2018 · 29 citations · 12 references
BusinessComputer ScienceInsuranceData MiningInsurance AnalyticsMachine LearningClassificationFraud DetectionFraud Detection In ClaimsFraud Detection In UnderwritingInsurance FraudSupport Vector MachinesClass ImbalanceCross-validationImbalanced ClassificationInsurance Claim Fraud DetectionMachine Learning For InsuranceMultilayer PerceptronPerceptronSupport Vector Machine Classification
This article demonstrates a novel fraud identification methodology in auto insurance sector with the help of an adaptive oversampling technique (ADASYN). Initially, the data imbalanceness is removed from the original claim data set by employing ADASYN on the minority class instances. Three different classifiers, namely, Support Vector Machine, Decision Tree and Multi Layer Perceptron are further used for classifying the anomalous records from the normal ones. The efficacy of the models are tested with the 10-fold cross validation methodology. The performance of the model is demonstrated by several tests carried out on an auto insurance data set. Moreover, the outcomes justify the effectiveness of the proposed algorithm on a balanced data set over an unbalanced one.
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SMOTE: Synthetic Minority Over-sampling Technique
Nitesh V. Chawla, Kevin W. Bowyer, Lawrence Hall et al. · Journal of Artificial Intelligence Research · 2002 · 31.2K citations · Full text
ADASYN: Adaptive synthetic sampling approach for imbalanced learning
Haibo He, Yang Bai, Edwardo A. Garcia et al. · 2008 · 4.4K citations