Annals of Actuarial Science最新文献

{"title":"Empirical tests for ex post moral hazard in a market for automobile insurance","authors":"D. Rowell, S. Nghiem, L. Connelly","doi":"10.1017/s1748499521000191","DOIUrl":"https://doi.org/10.1017/s1748499521000191","url":null,"abstract":"Abstract Ex post moral hazard arises when the insured has an unobservable influence on the size of a loss after its occurrence. In automobile (property) insurance, ex post moral hazard could increase in the scope of the repairs and/or the value of the repairs. Both vehicle owners and auto repairers could gain from increasing the scope of repairs, while auto repairers would gain from an increase in the value of repairs. An analysis of 994 Australian road traffic crashes found that ex post moral hazard increased the value of repairs by 46.8 per cent of which 9 percentage points was explained by an increase in the scope of the repairs, which was defined as an increased from 2 to 2.4 parts per auto repair.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42736385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multidisciplinary collaboration on discrimination – not just “Nice to Have”","authors":"C. Dolman, Edward (Jed) Frees, Fei Huang","doi":"10.1017/S174849952100021X","DOIUrl":"https://doi.org/10.1017/S174849952100021X","url":null,"abstract":"Although much of the discipline of actuarial science has its roots in isolated mathematicians or small collaborative teams toiling to produce fundamental truths, practice today is frequently geared towards large collaborative teams. In some cases, these teams can cross academic disciplines. In our view, whilst certain matters can be effectively researched within isolated disciplines, others are more suited to multidisciplinary teamwork. Discrimination, particularly data-driven discrimination, is an extremely rich and broad topic. Here, we mainly focus on insurance discrimination in underwriting/pricing, and we use the word “discrimination” in an entirely neutral way, taking it to mean the act of treating distinct groups differently – whether or not such discrimination can be justified based on legal, economic or ethical grounds. Whilst narrow research into this subject is certainly possible, a broad perspective is likely to be beneficial in creating robust, well-considered solutions to actual or perceived problems. Significant harms can and, indeed, have been caused by well-intended but narrowly framed solutions to large, difficult problems. In discrimination, for example, the intuitively appealing “fairness through unawareness” is known to make overall discrimination worse in some circumstances (for a worked example, see Reid & O’Callaghan 2018). Whilst the unawareness problem has been understood in the computer science community for some time (see, e.g. Pedreschi et al. 2008), it is an idea still embedded in many laws around the world, and too frequently seen by some as a solution for data-driven discrimination. As with other institutions, insurers are redefining the way that they do business with the increasing capacity and computational abilities of computers, availability of new and innovative sources of data, and advanced artificial intelligence algorithms that can detect patterns in data that were previously unknown. Conceptually, Big Data and new technologies do not alter the fundamental issues of insurance discrimination; one can think of credit-based insurance scoring and price optimization as simply forerunners of this movement. Yet, old challenges may becomemore prominent in this rapidly developing landscape. Issues regarding privacy and the use of algorithmic proxies take on increased importance as insurers’ extensive use of data and computational abilities evolve. Actuaries need to be attuned to these issues and, ideally, involved in proposals to address them. For example, Frees & Huang (2021) draw upon historical, economic, legal, and computer science literatures to understand insurance discrimination. In particular, they review social and economic principles that can be used to assess whether insurance discrimination is ethical or is “unfair” and morally indefensible in some sense, examine insurance regulations and laws across different lines of business and jurisdictions, and explore the machine learning literature on mitigating ","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46755661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Package AdvEMDpy: Algorithmic variations of empirical mode decomposition in Python","authors":"Cole van Jaarsveldt, M. Ames, Gareth W. Peters, M. Chantler","doi":"10.2139/ssrn.3947132","DOIUrl":"https://doi.org/10.2139/ssrn.3947132","url":null,"abstract":"Abstract This work presents a \u0000$textsf{Python}$\u0000 EMD package named AdvEMDpy that is both more flexible and generalises existing empirical mode decomposition (EMD) packages in \u0000$textsf{Python}$\u0000 , \u0000$textsf{R}$\u0000 , and \u0000$textsf{MATLAB}$\u0000 . It is aimed specifically for use by the insurance and financial risk communities, for applications such as return modelling, claims modelling, and life insurance applications with a particular focus on mortality modelling. AdvEMDpy both expands upon the EMD options and methods available, and improves their statistical robustness and efficiency, providing a robust, usable, and reliable toolbox. Unlike many EMD packages, AdvEMDpy allows customisation by the user, to ensure that a broader class of linear, non-linear, and non-stationary time series analyses can be performed. The intrinsic mode functions (IMFs) extracted using EMD contain complex multi-frequency structures which warrant maximum algorithmic customisation for effective analysis. A major contribution of this package is the intensive treatment of the EMD edge effect which is the most ubiquitous problem in EMD and time series analysis. Various EMD techniques, of varying intricacy from numerous works, have been developed, refined, and, for the first time, compiled in AdvEMDpy. In addition to the EMD edge effect, numerous pre-processing, post-processing, detrended fluctuation analysis (localised trend estimation) techniques, stopping criteria, spline methods, discrete-time Hilbert transforms (DTHT), knot point optimisations, and other algorithmic variations have been incorporated and presented to the users of AdvEMDpy. This paper and the supplementary materials provide several real-world actuarial applications of this package for the user’s benefit.