TITLE:
A Predictive Modeling for Detecting Fraudulent Automobile Insurance Claims
AUTHORS:
Hojin Moon, Yuan Pu, Cesarina Ceglia
KEYWORDS:
Classification, Cross-Validation, Prediction Models, Statistical Learning Algorithms, Variable Importance Algorithms
JOURNAL NAME:
Theoretical Economics Letters,
Vol.9 No.6,
August
20,
2019
ABSTRACT: Fraudulent automobile insurance claims are not only
a loss for insurance companies, but also for their policyholders. The goal of
this research is to develop, first, a decision-making algorithm to classify
whether a claim is classified as fraudulent or not; and, second, what types of
variables should be focused to detect fraudulent claims. To achieve this goal,
highly accurate prediction models are built by discovering important sets of
features via variable selection algorithms, which can in turn help prevent
future loss. In this research, parametric and nonparametric statistical
learning algorithms are considered to reduce uncertainty and increase the
chances of detecting the appropriate claims. An important set of features for a
model is determined by measuring variable importance based on the observed characteristics
of a claim via a cross-validation and by testing improvement of the performance
at which automobile fraudulent claims are accurately classified using Akaike
Information Criterion. We could achieve accuracy above 95% with a set of
features selected via a cross-validation. This research would offer some
benefit to the insurance industry for their fraud detection research in order
to prevent insurance abuse from escalating any further.