance of the country and size effects according to the ordered logit regressions and ANOVAs. The correlation tables can be found in the appendix (Tables A1 to A3). The results of the descriptive analyses at country and size level as well as the detailed ordered logit and ANOVA results are not included in the paper and can be provided upon request by the authors. 4.1 Actuarial Pricing In the first step, insurance companies calculate the actuarial premium based on a series of tariff criteria. In this section, we examine the number of rating characteristics (question set A) as well as their impact on the tariff (question set B). 9 As the number of tariff criteria must be an integer, the empirical quantiles are calculated in the case of question set A. In contrast, a continuous quantile function is assumed for question set G (the quantile function of type 7 according to Hyndman and Fan, 1996). 11

13 Number of Characteristics Table 2 summarizes the answers to question set A. It shows that motor insurers in the three Germanspeaking countries collect an average of 24 characteristics (A1). However, as the answers range from 4 to 46 characteristics, there are huge discrepancies between the companies. The mean number of tariff criteria (A2) amounts to 19 and is thus smaller than the average of (A1). With a minimum of 2 and a maximum of 40, the number of characteristics used for rating also varies considerably. Mean St. dev Min Q 0.25 Q 0.50 Q 0.75 Max C (A1) Collected characteristics * (A2) Used characteristics *** Table 2: Number of Customer Characteristics The table contains contains the results for the variables number of collected characteristics and number of characteristics used for rating. The values in the first five columns correspond to the mean, standard deviation, minimum, 25%/50%/75%-quantile and the maximum of the number of collected / used criteria. The last column, denoted with C, shows the significance of the country effect in the ANOVA regressions (the size effect is insignificant for both variables). *** indicates significance at the 1%-level, ** significance at the 5%-level, and * significance at the 10%-level Our finding that insurers use less criteria for rating than they actually collect can be explained in several ways. For example, some information may be necessary for administrative purposes or captured in case it might be needed in the future. The latter could occur if some criteria are prohibited by the legal authorities, such as gender by the Unisex-directive in the European Union since the end of the year Due to the correlation of various characteristics, if one criteria is forbidden, it may be replaced by other ones without too much loss of precision. For example, Aseervatham et al. (2013) show that the omission of gender can be compensated almost completely by other characteristics (among others, engine power and annual mileage) and merely affects the calculated premium. A country-specific analysis shows that German companies collect and use the highest amount of information. On average, they collect 29 characteristics and apply 26 tariff criteria for actuarial pricing. In Switzerland, somewhat fewer criteria are collected and used, on average (26 and 20, respectively). Austrian motor insurers rely on the lowest number of criteria, by far. The mean numbers of collected and used characteristics in Austria are 17 and 9, respectively. Comparing the descriptive statistics for the groups of small and large insurers indicates that companies with a large motor insurance portfolio tend to collect and use more information than their smaller competitors. On average, large companies collect (use) 7 (4) characteristics more than small firms. ANOVA analyses for the number of collected and used characteristics show a significant effect of the country factor. 10 Thus, the discrepancy between the countries is so enormous that even in our rather small sample we can conclude at a very high confidence level that the number of tariff criteria varies significantly between the three insurance markets. In contrast, according to the ANOVAs, the differences with respect to the firm size are not significant. Two conclusions are possible: On the one hand, the firm s size might in fact not have a significant influence and the observed differences in the means might result from the fact that 10 The ANOVA assumptions cannot be rejected for both variables in several tests for normality and Levene s test for variance homogeneity. 12