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86455179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pricing insurance policies with offsetting relationship","authors":"Hamza Hanbali","doi":"10.1017/S1748499521000208","DOIUrl":"https://doi.org/10.1017/S1748499521000208","url":null,"abstract":"Abstract This paper investigates the benefits of incorporating diversification effects into the pricing process of insurance policies from two different business lines. The paper shows that, for the same risk reduction, insurers pricing policies jointly can have a competitive advantage over those pricing them separately. However, the choice of competitiveness constrains the underwriting flexibility of joint pricers. The paper goes a step further by modelling explicitly the relationship between premiums and the number of customers in each line. Using the total collected premiums as a criterion to compare the competing strategies, the paper provides conditions for the optimal pricing decision based on policyholders’ sensitivity to price discounts. The results are illustrated for a portfolio of annuities and assurances. Further, using non-life data from the Brazilian insurance market, an empirical exploration shows that most pairs satisfy the condition for being priced jointly, even when pairwise correlations are high.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57010062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SPLICE: a synthetic paid loss and incurred cost experience simulator","authors":"Benjamin Avanzi, G. Taylor, Melantha Wang","doi":"10.1017/S1748499522000057","DOIUrl":"https://doi.org/10.1017/S1748499522000057","url":null,"abstract":"Abstract In this paper, we first introduce a simulator of cases estimates of incurred losses called SPLICE (Synthetic Paid Loss and Incurred Cost Experience). In three modules, case estimates are simulated in continuous time, and a record is output for each individual claim. Revisions for the case estimates are also simulated as a sequence over the lifetime of the claim in a number of different situations. Furthermore, some dependencies in relation to case estimates of incurred losses are incorporated, particularly recognising certain properties of case estimates that are found in practice. For example, the magnitude of revisions depends on ultimate claim size, as does the distribution of the revisions over time. Some of these revisions occur in response to occurrence of claim payments, and so SPLICE requires input of simulated per-claim payment histories. The claim data can be summarised by accident and payment “periods” whose duration is an arbitrary choice (e.g. month, quarter, etc.) available to the user. SPLICE is built on an existing simulator of individual claim experience called SynthETIC (introduced in Avanzi et al. 2021b, Insurance: Mathematics and Economics, 100, 296–308), which offers flexible modelling of occurrence, notification, as well as the timing and magnitude of individual partial payments. This is in contrast with the incurred losses, which constitute the additional contribution of SPLICE. The inclusion of incurred loss estimates provides a facility that almost no other simulators do. SPLICE is is a fully documented R package that is publicly available and open source (on CRAN). SPLICE, combined with SynthETIC, provides 11 modules (occurrence, notification, etc.), any one or more of which may be re-designed according to the user’s requirements. It comes with a default version that is loosely calibrated to resemble a specific (but anonymous) Auto Bodily Injury portfolio, as well as data generation functionality that outputs alternative data sets under a range of hypothetical scenarios differing in complexity. The general structure is suitable for most lines of business, with some reparameterisation.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43723090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pseudo-model-free hedging for variable annuities via deep reinforcement learning","authors":"W. F. Chong, Haoen Cui, Yuxuan Li","doi":"10.1017/s1748499523000027","DOIUrl":"https://doi.org/10.1017/s1748499523000027","url":null,"abstract":"This paper proposes a two-phase deep reinforcement learning approach, for hedging variable annuity contracts with both GMMB and GMDB riders, which can address model miscalibration in Black-Scholes financial and constant force of mortality actuarial market environments. In the training phase, an infant reinforcement learning agent interacts with a pre-designed training environment, collects sequential anchor-hedging reward signals, and gradually learns how to hedge the contracts. As expected, after a sufficient number of training steps, the trained reinforcement learning agent hedges, in the training environment, equally well as the correct Delta while outperforms misspecified Deltas. In the online learning phase, the trained reinforcement learning agent interacts with the market environment in real time, collects single terminal reward signals, and self-revises its hedging strategy. The hedging performance of the further trained reinforcement learning agent is demonstrated via an illustrative example on a rolling basis to reveal the self-revision capability on the hedging strategy by online learning.