This paper proposes the use of bootstrap, robust and fuzzy multiple linear regressions method in
handling general insurance in order to get improved results. The main objective of bootstrapping is to
estimate the distribution of an estimator or test statistic by resampling one's data or a model estimated
from the data under conditions that hold in a wide variety of econometric applications. In addition,
bootstrap also provides approximations to distributions of statistics, coverage probabilities of confidence
intervals, and rejection probabilities of hypothesis tests that produce accurate results. In this paper, we
emphasize the combining and modelling using bootstrapping, robust and fuzzy regression methodology.
The results show that alternative methods produce better results than multiple linear regressions (MLR)
model.