14 the subsample of small companies comprises more Austrian and fewer German insurers than the subsample of large firms. On the other hand, the result might be attributed to the small power of the significance test and there could be differences with respect to the firm size nonetheless. As insurers with a small motor insurance portfolio have less data available for calibrating their tariffs it is very likely that they are less able to differentiate between their policyholders than large companies. Impact of Different Tariff Criteria Earlier papers on motor insurance pricing show a varying impact of the various tariff criteria on the premium (see, e.g., Meyer, 2005, and Störmer and Wagner, 2013). As insurance companies in general do not disclose their actual rate calculations, this result is typically based on analyses of existing premiums in the market. Table 3 gives an overview of the influence of a series of tariff criteria when asking the companies directly. With regard to the characteristics of the policyholder or most frequent driver, the driver s age, captured by (B1) and (B3), plays a key role at virtually all companies. Moreover, the place of residence (B4) has a moderate or strong impact in the majority of firms while the driver s family status (B5) seems to be of little importance. The impact of the date of driving license acquisition varies between the companies. The vehicle type or make (B6) turns out to be the most important criterion among the vehicle characteristics, followed by the date of first registration (B7) and power-to-weight ratio (B9). In contrast, the energy efficiency (B10) and method of financing (B11) have little or no impact on the premium for the majority of insurance firms. The type of usage (B12) and the annual mileage (B13) seem to be the most influential criteria among those describing the vehicle usage. Nonetheless, they do not rank as high in importance as the driver s age, the vehicle type, and the registration date. The two remaining criteria (B14) and (B15) have a limited or very limited impact for the majority of companies. There are several reasons for these differences in influence. One major cause is their varying predictive power with respect to future claims. Other aspects are the costs for their implementation or the possibility of being manipulated by the customer (see, e.g., Meyer, 2005, and Kelly and Nielson, 2006). For example, if the accuracy of the customer s information is difficult to verify, to the extent that there is a high probability of insurance fraud, the company should not rely heavily on that criterion (see Abraham, 1985). Furthermore, the results are to a large extent in line with the findings of previous papers. As indicated by the annual accident statistics (see, e.g., Statistisches Bundesamt, 2014) and shown, e.g., by Kelly and Nielson (2006) and Oxera (2012), there exists a strong U-shaped relationship between the driver s age and expected losses. While young drivers on average have larger claims as they have less driving experience and take more risks, older individuals are riskier drivers due to a deterioration of their cognitive and sensory skills (see, e.g., Kelly and Nielson, 2006, McKnight and McKnight, 1999, McKnight and McKnight, 2003). Age is therefore a very powerful predictor of future claims (see, e.g., Kelly and Nielson, 2006, and Oxera, 2012). In addition, age is tied to a number of other useful characteristics (e.g., reliability over time, low implementation costs, objectivity) and is superior to several other rating criteria (e.g., the number of years since obtaining a driving license) (see Kelly and Nielson, 2006). 13

15 Very limited (1) Limited (2) Moderate (3) Strong (4) Very strong (5) Mean St. deviation Country effect Policyholder / most frequent driver (B1) Age / date of birth (B2) Date of driving license acquisition (B3) Group of drivers / young people * (B4) Place of residence * (B5) Family status / household composition Vehicle (B6) Make / type ** (B7) First registration * (B8) Cubic capacity (B9) Power-to-weight ratio *** (B10) Energy efficiency (B11) Method of financing *** Vehicle usage (B12) Type of usage (private / business) ** (B13) Annual mileage *** (B14) Driving behavior (B15) Garage * Table 3: Impact of Tariff Criteria on Premium This table summarizes the answers with respect to the strength of the impact of different tariff criteria on premiums. The values in the first five columns correspond to the percentages of companies where the impact is very limited (1), limited (2), moderate (3), strong (4), and very strong (5), respectively. Columns 6 and 7 show the means and standard deviations of the answers when the five categories take on the values 1 to 5. In the last column the asterisks ***/**/* indicate the significance of the country factor at the 1%/5%/10% level in the ordered logit regressions (the size factor is insignificant for B1 to B15). 14

