Exploring the Role of Pseudodeductibles in Auto Insurance Claims Reporting

Many who purchase insurance understand that by reporting covered losses to their insurers they increase the chances of future premium increases or reductions in insurance coverage.If under such circumstances a policyholder decides not to report an otherwise covered loss, the policyholder is effectively displaying the presence of a “pseudodeductible.” That is, given a covered loss occurs, a policyholder may define a personal and unobservable threshold that is greater than any stated deductible in the policy below which an insurance claim for the loss will not be reported. The scant amount of empirical research on this topic suffers from a lack of information about losses for which insurance claims were never filed. This research relies on a unique dataset that captures when policyholders choose to forgo insurance claims and why. The findings increase our understanding of the role that pseudodeductibles play in the claims reporting behavior of policyholders

Fine-Tuning a -Nearest Neighbors Machine Learning Model for the Detection of Insurance Fraud

Billions of dollars are lost within insurance companies due to fraud. Large money losses force insurance companies to increase premium costs and/or restrict policies. This negatively affects a company’s loyal customers. Although this is a prevalent problem, companies are not urgently working toward bettering their machine learning algorithms. Underskilled workers paired with inefficient computer algorithms make it difficult to accurately and reliably detect fraud. The goal of this study is to understand the idea of -Nearest Neighbors ( -NN) and to use this classification technique to accurately detect fraudulent auto insurance claims. Using -NN requires choosing a value and a distance metric. The best choice of values and distance metrics will be unique to every dataset. This study aims to break down the processes involved in determining an accurate value and distance metric for a sample auto insurance claims dataset. Odd values 1 through 19 and the Euclidean, Manhattan, Chebyshev, and Hassanat metrics are analyzed using Excel and R. Results support the idea that unique values and distance metrics are needed depending on the dataset being worked with. Keywords: machine learning, insurance, fraud, detection, k-NN, distanc

Testing extreme value copulas to estimate the quantile

We generalize the test proposed by Kojadinovic, Segers and Yan which is used for testing whether the data belongs to the family of extreme value copulas. We prove that the generalized test can be applied whatever the alternative hypothesis. We also study the effect of using different extreme value copulas in the context of risk estimation. To measure the risk we use a quantile. Our results have been motivated by a bivariate sample of losses from a real database of auto insurance claims

Testing extreme value copulas to estimate the quantile

We generalize the test proposed by Kojadinovic, Segers and Yan which is used for testing whether the data belongs to the family of extreme value copulas. We prove that the generalized test can be applied whatever the alternative hypothesis. We also study the effect of using different extreme value copulas in the context of risk estimation. To measure the risk we use a quantile. Our results have been motivated by a bivariate sample of losses from a real database of auto insurance claims

Testing extreme value copulas to estimate the quantile

We generalize the test proposed by Kojadinovic, Segers and Yan which is used for testing whether the data belongs to the family of extreme value copulas. We prove that the generalized test can be applied whatever the alternative hypothesis. We also study the effect of using different extreme value copulas in the context of risk estimation. To measure the risk we use a quantile. Our results have been motivated by a bivariate sample of losses from a real database of auto Insurance claims

Testing extreme value copulas to estimate the quantile

Testing weather or not data belongs could been generated by a family of extreme value copulas is difficult. We generalize a test and we prove that it can be applied whatever the alternative hypothesis. We also study the effect of using different extreme value copulas in the context of risk estimation. To measure the risk we use a quantile. Our results have motivated by a bivariate sample of losses from a real database of auto insurance claims. Methods are implemented in R

Testing extreme value copulas to estimate the quantile

Testing weather or not data belongs could been generated by a family of extreme value copulas is difficult. We generalize a test and we prove that it can be applied whatever the alternative hypothesis. We also study the effect of using different extreme value copulas in the context of risk estimation. To measure the risk we use a quantile. Our results have motivated by a bivariate sample of losses from a real database of auto insurance claims. Methods are implemented in R

An analysis of claim frequency and claim severity for third party motor insurance using Monte Carlo simulation techniques

The purpose of this thesis is to introduce the reader to Multiple Regression and Monte Carlo simulation techniques in order to find the expected compensation cost the insurance company needs to pay due to claims made. With a fundamental understanding of probability theory, we can advance to Markov chain theory and Monte Carlo Markov Chains (MCMC). In the insurance field, in particular non-life insurance, expected compensation is very important to calculate the average cost of each claim. Applying Markov models, simulations will be run in order to predict claim frequency and claim severity. A variety of models will be implemented to compute claim frequency. These claim frequency results, along with the claim severity results, will then be used to compute an expected compensation for third party auto insurance claims. Multiple models are tested and compared.Master of Science (MSc) in Computational Science

Design and Implementation of Car Insurance Claims Mobile Survey Platform

近年来随着国民经济之快速发展，人民生活富裕；汽车购买力大幅提升，而汽、机车数量也随之急遽增加。伴随着汽车数量之快速成长，汽车保险业务量成倍数扩展。由于汽车保险市场一向是产险公司主要之战场，我国产物保险总保费收入当中之一半来自汽车险，其如何改善汽车保险业务之经营，直接冲击保险业者经营之利润，成为产险业者最关心之课题，也是整个产险经营成功之所系。车辆保险的理赔工作，是一项复杂的平台性工作，要求保险公司有一套标准、详细、完善的理赔制度和操作手段，也要求保险公司从业人员有良好的职业态度和职业素养。只有这样，才能在为众多被保险人分散风险、进行经济补偿的同时，满足保险公司对利润的追求。 现场查勘人员如何...In recent years, with the rapid development of the national economy, people's lives rich; greatly enhance the purchasing power of automobile, and the steam locomotive number also increased sharply. With the rapid growth of the number of cars, car insurance business volume expanded. Because the automobile insurance market has always been the main battlefield of the insurance company, half of China'...学位：工程硕士院系专业：软件学院_工程硕士(软件工程)学号：X201323050

Product-limit estimators of the gap time distribution of a renewal process under different sampling patterns

Nonparametric estimation of the gap time distribution in a simple renewal process may be considered a problem in survival analysis under particular sampling frames corresponding to how the renewal process is observed. This note describes several such situations where simple product limit estimators, though inefficient, may still be useful