Abstract:
Medical Malpractice Claims present unique challenges for healthcare providers
and insurance carriers because of their uncertain nature. Large financial losses
arising from a high claim count are difficult to quantify. Therefore, an appropriate
distribution model and estimation method are essential for effective risk
management and planning. One of the significant distribution models is Pareto
Distribution, having estimation techniques such as Maximum Likelihood
Estimation (MLE) and Method of Moments (MoM). The objective of this study is
to investigate the potential performance disparity between two major types of
methods that is Maximum Likelihood Estimation and Method of Moments for
estimating Pareto distribution parameters on medical malpractice claims data as
applied specifically to the frequency-of-claims problem, focusing particularly on
claim ages falling within age interval 36–65 years. The evaluation is done based
on error metrics that is Mean Absolute Error (MAE), Mean Absolute Percentage
Error (MAPE), Mean Squared Error (MSE), and Root Mean Squared Error
(RMSE). The analysis results show that MAE value for MoM is 0.02065, while
for MLE is 0.01883. The MAPE calculation results show that the prediction value
of MoM is 63%, while that of the MLE is 57%. In addition, the MSE value for
MoM is 0.00060 and for the MLE is 0.00047. Last, the RMSE value for MoM is
0.02440, while for the MLE is 0.02163. Furthermore, the MAPE exceeding 50%
indicates both methods are less effective for this particular case. However, based
on the MAE, MSE, and RMSE results in this study, MLE and MoM show almost
the same accuracy, so both methods can be considered equally good for parameter
estimation. Nonetheless, in general, MLE is often considered a better method than
MoM due to its more comprehensive approach in theory.