16 The relatively high importance of the place of residence (B4) is in line with the high regional differences noted for motor insurance premiums (see, e.g., Köhne, 2011). Thus, motor insurers still heavily rely on regional classes for tariff schemes. The reason for this is that the frequency and severity of accidents differ substantially between places due to different traffic volume and road conditions (see, e.g., Etgar, 1975, and Sipulskyte, 2012). While the number of accidents is typically higher in urban areas, the consequences of crashes are worse in rural regions (see, e.g., Sipulskyte, 2012). The importance of the make and type of the vehicle can be explained by the results of earlier papers in several ways. First, due to the differences in the vehicle prices, the repair and thus the claim costs differ between vehicle types and makes (see Sipulskyte, 2012). Second, several papers show that the vehicle type influences the risk of a fatal accident and thus the severity of a crash (see, e.g., Kim et al., 2006, and Wenzel and Ross, 2005). Finally, as there is a strong relationship between the vehicle type and gender, the vehicle type can capture some of the gender effect (see ABI, 2010). Concerning the annual mileage (B13), the moderate to strong impact can be explained as follows: As already indicated by Bailey and Simon (1960) and confirmed by a series of subsequent papers, the amount of annual claims and the frequency of claims increase with the driven distance (see, e.g., Litman, 2011, and Boucher et al., 2013). However, depending on the method of incorporating the annual mileage there can be a loss in accuracy, risk of manipulation (fraud) or additional costs that reduce the benefits of this tariff criterion (see Abraham, 1985). Currently, most tariffs in the three countries only take the annual mileage as estimated by the customer into account. Moreover, instead of using the exact distance, drivers are classified only in a very small number of broad classes. Thus there is a risk of misstatement by the policyholder (see White, 1976, and Abraham, 1985) and a loss in the predictive power for individual losses. Telematics-based tariffs permit the use of the exact driven distance (see, e.g., Laurie, 2011). Moreover, it is possible to take the driving behavior into account (see, e.g., Laurie, 2011). However, the introduction of these tariffs poses several challenges (e.g., costs for the measurement system, privacy aspects, see e.g., Laurie, 2011, and Reddy, 2012) and in the three considered countries telematics-based contracts are still scarce (see Paefgen et al., 2013a, and Bruneteau et al., 2013). This also explains why the driving behavior, which cannot be measured without these techniques, has a substantial impact on motor insurance tariffs only in some companies, although the variable has a very high predictive power (see, e.g., the study by Progressive, 2012). Finally, the low importance of the garage criterion is in line with the results given in the paper by Störmer and Wagner (2013). Thus the positive effects of a garage on the expected claims (less damages from hail or storm events, less risk of theft) seem to be only small. 11 An analysis of the correlation of the characteristics impact by means of Kendall s tau shows a significant correlation of 22 pairs of characteristics (out of a total of 105 pairs) (see Table A1). Both criteria within one category (e.g., B1 and B3) and criteria of different categories (e.g., B1 and B5) are correlated. Moreover, while the remaining pairs (with significant results) are all positively related, the pairs (B6) and (B9), (B7) and (B9), as well as (B13) and (B9) correlate in a negative way. The negative rank correlation coefficient of the first two pairs can be attributed to the fact that the vehicle type (B6) and date of registration (B7) 11 The existence of a garage only directly affects the claims in comprehensive insurance, not in third-party liability insurance. As our survey does not differentiate between the different insurance types, the results might understate the impact of the garage criterion. 15

17 already imply a certain power-to-weight ratio (B9). This might also explain why in 28 companies of the sample the power-to-weight ratio only has a very small impact on the price although this vehicle characteristic significantly influences the probability of having an accident as well as the survival probability in a crash (see Kim et al., 2006). While there seem to be only minor differences between companies of different sizes, the importance of numerous characteristics varies in the three countries. With respect to the characteristics of the policyholder or most frequent driver, the group of drivers (B3) and place of residence (B4) have greater impact on the tariffs for German companies than for insurance firms in Austria and Switzerland. Moreover, while the date of license acquisition (B2) has a strong or very strong impact in 50% of Swiss motor insurers, this criterion is hardly relevant for the majority of companies in the two EU countries. This criterion s relative importance in Switzerland can be explained by the deductible that Swiss motor insurers in general require from new drivers in the first two years after driving license acquisition. However, despite these differences, the ranking of the five criteria, resulting from ordering the criteria according to their mean, is similar in the three countries. The impact of the vehicle characteristics also varies between the three countries. German motor insurers put considerable weight on the vehicle s type (B6) and registration type (B7) and merely take the other criteria into account. In Swiss companies, (B6) and (B7) also have a strong impact, but they are frequently supplemented by other characteristics such as the power-to-weight ratio (B9) and method of financing (B11). In Austria, only around half of the companies consider (B6) and (B7) as highly relevant. The remainder take these criteria into account only to a limited extent. Compared to Germany, Austrian insurers rely more on the vehicle s power-to-weight-ratio. With respect to vehicle usage information, the type of usage (B12) and annual mileage (B13) play a much more important role in Germany than in Austria and Switzerland. Furthermore, while the existence of a garage has at least a small effect for most German and Swiss companies, in Austria it seems irrelevant. The results are mainly confirmed by the ordered logit regressions. These show significant differences between countries for the criteria (B3), (B4), (B6), (B7), (B9), (B11), (B12), (B13), and (B15). 4.2 Further Pricing Components Pricing Tools The calculation of the actuarial (i.e., cost-based) price is only the first step in the determination of the final premium. In the next step, the price has to be adapted to market conditions, taking the company s strategy into account. Moreover, depending on the customer s willingness to pay and the value of the individual customer for the insurer, a customer-specific discount or surcharge has to be determined. The final price offered to the client is finally determined by the salesperson at the time of the sale of the product (see Erdönmez et al., 2007, and Hartmann et al., 2014). The derivation of the market-based price, the customer-specific discount or surcharge as well as the influence of the sales staff have to be done in a professional, controlled way based on comprehensive analyses. This requires the implementation of tools that support and standardize the decision-making process. 16

