Browsing by Subject "insurance"

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  • Russo, Fabrizio; De Salvatore, Sergio; Ambrosio, Luca; Vadalà, Gianluca; Fontana, Luca; Papalia, Rocco; Rantanen, Jorma; Iavicoli, Sergio; Denaro, Vincenzo (2021)
    Low back pain (LBP) is currently the leading cause of disability worldwide and the mostcommon reason for workers’ compensation (WC) claims. Studies have demonstrated that receivingWC is associated with a negative prognosis following treatment for a vast range of health conditions.However, the impact of WC on outcomes after spine surgery is still controversial. The aim of thismeta-analysis was to systematically review the literature and analyze the impact of compensationstatus on outcomes after lumbar spine surgery. A systematic search was performed on Medline,Scopus, CINAHL, EMBASE and CENTRAL databases. The review included studies of patientsundergoing lumbar spine surgery in which compensation status was reported. Methodologicalquality was assessed through ROBINS-I and quality of evidence was estimated using the GRADErating. A total of 26 studies with a total of 2668 patients were included in the analysis. WC patientshad higher post-operative pain and disability, as well as lower satisfaction after surgery whencompared to those without WC. Furthermore, WC patients demonstrated to have a delayed return towork. According to our results, compensation status is associated with poor outcomes after lumbarspine surgery. Contextualizing post-operative outcomes in clinical and work-related domains helpsunderstand the multifactorial nature of the phenomenon.
  • Sahlberg, Eero (Helsingin yliopisto, 2018)
    This thesis examines underlying causes of customer churn in the Finnish insurance market. Using individual data on moving insurance customers, econometric modeling is conducted to find significant relations between observed customer characteristics and behavior, and the probability to churn. A subscription-based business gains revenue not only from new sales but more importantly from automatic renewals of existing customers, i.e. retention. Significant drops in retention are important to understand for the insurer in order to not lose profit. Churn is an antonym for retention. A change of address – or moving homes – is an event around which churn rates spike, as it is a time when all address-specific subscriptions (electricity, internet, etc.) need to be proactively renewed by the consumer. There were one million moving individuals in 2016, as reported by Posti. This means that a significant share of an insurer’s customers are at a heightened risk to churn, with an address change being the common denominator. This thesis asks which customer characteristics and experiences significantly either increase or decrease the probability of a customer either changing their home insurance or churning completely around the time of their move. Insurance literature such as Hillson & Murray-Webster (2007) and Vaughan (1996) are reviewed to present the nature of risk, the insurance mechanism and the modern insurance business model. An annual report by Finance Finland (2017) provides accounting data via which the Finnish market situation is presented, while data and reports by Posti (2016; 2017a; 2017b) provide the numbers and facts regarding Finnish movers. Churn modeling is based on 20th century discrete choice theory, literature of which is reviewed, most notably by Nobel-laureate Daniel McFadden (1974; 2000). Also presented are modern applications of choice theory into churn problems, such as Madden et al (1999). The empirical section of the thesis consists of data presentation, model construction and evaluation and finally discussion of the results. The final sample of customer data consists of 24 230 observations with 21 variables. Following Madden et al (1999) and with help from Cox (1958) and McFadden (1974), binomial logistic regression models are constructed to relate the probability of churning with the specified variables. It is found that customer data can be used to predict churn among movers. Significant weights are found for variables denoting the size of a customer’s insurance portfolio as well as customer age and the duration of customership. Also the presence of personal insurance products and contact with one’s insurer notably affect retention positively. Younger segments and customers with implications of lower income (with fewer insurance products, more payment installments) exhibit a significantly increased probability of churning.
  • Bernardo, Alexandre (Helsingin yliopisto, 2020)
    In insurance and reinsurance, heavy-tail analysis is used to model insurance claim sizes and frequencies in order to quantify the risk to the insurance company and to set appropriate premium rates. One of the reasons for this application comes from the fact that excess claims covered by reinsurance companies are very large, and so a natural field for heavy-tail analysis. In finance, the multivariate returns process often exhibits heavy-tail marginal distributions with little or no correlation between the components of the random vector (even though it is a highly correlated process when taking the square or the absolute values of the returns). The fact that vectors which are considered independent by conventional standards may still exhibit dependence of large realizations leads to the use of techniques from classical extreme-value theory, that contains heavy-tail analysis, in estimating an extreme quantile of the profit-and-loss density called value-at-risk (VaR). The need of the industry to understand the dependence between random vectors for very large values, as exemplified above, makes the concept of multivariate regular variation a current topic of great interest. This thesis discusses multivariate regular variation, showing that, by having multiple equivalent characterizations and and by being quite easy to handle, it is an excellent tool to address the real-world issues raised previously. The thesis is structured as follows. At first, some mathematical background is covered: the notions of regular variation of a tail distribution in one dimension is introduced, as well as different concepts of convergence of probability measures, namely vague convergence and $\mathbb{M}^*$-convergence. The preference in using the latter over the former is briefly discussed. The thesis then proceeds to the main definition of this work, that of multivariate regular variation, which involves a limit measure and a scaling function. It is shown that multivariate regular variation can be expressed in polar coordinates, by replacing the limit measure with a product of a one-dimensional measure with a tail index and a spectral measure. Looking for a second source of regular variation leads to the concept of hidden regular variation, to which a new hidden limit measure is associated. Estimation of the tail index, the spectral measure and the support of the limit measure are next considered. Some examples of risk vectors are next analyzed, such as risk vectors with independent components and risk vectors with repeated components. The support estimator presented earlier is then computed in some examples with simulated data to display its efficiency. However, when the estimator is computed with real-life data (the value of stocks for different companies), it does not seem to suit the sample in an adequate way. The conclusion is drawn that, although the mathematical background for the theory is quite solid, more research needs to be done when applying it to real-life data, namely having a reliable way to check whether the data stems from a multivariate regular distribution, as well as identifying the support of the limit measure.