Spatial modeling has
largely been applied in epidemiology and disease modeling. Different methods
such as Generalized linear models (GLMs) have been made available to prediction
of the claim frequencies. However, due to heterogeneity nature of policies, the
methods do not generate precise and accurate claim frequencies predictions;
these parametric statistical methods extensively depend on limiting assumptions
(linearity, normality, independence among predictor variables, and a
pre-existing functional form relating the criterion variable and predictive
variables). This study investigates how to derive a spatial nonparametric model
estimator based on smoothing Spline for predicting claim frequencies. The
simulation results showed that the proposed estimator is efficient for
prediction of claim frequencies than the kernel based counterpart. The
estimator derived was applied to a sample of 6500 observations obtained from
Cooperative Insurance Company, Kenya for the period of 2018-2020 and the
results showed that the proposed method performs better than the kernel based counterpart. It is
worth noting that inclusion of the spatial effects significantly improves the
estimator prediction of claim frequency.
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