18 Not avail. (0) Part. avail. (1) Available (2) Mean St. deviation Size effect Tools for market-based pricing (C1) Systematic analysis of competitors new products (C2) Benchmarking with other insurance providers (C3) Systematic screening of competitors prices Tools for customer-specific pricing (C4) Systematic collection / management of customer data (C5) Systematic process for customer-specific pricing (C6) Strategy based on customer s value *** (C7) Inclusion of customer potential Tools to integrate the sales channel (C8) Influence of the sales channel on the product development (C9) Influence of the sales channel on the price *** (C10) Influence of the sales channel on discount policy Table 4: Availability of Pricing Tools This table shows the availability of different pricing instruments. Columns 1 to 3 give the percentages of companies where the respective tool is not available (0), partially available (1) or available (2). The next two columns contain the mean and standard deviation of the answers when they are coded by 0, 1, and 2, respectively. The last column shows the significance of the size effect according to the ordered logit regressions (the country effect is insignificant for all variables). *** indicates significance at the 1%-level, ** significance at the 5%-level, and * significance at the 10%-level. Table 4 sums up the availability of various tools in the three fields of pricing. The results show that almost all insurers have set up instruments for market-based pricing (C1) to (C3) and to integrate the sales channel (C8) to (C10). However, in the majority of firms, the tools are implemented only partially and only around 30% are able to use them fully. With respect to instruments for customer-specific pricing, only (C4) is widely implemented. The remaining tools (C5) to (C7) exist and are developed to a lesser extent. This especially applies to instruments for pursuing a strategy based on the customer s value (C6) and taking into account the customer s potential (C7), which are unavailable in 43% and 46% of the companies, respectively. Moreover, a calculation of Kendall s tau for each pair of tools indicates that the degree of availability of various instruments within the same category (e.g., C6 and C7 in the market category) is positively correlated (see Table A2). Thus, instruments within a single category are not considered as interchangeable and companies with a higher level of development for one tool frequently also have other tools available within the same category. In contrast, tools of different categories do not exhibit a significant non-zero correlation, implying that firms with many available instruments for market-based pricing have not necessarily developed (fully or partially) tools for customer-specific pricing or systematic integration of the sales channel. Further valuable insights can be gained through country- and size-specific analyses. Differentiating by 17

