Ein Reservierungsverfahren für die Rechtsschutzversicherung nach Art der Lebensversicherung

In this paper we introduce a new approach to the calculation of claims reserves (known and IBNR cases) which is particularly adapted to the business model of legal expense insurance. An essential aspect here is the split into two model components: case numbers and average claim costs. In contrast to other reserving methods for case numbers and claims cash flows which are frequently used in practice without checking the validity for application we introduce a model in which the time until case settlement is described by a lifetime distribution according to the principles of life insurance. The split of model components also allows for a simple implementation of cost inflation effects which is required by German law. Finally, the approach proposed here can readily be transferred to the calculation of IBNR reserves.     

A Robustification of the Chain-Ladder Method

Abstract

In a non-life insurance business an insurer often needs to build up a reserve to able to meet his or her future obligations arising from incurred but not reported completely claims. To forecast these claims reserves, a simple but generally accepted algorithm is the classical chain-ladder method. Recent research essentially focused on the underlying model for the claims reserves to come to appropriate bounds for the estimates of future claims reserves. Our research concentrates on scenarios with outlying data. On closer examination it is demonstrated that the forecasts for future claims reserves are very dependent on outlying observations. The paper focuses on two approaches to robustify the chain-ladder method: the first method detects and adjusts the outlying values, whereas the second method is based on a robust generalized linear model technique. In this way insurers will be able to find a reserve that is similar to the reserve they would have found if the data contained no outliers. Because the robust method flags the outliers, it is possible to examine these observations for further examination. For obtaining the corresponding standard errors the bootstrapping technique is applied. The robust chain-ladder method is applied to several run-off triangles with and without outliers, showing its excellent performance.     

Reserve risk estimation in a linear model

This paper presents the linear model, a framework for stochastic modeling of known methods as well as for the development of new methods of loss reserving. The linear model allows the determination of optimal predictors of non-observable cumulated and incremental losses and thus the derivation of optimal reserves by minimization of the expected squared prediction error.     

Bootstrapping the chain-ladder method for several correlated run-off portfolios

The prediction of the outstanding loss liabilities for a non-life run-off portfolio as well as the quantification of the prediction error is one of the most important actuarial tasks in non-life insurance. In this paper we consider this prediction problem in a multivariate context. More precisely, we derive the predictive distribution of the claims reserves simultaneously for several correlated run-off portfolios in the framework of the Chain-ladder claims reserving method for several correlated run-off portfolios.     

“The Future of Risk Classification in the Age of Predictive DNA-Based Testing”, Donald C. Chambers,January 1999

Abstract

The correlation among multiple lines of business plays an important role in quantifying the uncertainty of loss reserves for insurance portfolios. To accommodate correlation, most multivariate loss-reserving methods focus on the pairwise association between corresponding cells in multiple run-off triangles. However, such practice usually relies on the independence assumption across accident years and ignores the calendar year effects that could affect all open claims simultaneously and induce dependencies among loss triangles. To address this issue, we study a Bayesian log-normal model in the prediction of outstanding claims for dependent lines of business. In addition to the pairwise correlation, our method allows for an explicit examination of the correlation due to common calendar year effects. Further, different specifications of the calendar year trend are considered to reflect valuation actuaries’ prior knowledge of claim development. In a case study, we analyze an insurance portfolio of personal and commercial auto lines from a major U.S. property-casualty insurer. It is shown that the incorporation of calendar year effects improves model fit significantly, though it contributes substantively to the predictive variability. The availability of the realizations of predicted claims permits us to perform a retrospective test, which suggests that extra prediction uncertainty is indispensable in modern risk management practices.     

Reversible Jump Markov Chain Monte Carlo Method for Parameter Reduction in Claims Reserving

Abstract

We present an application of the reversible jump Markov chain Monte Carlo (RJMCMC) method to the important problem of setting claims reserves in general insurance business for the outstanding loss liabilities. A measure of the uncertainty in these claims reserves estimates is also needed for solvency purposes. The RJMCMC method described in this paper represents an improvement over the manual processes often employed in practice. In particular, our RJMCMC method describes parameter reduction and tail factor estimation in the claims reserving process, and, moreover, it provides the full predictive distribution of the outstanding loss liabilities.     

Non-parametric and parametric bootstrap techniques for age-to-age development factor methods in stochastic claims reserving

In the literature, one of the main objects of stochastic claims reserving is to find models underlying the chain-ladder method in order to analyze the variability of the outstanding claims, either analytically or by bootstrapping. In bootstrapping these models are used to find a full predictive distribution of the claims reserve, even though there is a long tradition of actuaries calculating the reserve estimate according to more complex algorithms than the chain-ladder, without explicit reference to an underlying model. In this paper we investigate existing bootstrap techniques and suggest two alternative bootstrap procedures, one non-parametric and one parametric, by which the predictive distribution of the claims reserve can be found for other age-to-age development factor methods than the chain-ladder, using some rather mild model assumptions. For illustration, the procedures are applied to three different development triangles.     

