A DATA WAREHOUSE DESIGN FOR THE DETECTION OF FRAUD IN THE SUPPLY CHAIN BY USING THE BENFORD’S LAW
- 1 , Germany
- 2 Concordia University, Canada
Large data volumes and the inability to analyse them enables fraudulent activities to go unnoticed in supply chain management processes such as procurement, warehouse management and inventory management. This fraud increases the cost of the supply chain management and a fraud detection mechanism is necessary to reduce the risk of fraud in this business area. This study was carried out in order to develop a data warehouse design that supports forensic analytics by using the Benford’s law in order to detect fraud. The approach relies on a generic and re-usable store procedure for data analytics. The data warehouse was tested with two datasets collected from an operational supply chain database from the inventory management and warranty claims processes. The results of the research showed that the supply chain data analyzed obeys to Benford’s theory and that parameterized stored procedures with Dynamic SQL provide an excellent tool to analyze data in the supply chain for possible fraud detection. The implications of the results of the study are that the Benford’s law can be used to detect fraud in the supply chain with the help of parameterized stored procedures and a data ware house, this can ease the workload of the fraud analyst in the supply chain function. Although the research only used data from the inventory management and warranty claim processes, the proposed store procedures can be extended to any process in the supply chain making the results generalizable to the supply chain management process.
Copyright: © 2014 Cornelia Kraus and Raul Valverde. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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- 20 Citations
- Supply Chain Fraud
- Supply Chain Management Systems
- Benford’s law
- Fraud Detection
- Data Mining for Fraud
- Accounting Information Systems