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The purpose of this paper is to demonstrate the value of enriched customer data for analytical customer relationship management (CRM) in the insurance sector. In this study, online quotes from an insurer’s website are evaluated in terms of serving as a trigger event to predict churn, retention, and cross-selling.

For this purpose, the records of online quotes from a Swiss insurer are linked to records of existing customers from 2012 to 2015. Based on the data from automobile and home insurance policyholders, random forest prediction models for classification are fitted.

Enhancing traditional customer data with such additional information substantially boosts the accuracy for predicting future purchases. The models identify customers who have a high probability of adjusting their insurance coverage.

The findings of the study imply that enriching traditional customer data with online quotes yields a valuable approach to predicting purchase behavior. Moreover, the quote data provide supplementary features that contribute to improving prediction performance.

This study highlights the importance of selecting the relevant data sources to target the right customers at the right time and to thus benefit from analytical CRM practices.

This paper is one of the first to investigate the potential value of data-rich environments for insurers and their customers. It provides insights on how to identify relevant customers for ensuing marketing activities efficiently and thus avoiding irrelevant offers. Hence, the study creates value for insurers as well as customers.

Over the last decade, technological and social trends have significantly influenced the relationship between customers and insurers. New buying patterns, price comparison platforms and the usage of different interaction channels driving single-product purchases and impacting lapses have influenced insurers’ customer portfolios and development. The purpose of this paper is to study the features driving the customer relationship along three areas, namely, customer acquisition, development and retention.

After defining 14 related hypotheses, the authors use econometric analyses to quantitatively support these hypotheses in the three areas of interest. The authors build on a large-scale longitudinal data set from a Swiss insurance company covering the period from 2005 to 2014 and including 2,757,000 customer-years. The data comprise information on private customers, their contract history, including coverage and losses and the channels used for buying insurance. This analysis focuses on the two most common non-life insurance products, namely, household/liability and car insurance.

The authors provide descriptive statistics and results from econometric analyses to determine the significant features and patterns affecting customer development and retention. Among the main results, the authors underline the significant influence on cross-selling given by the customer’s age and the interaction channel. Customers from rural regions are more loyal and likely to conduct cross-buying when compared to their peers from urban regions. Car insurance holders are more likely to lapse than household/liability insurance clients. Finally, while newly acquired customers tend to buy only a single product, the authors show the importance of cross-selling for retaining customers. In fact, customer retention is positively influenced by the number of products hold.

This work is relevant for academics and practitioners alike, adding a quantitative basis to the understanding of managing customer relationships and for the development of further prospective models. Further work could investigate or add products, extend the study to other companies and focus on customer development with time.

This study explores a large-scale longitudinal data set. The analyses of customer acquisition, development and retention can support insurers to construct their own models for customer relationship management.

The purpose of this paper is to analyze what transaction costs are acceptable for customers in different investments. In this study, two life insurance contracts, a mutual fund and a risk-free investment, as alternative investment forms are considered. The first two products under scrutiny are a life insurance investment with a point-to-point capital guarantee and a participating contract with an annual interest rate guarantee and participation in the insurer’s surplus. The policyholder assesses the various investment opportunities using different utility measures. For selected types of risk profiles, the utility position and the investor’s preference for the various investments are assessed. Based on this analysis, the authors study which cost levels can make all of the products equally rewarding for the investor.

The paper notes the risk-neutral valuation calibration using empirical data utility and performance measurement dynamics underlying: geometric Brownian motion numerical examples via Monte Carlo simulation.

In the first step, the financial performance of the various saving opportunities under different assumptions of the investor’s utility measurement is studied. In the second step, the authors calculate the level of transaction costs that are allowed in the various products to make all of the investment opportunities equally rewarding from the investor’s point of view. A comparison of these results with transaction costs that are common in the market shows that insurance companies must be careful with respect to the level of transaction costs that they pass on to their customers to provide attractive payoff distributions.

To the best of the authors’ knowledge, their research question – i.e. which transaction costs for life insurance products would be acceptable from the customer’s point of view – has not been studied in the above described context so far.

In view of the fact that claim payouts account for about 70 per cent of annual direct costs in non-life insurance companies and that claims-handling staff sums up to 10-20 per cent of all employees, an optimal claims management environment is of strategic importance. The purpose of this paper is twofold, i.e. on the one hand, the authors introduce a standardized claims management process model and, on the other hand, they apply process benchmarks to various operational parameters.

The proposed claims management process landscape comprises current industry standards for claims handling from a theoretical perspective, supported by practice insights from the industry. Our model aims to reflect the most important claims processing activities. The claims-handling work flow is structured into five core steps, namely, notification, registration, coverage audit, settlement and closing of the claim. For these core steps, the authors differentiate between three claim complexity categories and their associated back-office levels. In the second part of the paper, the authors assess the industry’s claims-handling efficiency. The authors benchmark industry processes with reference to detailed claims management data from 11 insurers in Germany and Switzerland.

