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
Scholarly Commons Citation
Bhowmik, Rekha, "Data Mining Techniques for Fraud Detection" (2008). Annual ADFSL Conference on Digital Forensics, Security and Law. 6.
https://commons.erau.edu/adfsl/2008/friday/6
Included in
Computer Engineering Commons, Computer Law Commons, Electrical and Computer Engineering Commons, Forensic Science and Technology Commons, Information Security Commons
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
