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Loss reserving generally focuses on identifying a single model that can generate superior predictive performance. However, different loss reserving models specialise in capturing different aspects of loss data. This is recognised in practice in the sense that results from different models are often considered, and sometimes combined. For instance, actuaries may take a weighted average of the prediction outcomes from various loss reserving models, often based on subjective assessments. In this paper, we propose a systematic framework to objectively combine (i.e. ensemble) multiple stochastic loss reserving models such that the strengths offered by different models can be utilised effectively. Our framework contains two main innovations compared to existing literature and practice. Firstly, our criteria model combination considers the full distributional properties of the ensemble and not just the central estimate – which is of particular importance in the reserving context. Secondly, our framework is that it is tailored for the features inherent to reserving data. These include, for instance, accident, development, calendar, and claim maturity effects. Crucially, the relative importance and scarcity of data across accident periods renders the problem distinct from the traditional ensembling techniques in statistical learning. Our framework is illustrated with a complex synthetic dataset. In the results, the optimised ensemble outperforms both (i) traditional model selection strategies, and (ii) an equally weighted ensemble. In particular, the improvement occurs not only with central estimates but also relevant quantiles, such as the 75th percentile of reserves (typically of interest to both insurers and regulators). The framework developed in this paper can be implemented thanks to an R package, ADLP, which is available from CRAN. [ABSTRACT FROM AUTHOR]

Deep neural networks have become an important tool for use in actuarial tasks, due to the significant gains in accuracy provided by these techniques compared to traditional methods, but also due to the close connection of these models to the generalized linear models (GLMs) currently used in industry. Although constraining GLM parameters relating to insurance risk factors to be smooth or exhibit monotonicity is trivial, methods to incorporate such constraints into deep neural networks have not yet been developed. This is a barrier for the adoption of neural networks in insurance practice since actuaries often impose these constraints for commercial or statistical reasons. In this work, we present a novel method for enforcing constraints within deep neural network models, and we show how these models can be trained. Moreover, we provide example applications using real-world datasets. We call our proposed method ICEnet to emphasize the close link of our proposal to the individual conditional expectation model interpretability technique. [ABSTRACT FROM AUTHOR]

Building small-population mortality tables has great practical importance in actuarial applications. In recent years, several works in the literature have explored different methodologies to quantify and assess longevity and mortality risk, especially within the context of small populations, and many models dealing with this problem usually use a two-population approach, modeling a mortality spread between a larger reference population and the population of interest, via likelihood-based techniques. To broaden the tools at actuaries' disposal to build small-population mortality tables, a general structure for a two-step two-population model is proposed, its main element of novelty residing in a machine-learning-based approach to mortality spread estimation. In order to obtain this, Contrast Trees and the related Estimation Contrast Boosting techniques have been applied. A quite general machine-learning-based model has then been adapted in order to generalize Italian actuarial practice in company tables estimation and implemented using data from the Human Mortality Database. Finally, results from the ML-based model have been compared to those obtained from the traditional model. [ABSTRACT FROM AUTHOR]

The Society of Actuaries has released some additional reports as part of its ongoing research on generational similarities and differences. Its stated purpose for conducting this ongoing research is “…to understand the similarities and differences across the generations, to learn how younger people may be expected to fare versus those older than them, and to identify areas where further work can be done to find ways to improve the retirement security of Americans.” We take a closer look at this research for additional insights and understanding that financial professionals can put to use on behalf of their clients. [ABSTRACT FROM AUTHOR]

Over the past two years, COVID-19 has had a significant impact on Americans' lives and health, jobs, how organizations adapt to change, and the economy. Climate change has had a growing impact over a number of years, and it has been increasingly recognized as a major societal issue recently. Society of Actuaries research documents household impacts linked to these and other emerging risks. [ABSTRACT FROM AUTHOR]

Copyright of NTU Management Review is the property of NTU Management Review and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In non-life insurance practice, actuaries are often faced with the challenge of predicting the number of claims and claim amounts to be incurred at any given time, which serve to implement fair pricing and reserves given the nature of the risk. This paper extends Jewell's credible distribution in terms of forecasting the distribution of individual risk in cases where the observations are weighted or are grouped in intervals. More specifically, we show how empirical distribution functions can be embedded within Bühlmann's and Straub's credibility model. The optimal projection theorem is applied for credibility estimation and more insight into the derivation of the credibility distribution estimators is also provided. In addition, distribution credibility estimators are established and numerical illustrations are presented herein. Two examples of distribution credibility estimation are given, one with insurance loss data and the other with industry financial data. [ABSTRACT FROM AUTHOR]

