Uncovering Fraud in Direct Marketing Data with a Fraud Auditing Case Builder
- Cite this paper as:
- Lu F. (2007) Uncovering Fraud in Direct Marketing Data with a Fraud Auditing Case Builder. In: Kok J.N., Koronacki J., Lopez de Mantaras R., Matwin S., Mladenič D., Skowron A. (eds) Knowledge Discovery in Databases: PKDD 2007. PKDD 2007. Lecture Notes in Computer Science, vol 4702. Springer, Berlin, Heidelberg
This paper illustrates an automated system that replicates the investigative operation of human fraud auditors. Human fraud auditors often utilize fraud detection methods that exploit structure in database tables to uncover outliers that may be part of a fraud case. From the uncovered outliers, an auditor will build a case of fraud by searching data related to the outlier possibly across many different databases and tables within these different databases. This paper illustrates an industrial implementation of an adaptive fraud case building system that uses machine learning to conduct the search and decision-making process with an automated outlier detection component. This system was successfully applied to uncover fraud cases in real marketing data.
KeywordsFraud Detection Benford’s Law Reinforcement Learning
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