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Dissertation Chapters | Statistics | India | Volume 11 Issue 11, November 2022 | Rating: 4.9 / 10
Statistical Modelling for Health Insurance Data
K. Navatha [2] | V V Hara Gopal [2]
Abstract: Uncertainty refers to the randomness and is different from a lack of predictability or market inefficiency. An emergent research view holds that financial markets are both uncertain and predictable. Also, markets can be efficient but also uncertain. Insurance companies typically face two major problems when they want to forecast future premiums paid by using past or present behavior of premiums paid. For this, one has to find an appropriate statistical Probability distribution for the premiums paid. Then after test how well this statistical distribution fits the claims data. In modeling insurance claims, when there are extreme observations in the data, the commonly used loss distributions often are able to ?t the bulk of the data well but fail to do so at the tail. One approach to overcome this problem is to focus on the extreme observations only and model them with the generalized Pareto distribution, supported by extreme value theory. The objective of this paper is to obtain an appropriate statistical Probability distribution for the insurance premium amounts and to test how well the chosen statistical distribution fits the premiums data. The modeling process will ascertain a statistical distribution that could capable model the claim amounts, and then the goodness of fit test was done mathematically using graphically using the Probability - Probability Plots (P - P plots) and Quantile - Quantile Plots (Q - Q plots). Finally, the study gives a summary, conclusion and recommendations that can be used by insurance companies to improve their results concerning future premium inferences.
Keywords: premiums, extreme value theory, generalized Pareto distribution, generalized extreme value distribution, P - P plot, Q - Q Plot
Edition: Volume 11 Issue 11, November 2022,
Pages: 1082 - 1084