Climate change is expected to increase the frequency and intensity of extreme weather events. To properly assess the increased economical risk of these events, actuaries can gain in relying on expert models/opinions from multiple different sources, which requires the use of model combination techniques. From non-parametric to Bayesian approaches, different methods rely on varying assumptions potentially leading to very different results. In this paper, we apply multiple model combination methods to an ensemble of 24 experts in a pooling approach and use the differences in outputs from the different combinations to illustrate how one can gain additional insight from using multiple methods. The densities obtained from pooling in Montreal and Quebec City highlight the significant changes in higher quantiles obtained through different combination approaches. Areal reduction factor and quantile projected changes are used to show that consistency, or lack thereof, across approaches reflects the uncertainty of combination methods. This shows how an actuary using multiple expert models should consider more than one combination method to properly assess the impact of climate change on loss distributions, seeing as a single method can lead to overconfidence in projections. [ABSTRACT FROM AUTHOR]

Actuaries have long been interested in the forecasting of mortality for the purpose of the pricing and reserving of pensions and annuities. Most models of mortality in age and year of death, and often year of birth, are not identifiable so actuaries worried about what constraints should be used to give sensible estimates of the age and year of death parameters, and, if required, the year of birth parameters. These parameters were then forecast with an ARIMA model to give the required forecasts of mortality. A recent article showed that, while the fitted parameters were not identifiable, both the fitted and forecast mortalities were. This result holds if the age term is smoothed with P-splines. The present article deals with generalized linear models with a rank deficient regression matrix. We have two aims. First, we investigate the effect that different constraints have on the estimated regression coefficients. We show that it is possible to fit the model under different constraints in R without imposing any explicit constraints. R does all the necessary booking-keeping 'under the bonnet'. The estimated regression coefficients under a particular set of constraints can then be recovered from the invariant fitted values. We have a black box approach to fitting the model subject to any set of constraints. [ABSTRACT FROM AUTHOR]

This paper examines differences in the wage-offer functions between males and females in the insurance industry. The results of random forest regression (RFR) residual analysis and quantile regressions (QRs) by gender indicate considerable inequities for underwriters, sales agents, and claims adjusters. We find relatively modest wage inequities among actuaries. Underwriters' and adjusters' gender wage inequality lies between the actuaries and sales agents. Across the specifications (RFR, QR, and the OLS benchmark), males benefit more from experience than females except for actuaries. In addition, males generally have a greater return to education than females (except for actuaries). Sales agents' jobs exhibit the greatest inequality, with extremely high values for the regression Gini index of inequality at the upper quantiles. Actuaries exhibit the least amount of gender inequality across the board, with demographic responses suggesting competitive pressures across states yielding the least wage-offer inequality across gender. In summary, taste-based discrimination, social employment networks, difficulties in assessing productivity in heterogeneous work situations, competitiveness in the labor market, and the flexibility of work hours help explain our findings for different occupations in the insurance industry. [ABSTRACT FROM AUTHOR]

This article explores the concept of uncertainty versus risk in avalanche forecasting and decision-making. It emphasizes the distinction between decisions and judgments, with judgments being subjective opinions made in the context of uncertainty. The article discusses the traps and biases that can affect decision-making in avalanche situations and highlights the need for more sophisticated accident investigations. It also explores the difference between complicated and complex systems, with avalanches falling into the complex category due to their unpredictable nature. The author suggests that professionals in the field need to manage uncertainty more effectively and be cautious about relying too heavily on past experiences. [Extracted from the article]

This paper examines the association between monitoring and earnings management by property‐casualty insurers. Prior literature has evaluated the impact of auditors and actuaries on insurer reserving. We extend this work by considering the nonrandom nature of monitor assignment. We model the insurer decisions regarding choice of auditor and actuary jointly using a Heckman selection model. Consistent with prior literature, we account for potential loss reserving incentives that may confound these decisions. We find that the use of internal actuaries is significantly related to higher reserve errors, but this is reduced, but not fully offset, when the internal actuary is an officer of the insurer. We find lower reserve error for auditors from a Big N firm. However, the use of an auditor and actuary from the same Big N firm is significantly related to higher reserve errors. [ABSTRACT FROM AUTHOR]

