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.