19 country reveals that in German companies instruments (C5) to (C10) are fully implemented to a considerably lesser extent than in Swiss and Austrian insurance firms. Moreover, tools (C6), (C7), and (C10) are not even fully available in any of the participating companies from Germany. The ordered logit estimates show significant differences only for instruments (C9) and (C10) relating to the integration of the sales channel in price determination. However, in recognition of the low power of the test and the discrepancies shown in the descriptive comparison, we consider it legitimate to conclude that German insurers also have been very slow to integrate the customer s value and future potential. A comparison of the answers of small and large motor insurers illustrates that instrument (C3) for market-based pricing is fully available to a greater extent at small compared to large insurance firms. In contrast, tools for customer-based pricing (C4 to C7) and the integration of the sales channel (C8 to C10) are not only available (partially or full) more often in firms with large motor insurance portfolios, their implementation is also much more likely to have been completed already. According to the results of the ordered logit regressions the differences with respect to the tools for the integration of the customer s value (C6) and the standardization of the sales agents influence on the premium (C9) are statistically significant, as well. Thus, large insurers seem to have committed more to the development of these two steps for premium determination. On the one hand, this could imply that a majority of small companies are still unaware of the high importance of these aspects. On the other hand, the result could stem from the fact that companies with a small motor insurance portfolio had to invest more in improving the calculation of the actuarial price and therefore had limited resources remaining for the other pricing components (see also Hartmann et al., 2014). In summary, the analysis shows that the need for development is highest in the area of customer-specific pricing. In the fields of market-based pricing and the integration of the sales channel, the development process seems to have already started, as adequate tools are at least partially available at almost all companies. However, in order to make full use of these instruments, a large percentage of the firms have to undertake further efforts. Moreover, the need for development seems not to be equally distributed: in the fields of customer-specific pricing and the integration of the sales channel, companies from Germany or with a small portfolio tend to be slower to address these aspects. 12 There are several reasons for these shortcomings in the fields of market-based and customer-specific pricing. First, insurance carriers have only gradually begun to realize the importance of these components in recent years and it will take some time before they get established in companies where actuarial pricing has played a predominant role for decades. Second, they may face problems in obtaining appropriate market and customer data. Third, there may be difficulties in estimating adequate market-induced adjustments and the customer s profitability. These last two points are supported by the results of the survey by Earnix (2011a) among European non-life insurers. Some 40% of this survey s respondents consider the collection and use of competitive data, the collection and use of customer data, and anticipating the customers behavior as the main challenges in pricing. 12 The result of the need for further development in the three areas of pricing is confirmed by the essay of Gard and Eyal (2012), who state that 75% of companies are still focused on cost-based pricing (as indicated to us by one of this study s authors, this percentage is an estimate based on their experience in the P&C markets of Germany, France, Italy, Spain, and the United Kingdom). 18

20 Deviations from the Actuarial Price The integration of the market, customer, and sales channel factors in premium determination causes the final premium to deviate from the standard actuarial price. In order to identify the influence of the different areas of pricing on the final price, the participants of the survey were asked to indicate their maximum possible discounts or surcharges. As can be seen in Table 5, the limits in the areas of marketbased and customer-specific pricing vary considerably between the companies. In contrast, sales agents mostly have quite a large influence, as three-quarters of the insurance firms allow price reductions or addons between 10% and 20% or over 20%, and a further 11% do not provide a fixed limit. These high ranges can be explained by the widespread existence of discount budgets which limit the discounts of the field staff only for their whole contract portfolio but not at the individual level (see Schmidt-Gallas et al., 2010). < 5% 5 10% 10 20% > 20% No fixed limit S (D1) Due to the market situation (D2) Basedonthecustomer spot.value ** (D3) For the sales channel *** Table 5: Maximum Discounts and Surcharges This table shows the distribution of price ranges admitted in the different areas of pricing. The values in the first five columns give the percentages of companies with a maximum possible discount / surcharge of < 5%, 5% 10%, 10% 20%, > 20% or no fixed limit for variations of the price. In the last column (denoted with S) the asterisks show the significance of the size effect in the ordered logit regressions for the first four categories (the country factor is insignificant for D1 to D3). *** indicates significance at the 1%-level, ** significance at the 5%-level, and * significance at the 10%-level. An analysis of the answers at the country level reveals that the substantial discrepancies in the fields of market-based and customer-specific pricing can be attributed in part to differences between the three countries. While the majority of German motor insurers have restricted their market-based price variations (D1) to 10% or lower, Austrian companies mostly have limits in the range of 10% to 20% or even above 20%. On the other hand, half of the insurance firms in Switzerland do not have a fixed boundary at all. With respect to (D2), German companies tend to allow somewhat higher price variations than their competitors in Austria. Moreover, a large proportion of the companies in Switzerland again have no specific limit. With regard to differences between small and large companies, the participants answers show that large motor insurers more frequently admit high price variations or have not introduced a fixed limit. For example, while among the group of large insurance firms 54% have restricted customer-specific discounts and add-ons at some value above 10% (or even 20%), 54% of small companies have a limit of 10% or lower. The existence of size-specific differences is confirmed by the ordered logit regressions (for the companies using fixed limits), which reveal a significant effect of the size factor for (D2) and (D3). This result can be explained in several ways. First, due to the smaller portfolio size they are not able to offset high discounts as easily as large companies (at least if they do not have other lines of large size). Second, as also indicated in the previous section, small motor insurers still attach little importance to the additional pricing components. 19

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