This paper seeks to explore how professionals in the financial sector understand the challenge that climate change presents to economy and society. It is a case study into how 'climate-related financial risk' is understood in a particular area of expertise – within the actuarial profession. There is an increasingly prominent claim among financial regulators that climate change should be considered as an issue of financial risk and stability; it is argued that this will drive capital towards green ends, and an orderly low carbon transition. Responding to this, actuaries are seeking to establish climate-related risk as part of their professional jurisdiction. Yet they are struggling to do so because of their relationship to the investment chain and because the tools they employ for risk management, mostly drawn from financial economics, are fundamentally failing to consider, quantify and financialise climate risks. Instead, the profession is moving toward scenario-based tools for managing climate-related uncertainty that incorporate narratives about policy interventions and market reaction. The paper draws on interviews and ethnographic research conducted with members of the UK-based Institute and Faculty of Actuaries (IFoA) to explore these established and emerging risk management tools and perspectives. [ABSTRACT FROM AUTHOR]

Formulation of the problem. The paper is a contribution to the discussion of the importance of English proficiency for the actuarial profession in the present-day globalized world. Given the fact that the system of actuarial education in Ukraine is currently at the stage of its formation and hence relies greatly on the international standards set by actuarial organizations, we hypothesize that an English language is an effective tool of professional advancement not only for qualified professionals but also for aspiring actuaries. The study aims to investigate interdependencies between good English skills, the development of actuarial education in Ukraine, and attaining internationally recognized actuarial credentials by Ukrainian actuarial candidates as well as the promotion of the profession of an actuary in the country. Materials and methods. The current research utilizes the qualitative methods of the pedagogical theoretical analysis (curricula, legislative documents, scientific resources) and observations illustrating the practice of actuarial professionals` training in Ukraine and as well as the analysis of international actuarial organizations` internal reports and documents; the authors also employ the synthesis of pedagogical knowledge to draw up some practical recommendations on the improvement of actuarial education in Ukraine. Results. The paper is one of the first comprehensive attempts to analyze the role of English proficiency in aspiring actuaries` professional development in Ukraine and to work out some recommendations on the issue. Based on the analysis of the relevant sources we have identified that English mastery can bring the actuarial profession and education in Ukraine to a qualitatively new level. However, English skills are not considered to be a priority at the level of the national educational norms and regulations. We suggest that possible ways out are improved English instruction, cooperation with international actuarial organizations, and forming psychological readiness in delegates and professionals to speak professional English publicly. Conclusion. The role of English competence for actuarial professionals in Ukraine should be prioritized since the profession itself is regulated by the international standards guiding particular eligibility criteria to be recognized by the word actuarial community as a qualified actuary worldwide. We believe that the proposed recommendations will improve actuarial professional education and the existing language training and will help to bring the profession in general to a qualitatively new level.

The premium principle is a rule of pricing that adjusts premium with insurance risk and is the core of stating actuary insurance. This paper deals with a class of weighted premium principles that uses a positive parameter to add flexibility for an actuary to control the degree of risk aversion, and complements the study of flexibility on the modified variance premium initiated in Goovaerts et al. [9]. We apply our approach for some well-known premium principles, studying some theoretical properties, and we establish the nonmonotonic unimodal premium principles with respect to the loading parameter. Therefore, we obtain a loading maximization of weighted premiums, a useful tool to feasible contracts. We illustrate the results by some numerical examples. [ABSTRACT FROM AUTHOR]

This paper sets out the working party's view that for a defined benefit pension scheme's commutation rate the appropriate starting point should be to set it in line with the scheme's cash equivalent transfer value basis. We recognise that there may be several reasons why an actuary in their advice may deviate from that starting point and we explore these in detail, giving our views on when deviation is and is not justified, noting that many common reasons used such as selection risk are often used without (in our view) adequate justification. We also cover frequency of review -- our view is that commutation rates should be reviewed at least every 3 years and actuaries should consider performing a high-level review of commutation rates annually. We suggest that actuaries should consider proposing market-related commutation rates especially in periods of volatile market conditions. In terms of timing, there are good arguments to review commutation terms either following or during a valuation. Finally, we set out some considerations on how actuaries should present their advice, such as clearly setting out all the information required to take key decisions, following up with any actuarial certification in writing (if necessary) and illustrating the impact on members for changing commutation rates. [ABSTRACT FROM AUTHOR]

