Event / Presentation Title

Data Mining Techniques for Fraud Detection

Proposal / Submission Type

Peer Reviewed Paper

Location

Oklahoma City, Oklahoma

Start Date

25-4-2008 9:05 AM

Abstract

The paper presents application of data mining techniques to fraud analysis. We present some classification and prediction data mining techniques which we consider important to handle fraud detection. There exist a number of data mining algorithms and we present statistics-based algorithm, decision tree-based algorithm and rule-based algorithm. We present Bayesian classification model to detect fraud in automobile insurance. Naïve Bayesian visualization is selected to analyze and interpret the classifier predictions. We illustrate how ROC curves can be deployed for model assessment in order to provide a more intuitive analysis of the models.

Keywords: Data Mining, Decision Tree, Bayesian Network, ROC Curve, Confusion Matrix

 
Apr 25th, 9:05 AM

Data Mining Techniques for Fraud Detection

Oklahoma City, Oklahoma

The paper presents application of data mining techniques to fraud analysis. We present some classification and prediction data mining techniques which we consider important to handle fraud detection. There exist a number of data mining algorithms and we present statistics-based algorithm, decision tree-based algorithm and rule-based algorithm. We present Bayesian classification model to detect fraud in automobile insurance. Naïve Bayesian visualization is selected to analyze and interpret the classifier predictions. We illustrate how ROC curves can be deployed for model assessment in order to provide a more intuitive analysis of the models.

Keywords: Data Mining, Decision Tree, Bayesian Network, ROC Curve, Confusion Matrix