Das stochastische Re-Reserving

Next to premium risk, reserve risk is an essential part of underwriting risk for property and casualty insurers. Up to now, the literature on non-life reserve risk for the most part only considered an ultimative point of view, which means that the reserve risk uncertainty would be quantified up to final settlement. In the context of the new Solvency II project, as well as against the background of internal models in a several year context, for now an intensified discussion about a one-year horizon has occurred. Thereby, the reserve risk uncertainty would only be quantified up to a one-year claims development result. In this paper we would like to present a simulation method, called stochastic Re-Reserving for modelling the reserve risk on a yearly basis. In addition to that, we would like to make a paradigmatic comparison between stochastic Re-Reserving and an analytical approach, called claims development result method (see Merz and Wuethrich, Modelling the claims development result for solvency purposes. In: CAS E-Forum, pp. 542–568, 2008).     

Stochastic loss reserving: A new perspective from a Dirichlet model

Forecasting the outstanding claim liabilities to set adequate reserves is critical for a nonlife insurer's solvency. Chain–Ladder and Bornhuetter–Ferguson are two prominent actuarial approaches used for this task. The selection between the two approaches is often ad hoc due to different underlying assumptions. We introduce a Dirichlet model that provides a common statistical framework for the two approaches, with some appealing properties. Depending on the type of information available, the model inference naturally leads to either Chain–Ladder or Bornhuetter–Ferguson prediction. Using claims data on Worker's compensation insurance from several U.S. insurers, we discuss both frequentist and Bayesian inference.     

A User-Friendly Introduction to Property-Casualty Claim Reserves

Property-casualty (P&C) claim reserves are financial statement liabilities that represent estimated future claims. An understanding of P&C reserves is important to both risk managers and investment analysts, since this information is crucial in assessing solvency and value. We review some basics of P&C reserves, and add some of our own thoughts, in a way that tries to be user-friendly for practitioners and students.     

Sarmanov Family of Bivariate Distributions for Multivariate Loss Reserving Analysis

The correlation among multiple lines of business plays a critical role in aggregating claims and thus determining loss reserves for an insurance portfolio. We show that the Sarmanov family of bivariate distributions is a convenient choice to capture the dependencies introduced by various sources, including the common calendar year, accident year, and development period effects. The density of the bivariate Sarmanov distributions with different marginals can be expressed as a linear combination of products of independent marginal densities. This pseudo-conjugate property greatly reduces the complexity of posterior computations. In a case study, we analyze an insurance portfolio of personal and commercial auto lines from a major U.S. property-casualty insurer.     

Die Berücksichtigung klimatisch bedingter Veränderungen der Sturmaktivität in den Entscheidungs-prozessen deutscher Erstversicherer

Climate Change will lead to an increased number and intensity of severe winterstorms in Northwest-Europe and Germany. Along with this development comes a rise in potential claims; insurers are particularly affected in the storm and tempest insurance, the homeowner’s and householder’s comprehensive insurance and the motor hull insurance. Although, the predictions of storm changes refer to the next 50 or 100 years a consideration of the expected changes in today’s decissions is recommended. Several measures are already helpful in reaching the insurers’ goals and should therefore be realised soon.     

Selection Bias and Auditing Policies for Insurance Claims

Selection bias results from a discrepancy between the range of estimation of a statistical model and its range of application. This is the case for fraud risk models, which are estimated on audited claims but applied on incoming claims in the design of auditing strategies. Now audited claims are a minority within the parent sample since they are chosen after a severe selection performed by claims adjusters. This article presents a statistical approach that counteracts selection bias without using a random auditing strategy. A two‐equation model on audit and fraud (a bivariate probit model with censoring) is estimated on a sample of claims where the experts are left free to take the audit decision. The expected overestimation of fraud risk derived from a single‐equation model is corrected. Results are close to those obtained with a random auditing strategy, at the expense of some instability with respect to the regression components set. Then we compare auditing policies derived from the different approaches.     