Reinsurers possess high volumes of policy listings data from insurers, which they use to provide insurers with analytical insights and modeling that guide reinsurance treaties. These insurers often act on the same data for their own internal modeling and analytics needs. The problem is this data is messy and needs significant preparation in order to extract meaningful insights. Traditionally, this has required intensive manual labor from actuaries. However, a host of modern AI techniques and ML system architectures introduced in the past decade can be applied to the problem of insurance data preparation. In this paper, we explore a novel application of AI/ML on policy listings data that poses its own unique challenges, by outlining the holistic AI-based platform we developed, ECLIPSE (Elegant Cleaning and Labeling of Insurance Policies while Standardizing Entities). With ECLIPSE, actuaries not only save time on data preparation but can build more effective loss models and provide crisper insights. [ABSTRACT FROM AUTHOR]

In actuarial statistics, distributions with heavy tails are of great interest to actuaries, as they represent a better description of risk exposure through a type of indicator with a certain probability. These risk indicators are used to determine companies' exposure to a particular risk. In this paper, we present a distribution with heavy right tail, studying its properties and the behaviour of the tail. We estimate the parameters using the maximum likelihood method and evaluate the performance of these estimators using Monte Carlo. We analyse one set of simulated data and another set of real data, showing that the distribution studied can be used to model income data. [ABSTRACT FROM AUTHOR]

In with-profit life insurance, the prudent valuation of future insurance liabilities leads to systematic surplus that mainly belongs to the policyholders and is redistributed as bonus. For a fair and lawful redistribution of surplus, the insurer needs to decompose the total portfolio surplus with respect to the contributions of individual policies and with respect to different risk sources. For this task, actuaries have a number of heuristic decomposition formulas, but an overarching decomposition principle is still missing. This paper fills that gap by introducing a so-called ISU decomposition principle that bases on infinitesimal sequential updates of the insurer's valuation basis. It is shown that the existing heuristic decomposition formulas can be replicated as ISU decompositions. Furthermore, alternative decomposition principles and their relation to the ISU decomposition principle are discussed. The generality of the ISU concept makes it a useful tool also beyond classical surplus decompositions in life insurance. [ABSTRACT FROM AUTHOR]

Thanks to its outstanding performances, boosting has rapidly gained wide acceptance among actuaries. To speed up calculations, boosting is often applied to gradients of the loss function, not to responses (hence the name gradient boosting). When the model is trained by minimizing Poisson deviance, this amounts to apply the least-squares principle to raw residuals. This exposes gradient boosting to the same problems that lead to replace least-squares with Poisson Generalized Linear Models (GLM) to analyze low counts (typically, the number of reported claims at policy level in personal lines). This paper shows that boosting can be conducted directly on the response under Tweedie loss function and log-link, by adapting the weights at each step. Numerical illustrations demonstrate similar or better performances compared to gradient boosting when trees are used as weak learners, with a higher level of transparency since responses are used instead of gradients. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Statistical Research / İstatistik Araştırma Dergisi is the property of Turkish Statistical Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Modelling and forecasting mortality is a topic of crucial importance to actuaries and demographers. However, forecasts from the majority of mortality projection models are continuations of past trends seen in the data. As such, these models are unable to account for external opinions or expert judgement. In this work, we present a method for the incorporation of deterministic opinions into the smoothing and forecasting of mortality rates using constraints. Not only does our approach yield a smooth transition from the past into the future, but also, the shapes of the resulting forecasts are governed by a combination of the opinion inputs and the speed of improvements observed in the data. In addition, our approach offers the possibility to compute the amount of uncertainty around the projected mortality trends conditional on the opinion inputs, and this allows us to highlight some of the pitfalls of deterministic projection methods. [ABSTRACT FROM AUTHOR]

The 30-day hospital readmission rate is the percentage of patients who are readmitted within 30 days after the last hospital discharge. Hospitals with high readmission rates would have to pay penalties to the Centers for Medicare & Medicaid Services (CMS). Predicting the readmissions can help the hospital better allocate its resources to reduce the readmission rate. In this research, we use a data set from a hospital in North Carolina during the years from 2011 to 2016, including 71724 hospital admissions. We aim to provide a predictive model that can be helpful for related entities including hospitals, health insurance actuaries, and Medicare to reduce the cost and improve the clinical outcome of the healthcare system. We used R to process data and applied clustering, generalized linear model (GLM) and LASSO regressions to predict the 30-day readmissions. It turns out that the patient's age is the most important factor impacting hospital readmission. This research can help hospitals and CMS reduce costly readmissions. [ABSTRACT FROM AUTHOR]