Abstract:
Insurance companies often experience difficulties in planning funds. This is caused
by the risk of uncertainty in life. Not least, the insurance company went bankrupt
because they could not pay their obligations. For this reason, insurance companies
need to know the right strategy for setting up reserve funds. One solution that can
help insurance companies make decisions and determine strategies is to make claim
prediction. In this study, the author will use the Naïve Bayes Classifier method to
predict claims in automobile insurance. The Naïve Bayes Classifier itself is a simple
probability method where the calculations are based on Bayes’ Theorem. The data
used is secondary data from Kaggle.com where this data consists of 10,000 samples
with 19 features. The prediction results will be divided into two results, namely 0
and 1 where 0 means "no" and 1 means "yes". "yes" or "no" label will inform us
whether the customer will claim or not. The data will go through preprocessing in
python so that the format is appropriate. The model will be built without feature
selection and with feature selection then being compared to determine the best
model. Predicted data result will be compared to the actual data and the accuracy
of the best model is 82%. Other evaluation method was applied to evaluate how
well the model performed by using ROC – AUC score which has a score of 0.87
and 10-fold cross validation which has an average score of 80%. The result of the
prediction will help insurance company with underwriting decision and financial
approach or planning.