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47461700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial","authors":"Katrien Antonio, Christophe Dutang, A. Tsanakas","doi":"10.1017/S174849952100018X","DOIUrl":"https://doi.org/10.1017/S174849952100018X","url":null,"abstract":"The proceedings contains 344 papers. The topics discussed include: Chronic coronary syndrome patients with polyarterial disease are a high risk but heterogenous subset of patients. Insights from the CLARIFY registry;Additional prognostic value of vasodilator stress cardiovascular magnetic resonance in patients with inconclusive stress test to detect coronary artery disease;Applicability of the REDUCE-IT trial to the FAST-MI registry. Are the results of randomized trials relevant in routine clinical practice?;Medical therapy at discharge in patients admitted for acute coronary syndrome: Data of the French MONICA population registers;Post-infectious myocardial Infarction: Does percutaneous coronary intervention improve outcomes? A propensity-score matched analysis;Prevalence and prognosis of iron deficiency in acute myocardial infarction;Myocardial infarction rates overview during COVID-19 pandemic In France: Results of the MODIF registry;Short-term air pollution concentration variations and ST-elevation myocardial infarction: A case-crossover study from the SCALIM registry;Predictive value of early cardiac mri functional and geometric indexes on adverse left ventricular remodelling in anterior STEMI patients. A report from the CIRCUS study;High levels of lipoprotein(a) are associated with the severity of coronary disease in patients with acute myocardial infarction. Data from the RICO survey;More than 30% of symptomatic patients at one year in “infarct like” acute myocarditis;Impact of use stent with a polyethylene terephthalate micro-net covering on coronary microvascular dysfunction in patients with acute myocardial infarction;Impact of Covid-19 infection in high-risk coronary patients;Coronary artery lesions in Kawasaki Disease;Activity of a catheterization laboratory in Tunisia: A comparative study before and during confinement (COVID-19);Acute coronary syndromes in the era of SARS-CoV-2 Infection: A registry of the French group of acute cardiac care","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45608321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conditional mean risk sharing in the individual model with graphical dependencies","authors":"M. Denuit, C. Robert","doi":"10.1017/S1748499521000166","DOIUrl":"https://doi.org/10.1017/S1748499521000166","url":null,"abstract":"Abstract Conditional mean risk sharing appears to be effective to distribute total losses amongst participants within an insurance pool. This paper develops analytical results for this allocation rule in the individual risk model with dependence induced by the respective position within a graph. Precisely, losses are modelled by zero-augmented random variables whose joint occurrence distribution and individual claim amount distributions are based on network structures and can be characterised by graphical models. The Ising model is adopted for occurrences and loss amounts obey decomposable graphical models that are specific to each participant. Two graphical structures are thus used: the first one to describe the contagion amongst member units within the insurance pool and the second one to model the spread of losses inside each participating unit. The proposed individual risk model is typically useful for modelling operational risks, catastrophic risks or cybersecurity risks.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/S1748499521000166","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48007018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of the choice of risk assessment time horizons on defined benefit pension schemes","authors":"Doug Andrews, S. Bonnar, L. Curtis, Jaideep S. Oberoi, Aniketh Pittea, Pradip Tapadar","doi":"10.1017/S1748499521000178","DOIUrl":"https://doi.org/10.1017/S1748499521000178","url":null,"abstract":"Abstract We examine the impact of asset allocation and contribution rates on the risk of defined benefit (DB) pension schemes, using both a run-off and a shorter 3-year time horizon. Using the 3-year horizon, which is typically preferred by regulators, a high bond allocation reduces the spread of the distribution of surplus. However, this result is reversed when examined on a run-off basis. Furthermore, under both the 3-year horizon and the run-off, the higher bond allocation reduces the median level of surplus. Pressure on the affordability of DB schemes has led to widespread implementation of the so-called de-risking strategies, such as moving away from predominantly equity investments to greater bond investments. If the incentives produced by shorter term risk assessments are contributing to this shift, they might be harming the long-term financial health of the schemes. Contribution rates have relatively lower impact on the risk.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/S1748499521000178","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49433364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A robust random coefficient regression representation of the chain-ladder method","authors":"Ioannis Badounas, Apostolos Bozikas, G. Pitselis","doi":"10.1017/S1748499521000154","DOIUrl":"https://doi.org/10.1017/S1748499521000154","url":null,"abstract":"Abstract It is well known that the presence of outliers can mis-estimate (underestimate or overestimate) the overall reserve in the chain-ladder method, when we consider a linear regression model, based on the assumption that the coefficients are fixed and identical from one observation to another. By relaxing the usual regression assumptions and applying a regression with randomly varying coefficients, we have a similar phenomenon, i.e., mis-estimation of the overall reserves. The lack of robustness of loss reserving regression with random coefficients on incremental payment estimators leads to the development of this paper, aiming to apply robust statistical procedures to the loss reserving estimation when regression coefficients are random. Numerical results of the proposed method are illustrated and compared with the results that were obtained by linear regression with fixed coefficients.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/S1748499521000154","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45088917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}