Long-Term Disability Claims Rates and the Consumption-to-Wealth Ratio

A framework for linking long-term disability (LTD) claims rates to the macro-economy using the consumption-to-wealth ratio is developed from financial economic and option theories. Financial economic theory suggests that the consumption-to-wealth ratio reflects consumption smoothing and reveals expectations about future wealth. For individuals contemplating submitting an LTD claim, the expected payoff to exercising this insurance option is a function of their expectations about their future wealth. The lower (higher) their expectations about future wealth, the higher (lower) the expected payoff, and the higher (lower) claims rates are likely to be. Using cointegration analysis, we find that LTD claims rates and the consumption-to-wealth ratio are linked in a long-run equilibrium. When the consumption-to-wealth ratio is high (low), LTD claims rates are low (high).     

Asset pricing theory for two price economies

We show that nonlinearly discounted nonlinear martingales are related to no arbitrage in two price economies as linearly discounted martingales were related to no arbitrage in economies satisfying the law of one price. Furthermore, assuming risk acceptability requires a positive physical expectation, we demonstrate that expected rates of return on ask prices should be dominated by expected rates of return on bid prices. A preliminary investigation conducted here, supports this hypothesis. In general we observe that asset pricing theory in two price economies leads to asset pricing inequalities. A model incorporating both nonlinear discounting and nonlinear martingales is developed for the valuation of contingent claims in two price economies. Examples illustrate the interactions present between the severity of measure changes and their associated discount rates. As a consequence arbitrage free two price economies can involve unique discount curves and measure changes that are however specific to both the product being priced and the trade direction. Furthermore the developed valuation operators call into question the current practice of Debt Valuation Adjustments.     

Ruin probabilities in classical risk models with gamma claims

In this paper, we provide three equivalent expressions for ruin probabilities in a Cramér–Lundberg model with gamma distributed claims. The results are solutions of integro-differential equations, derived by means of (inverse) Laplace transforms. All the three formulas have infinite series forms, two involving Mittag–Leffler functions and the third one involving moments of the claims distribution. This last result applies to any other claim size distributions that exhibits finite moments.     

Robust bootstrap procedures for the chain-ladder method

Insurers are faced with the challenge of estimating the future reserves needed to handle historic and outstanding claims that are not fully settled. A well-known and widely used technique is the chain-ladder method, which is a deterministic algorithm. To include a stochastic component one may apply generalized linear models to the run-off triangles based on past claims data. Analytical expressions for the standard deviation of the resulting reserve estimates are typically difficult to derive. A popular alternative approach to obtain inference is to use the bootstrap technique. However, the standard procedures are very sensitive to the possible presence of outliers. These atypical observations, deviating from the pattern of the majority of the data, may both inflate or deflate traditional reserve estimates and corresponding inference such as their standard errors. Even when paired with a robust chain-ladder method, classical bootstrap inference may break down. Therefore, we discuss and implement several robust bootstrap procedures in the claims reserving framework and we investigate and compare their performance on both simulated and real data. We also illustrate their use for obtaining the distribution of one year risk measures.     

Separation of small and large claims on the basis of collective models

This paper is inspired by two papers of Riegel who proposed to consider the paid and incurred loss development of the individual claims and to use a filter in order to separate small and large claims and to construct loss development squares for the paid or incurred small or large claims and for the numbers of large claims. We show that such loss development squares can be constructed from collective models for the accident years. Moreover, under certain assumptions on these collective models, we show that a development pattern exists for each of these loss development squares, which implies that various methods of loss reserving can be used for prediction and that the chain ladder method is a natural method for the prediction of future numbers of large claims.     

Belgian Intragroup Relations and the Determinants of Corporate Liquid Reserves

The determinants of liquid reserves are investigated for a sample of 1038 large Belgian non‐financial firms in the 1992–94 period. The results confirm the hypothesis that the terms of payment of intragroup claims can be adjusted to the firm's liquidity needs, thereby reducing the need for liquid reserves. Furthermore, the results confirm the transaction motive for holding liquid reserves, but only partially confirm the precautionary motive. Finally, the results indicate that liquid reserves play a significant role in the financing of new investments, as predicted by the pecking order model of Myers and Majluf (1984).     

Prediction Error of the Multivariate Chain Ladder Reserving Method

Abstract

In this paper we consider the claims reserving problem in a multivariate context: that is, we study the multivariate chain-ladder (CL) method for a portfolio of N correlated runoff triangles based on multivariate age-to-age factors. This method allows for a simultaneous study of individual runoff subportfolios and facilitates the derivation of an estimator for the mean square error of prediction (MSEP) for the CL predictor of the ultimate claim of the total portfolio. However, unlike the already existing approaches we replace the univariate CL predictors with multivariate ones. These multivariate CL predictors reflect the correlation structure between the subportfolios and are optimal in terms of a classical optimality criterion, which leads to an improvement of the estimator for the MSEP. Moreover, all formulas are easy to implement on a spreadsheet because they are in matrix notation. We illustrate the results by means of an example.