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has been cited by the following article:

• TITLE: Measuring the Adequacy of Loss Distribution for the Ghanaian Auto Insurance Risk Exposure through Maximum Likelihood Estimation

  AUTHORS: Jacob Azaare, Zhao Wu, Yingying Zhu, Gabriel Armah, Gideon Mensah Engmann, Socrates Modzi Kwadwo, Bright Nana Kwame Ahia, Enock Mintah Ampaw

  KEYWORDS: Leptokurtic, Loss Distribution, Policyholders Claims, Auto Insurance, Lognormal

  JOURNAL NAME: Open Journal of Business and Management, Vol.10 No.2, March 22, 2022

  ABSTRACT: Loss distribution plays an influential role in evaluating risks from policyholders’ claims. Nevertheless, the auto insurance market in Ghana pays little attention to policyholders’ claims distribution, resulting in the market’s inefficiency. This study investigates the type of loss distribution function that best approximates policyholders’ claims in Ghana. We applied the Kullback-Leibler divergence, Kolmogorov Smirnov, Anderson-Darling statistical tests and maximum likelihood estimation (MLE) to estimate policyholders’ claims. The results suggest that Ghana’s auto policyholder’s claims are better approximated using the lognormal probability distribution. Through the lognormal distribution, the industry can adequately evaluate policyholders’ claims to minimize potential loss. Additionally, this distribution could enable the market reach decisions on premiums and expected profits theoretically.