The benchmarks are based on the previously defined claims management model and are applied separately to the three retail business lines of car, property and liability insurance. We measure claim process times (cycle times) as well as claim quantities and average claim payouts at different levels. Overall, within each business line, more than 30 data points are gathered from each respondent insurer. This allows us to compare the process performance of different insurance companies and to describe significant differences in their process patterns. Furthermore, principal findings are derived from descriptive statistics as well as ad hoc data analyses.

The paper seeks to contribute to the discussion of how different insurance companies perform in claims management and to define best practice. Our findings are relevant to academics and practitioners alike.

The concept of value at risk is used in the risk-based calculation of solvency capital requirements in the Basel II/III banking regulations and in the planned Solvency II insurance regulation framework planned in the European Union. While this measure controls the ruin probability of a financial institution, the expected policyholder deficit (EPD) and expected shortfall (ES) measures, which are relevant from the customer's perspective as they value the amount of the shortfall, are not controlled at the same time. Hence, if there are variations in or changes to the asset-liability situation, financial companies may still comply with the capital requirement, while the EPD or ES reach unsatisfactory levels. This is a significant drawback to the solvency frameworks. The paper aims to discuss these issues.

The author has developed a model framework wherein the author evaluates the relevant risk measures using the distribution-free approach of the normal power approximation. This allows the author to derive analytical approximations of the risk measures solely through the use of the first three central moments of the underlying distributions. For the case of a reference insurance company, the author calculates the required capital using the ruin probability and EPD approaches. For this, the author performs sensitivity analyses considering different asset allocations and different liability characteristics.

The author concludes that only a simultaneous monitoring of the ruin probability and EPD can lead to satisfactory results guaranteeing a constant level of customer protection. For the reference firm, the author evaluates the relative changes in the capital requirement when applying the EPD approach next to the ruin probability approach. Depending on the development of the assets and liabilities, and in the cases the author illustrates, the reference company would need to provide substantial amounts of additional equity capital.

A comparative assessment of alternative risk measures is relevant given the debate among regulators, industry representatives and academics about how adequately they are used. The author borrows the approach in parts from the work of Barth. Barth compares the ruin probability and EPD approach when discussing the RBC formulas of the US National Association of Insurance Commissioners introduced in the 1990s. The author reconsiders several of these findings and discusses them in the light of the new regulatory frameworks. More precisely, the author first performs sensitivity analyses for the risk measures using different parameter configurations. Such analyses are relevant since in practice parameter values may differ from estimates used in the model and have a significant impact on the values of the risk measures. Second, the author goes beyond a simple discussion of the outcomes for each risk measure, by deriving the firm conclusion that both the frequency and magnitude of shortfalls need to be controlled.

The purpose of this paper is to develop a model to hedge annuity portfolios against increases in life expectancy. Across the globe, and in the industrial nations in particular, people have seen an unprecedented increase in their life expectancy over the past decades. The benefits of this apply to the individual, but the dangers apply to annuity providers. Insurance companies often possess no effective tools to address the longevity risk inherent in their annuity portfolio. Securitization can serve as a substitute for classic reinsurance, as it also transfers risk to third parties.

This paper extends on methods insurer's can use to hedge their annuity portfolio against longevity risk with the help of annuity securitization. Future mortality rates with the Lee-Carter-model and use the Wang-transformation to incorporate insurance risk are forecasted. Based on the percentile tranching method, where individual tranches are aligned to Standard & Poor's ratings, we price an inverse survivor bond. This bond offers fix coupon payments to investors, while the principal payments are at risk and depend on the survival rate within the underlying portfolio.

The contribution to the academic literature is threefold. On the theoretical side, building on the work of Kim and Choi (2011), we adapt their pricing model to the current market situation. Putting the principal at risk instead of the coupon payments, the insurer is supplied with sufficient capital to cover additional costs due to longevity. On the empirical side, the method for the German market is specified. Inserting specific country data into the model, price sensitivities of the presented securitization model are analyzed. Finally, in a case study, the procedure to the annuity portfolio of a large German life insurer is applied and the price of hedging longevity risk is calculated.

To illustrate the implication of this bond structure, several sensitivity tests were conducted before applying the pricing model to the retail sample annuity portfolio from a leading German life insurer. The securitization structure was applied to calculate the securitization prices for a sample portfolio from a large life insurance company.

