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This report updates an earlier study in which the authors estimated the effects of a choice automobile insurance plan on the costs of compensating auto accident victims in which the no-fault option was absolute no-fault (ANF). The authors assumed that 50 percent of the consumers who would have purchased auto insurance under their state's current system would switch to ANF under the choice plan. That study, requested by the Joint Economic Committee of the U.S. Senate and using data from 1987, estimated how a variant of that plan would affect the cost of private passenger auto insurance if all currently insured drivers elected the no-fault option. The present report uses recently obtained data for a representative sample of people who were compensated for auto accident injuries in 1992. With these data, the authors have replicated their analyses for 46 states. They find that the choice plan could substantially reduce the costs of compensating people injured in auto accidents.

Automobile insurance --- No-fault automobile insurance --- Costs.

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This report updates an earlier study in which the authors estimated the effects of a choice automobile insurance plan on the costs of compensating auto accident victims in which the no-fault option was absolute no-fault (ANF). The authors assumed that 50 percent of the consumers who would have purchased auto insurance under their state's current system would switch to ANF under the choice plan. That study, requested by the Joint Economic Committee of the U.S. Senate and using data from 1987, estimated how a variant of that plan would affect the cost of private passenger auto insurance if all currently insured drivers elected the no-fault option. The present report uses recently obtained data for a representative sample of people who were compensated for auto accident injuries in 1992. With these data, the authors have replicated their analyses for 46 states. They find that the choice plan could substantially reduce the costs of compensating people injured in auto accidents.

Automobile insurance --- States.

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This work examines the compensation that automobile insurers paid to accident victims in California during a period, 1979 to 1988, when such punitive damages claims were permitted.

Automobile insurance. --- Insurance crimes. --- Insurance, Automobile - California. --- Automobile insurance --- Insurance fraud

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No-fault regimes, a formerly popular alternative to the tort compensation system for auto-accident victims, have gradually lost support. Over time, premiums and claim costs have grown in no-fault states relative to other states, primarily driven by explosive medical cost increases. No-fault and tort states have also converged across many domains affecting costs, including excess claiming, litigation patterns, and noneconomic-damage payments.

Liability for traffic accidents -- United States. --- No-fault automobile insurance -- Law and legislation -- United States. --- No-fault automobile insurance -- United States. --- No-fault automobile insurance --- Liability for traffic accidents --- Law - U.S. - General --- Law - U.S. --- Law, Politics & Government --- Law and legislation --- Automobile insurance, No-fault --- Insurance, No-fault automobile --- Insurance

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Machine learning is a relatively new field, without a unanimous definition. In many ways, actuaries have been machine learners. In both pricing and reserving, but also more recently in capital modelling, actuaries have combined statistical methodology with a deep understanding of the problem at hand and how any solution may affect the company and its customers. One aspect that has, perhaps, not been so well developed among actuaries is validation. Discussions among actuaries’ “preferred methods” were often without solid scientific arguments, including validation of the case at hand. Through this collection, we aim to promote a good practice of machine learning in insurance, considering the following three key issues: a) who is the client, or sponsor, or otherwise interested real-life target of the study? b) The reason for working with a particular data set and a clarification of the available extra knowledge, that we also call prior knowledge, besides the data set alone. c) A mathematical statistical argument for the validation procedure.

deposit insurance --- implied volatility --- static arbitrage --- parameterization --- machine learning --- calibration --- dichotomous response --- predictive model --- tree boosting --- GLM --- validation --- generalised linear modelling --- zero-inflated poisson model --- telematics --- benchmark --- cross-validation --- prediction --- stock return volatility --- long-term forecasts --- overlapping returns --- autocorrelation --- chain ladder --- Bornhuetter–Ferguson --- maximum likelihood --- exponential families --- canonical parameters --- prior knowledge --- accelerated failure time model --- chain-ladder method --- local linear kernel estimation --- non-life reserving --- operational time --- zero-inflation --- overdispersion --- automobile insurance --- risk classification --- risk selection --- least-squares monte carlo method --- proxy modeling --- life insurance --- Solvency II --- claims prediction --- export credit insurance --- semiparametric modeling --- VaR estimation --- analyzing financial data --- n/a --- Bornhuetter-Ferguson