Abstract:
Fraudulent claims in motor insurance policies continue to be a big menace to insurance companies. Fraudsters are
devising new tactics of fabricating claims to make them appear
valid. This makes insurance companies register huge losses in
billions of money every year. The insurance policyholders bear
these losses through increased premiums thus having negative
social and economic ramifications. Numerous approaches have
been proposed and applied in detecting and preventing fraudulent claims. The traditional approaches have become complex,
time-consuming, and with low success ratio. To improve on
fraud detection, the existing historical data can be used to train
prediction models. To optimize the performance, this data require
feature engineering to ensure only relevant features are used
and handling of class imbalance. In this paper, we propose a
model that is built on XGBoost algorithm. In data preparation,
we propose to handle class imbalance by oversampling, using
SMOTE. We aim at comparing the effect of class imbalance
and oversampling on the performance of our model. The results
obtained reveals that XGBoost performs well with SMOTE
compared to imbalanced training dataset and also compared
to other algorithms. Once the model is deployed, insurance
companies will be able to detect and identify perpetrators of
fraud and take necessary action. This will reduce their loss
adjustment expenses and thus increase their profits.