The findings contribute to the current discussion about how insurers can face longevity risk within their annuity portfolios. The fact that the rating structure has such a severe impact on the overall hedging costs for the insurer implies that companies that are willing to undergo an annuity securitization should consider their deal structure very carefully. In addition, we have pointed out that in imperfect markets, the retention of the equity tranche by the originator might be advantageous. Nevertheless, one has to bear in mind that by this behavior, the insurer is able to reduce the overall default risk in his balance sheet by securitizing a life insurance portfolio; however, the fraction of first loss pieces from defaults increases more than proportionally. The insurer has to take care to not be left with large, unwanted remaining risk positions in his books.

In this paper, we extend on methods insurer's can use to hedge their annuity portfolio against longevity risk with the help of annuity securitization. To do so, we take the perspective of the issuing insurance company and calculate the costs of hedging in a four-step process. On the theoretical side, building on the work of Kim and Choi (2011), we adapt their pricing model to the current market situation. On the empirical side, we specify the method for the German market. Inserting specific country data into the model, price sensitivities of the presented securitization model are analyzed.

The purpose of this paper is to study the risk of misspecifying solvency models for insurance companies.

Based on a basic solvency model, the authors examine the sensitivity of different risk measures with respect to model misspecification. An analysis considers the effects of introducing stochastic jumps and linear, as well as non‐linear dependencies into the basic setting on the solvency capital requirements, shortfall probability and expected policyholder deficit. Additionally, the authors take a regulatory view and consider the degree to which the deviations in risk measures, due to the different model specifications, can be diminished by means of requiring interim financial reports.

The simulation results suggest that the sensitivity of solvency capital as a risk measure – as it is in regulatory practice – underestimates the actual misspecification risk that policyholders are exposed to. It is also found that semi‐annual mandatory interim reports can already reduce the model uncertainty faced by a regulator, significantly. This has important implications for the design of risk‐based capital standards and the implementation of internal solvency models.

The results from the Monte Carlo simulation show that changes in the specification of a solvency model have a much greater impact on shortfall probabilities and expected policyholder deficits than they have on capital requirements. The shortfall risk measures react much more sensitively to small changes in the model assumptions, than the capital requirements. This leads us to the conclusion that regulators should not solely rely on capital requirements to monitor the solvency situation of an insurer, but should additionally consider shortfall risk measures. More precisely, an analysis of model risk focusing on the sensitivity of capital requirements will typically underestimate the relevant risk of model misspecification from a policyholder's perspective. Finally, the simulation results suggest that mandatory interim reports on the solvency and financial situation of an insurance company are a powerful tool in order to reduce the model uncertainty faced by regulators.

The purpose of this paper paper is to study effective measures in dealing with the phenomenon of insurance claims’ fraud. In fact, fraud is one of the major industry concerns. It occurs in all classes of insurance and accounts for a substantial portion of indemnity payments each year.

This paper develops a model framework based on a costly state verification setting in which – while policyholders observe the amount of loss privately – the insurance company can decide to audit incoming claims at some cost. The aim is to derive optimal auditing strategies from the insurance company’s perspective while maintaining contract attractiveness to policyholders willing to adhere to the insurance relationship. The possibility for each stakeholder to adapt its behavioral strategy over the course of several periods is taken into account. Using a numerical approach based on Monte Carlo simulations, the impact of different parameterizations on the optimal auditing range by means of a sensitivity analysis is illustrated and analyzed.

The central outcome of the model is an auditing range which selects those claims which should be subject to verification.

This paper comes to the conclusion that, given some constant cost per audit, it is optimal to verify the accuracy of claims from the mid-value segment. Furthermore, it can be shown that while the option to adapt one’s strategy might be favorable from the insurance company perspective, it has a negative impact on the policyholders’ position. This disproves the common belief that adapting the defrauding strategy with the help of signals from service providers would be advantageous.

This paper extends the stand of literature on costly state verification and gives indications for optimal auditing strategies in industry practice.

The purpose of this study was to find out if colleges of education in Botswana were preparing teachers to meet the diverse needs of prospective students in primary schools, and what were the teachers’ attitudes toward teaching such students.

This study used questionnaire and interview methods to collect information from the students of a college of education in Botswana, who were undertaking teaching practices in various primary schools at the time of their study.

The findings of this study show that the pre-service teachers were aware of multiculturalism in schools, but were not prepared by their colleges of education to teach culturally diverse students.

The implication is that colleges of education should design programs that adequately prepare teachers to better meet the needs of culturally and linguistically diverse students in formal education to achieve true democracy in education.

This study is essential in Botswana because multiculturalism seems to be lacking in the Botswana education system. Mokgosi and Jotia (2013) are concerned that the Botswana curricular lacks diversity in approach and is geared toward addressing the needs of the mainstream in Botswana society. In the authors’ knowledge, this is one of the few (if any) studies that explores pre-service teachers’ preparedness for teaching multicultural students.

Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.