A Fraud Detection Visualization System Utilizing Radial Drawings and Heat-maps

Evmorfia N. Argyriou, Antonios Symvonis, Vassilis Vassiliou

Abstract

We present a prototype system developed in cooperation with a business organization that combines information visualization and pattern-matching techniques to detect fraudulent activity by employees. The system is built upon common fraud patterns searched while trying to detect occupational fraud suggested by internal auditors of a business company. The main visualization of the system consists of a multi-layer radial drawing that represents the activity of the employees and clients. Each layer represents a different examined pattern whereas heat-maps indicating suspicious activity are incorporated in the visualization. The data are first preprocessed based on a decision tree generated by the examined patterns and each employee is assigned a value indicating whether or not there exist indications of fraud. The visualization is presented as an animation and the employees are visualized one by one according to their severity values together with their related clients.

References

  1. Argyriou, E. N., Sotiraki, A., and Symvonis, A. (2013a). Occupational fraud detection through visualization. In ISI, pages 4-7.
  2. Argyriou, E. N. and Symvonis, A. (2012). Detecting periodicity in serial data through visualization. In ISVC, volume 7432, pages 295-304.
  3. Argyriou, E. N., Symvonis, A., and Vassiliou, V. (2013b). A fraud detection visualization system utilizing radial drawings and heat-maps. Technical report, arXiv:submit/0845288.
  4. Association of Certified Fraud Examiners (2012). Report to the Nation on Occupational Fraud and Abuse.
  5. Bolton, R. J. and Hand, D. J. (2002). Statistical fraud detection: A review. Statistical Science, 17:2002.
  6. Chang, R., Lee, A., Ghoniem, M., Kosara, R., and Ribarsky, W. (2008). Scalable and interactive visual analysis of financial wire transactions for fraud detection. Information Visualization, 7(1):63-76.
  7. Didimo, W., Liotta, G., and Montecchiani, F. (2012). Vis4aui: Visual analysis of banking activity networks. In GRAPP/IVAPP, pages 799-802.
  8. Didimo, W., Liotta, G., Montecchiani, F., and Palladino, P. (2011). An advanced network visualization system for financial crime detection. In PacificVis, pages 203- 210.
  9. Eberle, W. and Holder, L. B. (2009). Mining for insider threats in business transactions and processes. In CIDM, pages 163-170.
  10. Giacomo, E. D., Didimo, W., Liotta, G., and Palladino, P. (2010). Visual analysis of financial crimes. In AVI, pages 393-394.
  11. Huang, M. L., Liang, J., and Nguyen, Q. V. (2009). A visualization approach for frauds detection in financial market. IV 7809, pages 197-202.
  12. Kou, Y., Lu, C.-T., Sirwongwattana, S., and Huang, Y.-P. (2004). Survey of fraud detection techniques. In Networking, Sensing and Control, 2004 IEEE Int. Conf., volume 2, pages 749-754.
  13. Luell, J. (2010). Employee fraud detection under real world conditions. PhD thesis.
  14. Phua, C., Lee, V. C. S., Smith-Miles, K., and Gayler, R. W. (2010). A comprehensive survey of data mining-based fraud detection research. CoRR, abs/1009.6119.
  15. Senator, T. E., Goldberg, H. G., Shyr, P., Bennett, S., Donoho, S., and Lovell, C. (2002). chapter The NASD regulation advanced detection system: integrating data mining and visualization for break detection in the NASDAQ stock market, pages 363-371.
  16. Senator, T. E., Goldberg, H. G., Wooton, J., Cottini, M. A., Khan, A. F. U., Klinger, C. D., Llamas, W. M., Marrone, M. P., and Wong, R. W. H. (1995). The financial crimes enforcement network ai system (fais) identifying potential money laundering from reports of large cash transactions. AI Magazine, 16(4):21-39.
  17. Stillwell, W. G., Seaver, D. A., and Edwards, W. (1981). A comparison of weight approximation techniques in multiattribute utility decision making. Organ. Behavior and Human Performance, 28(1):62 - 77.
  18. SynerScope (2011). http://www.synerscope.com/.
  19. Vlachos, M., Hadjieleftheriou, M., Gunopulos, D., and Keogh, E. (2003). Indexing multi-dimensional timeseries with support for multiple distance measures. In ACM SIGKDD int. conf. on Knowledge discovery and data mining, KDD 7803, pages 216-225.
  20. Wolverton, M., Berry, P., Harrison, I., Lowrance, J., Morley, D., Rodriguez, A., Ruspini, E., and Thomere, J. (2003). Law: A workbench for approximate pattern matching in relational data. In IAAI, pages 143-150.
Download


Paper Citation


in Harvard Style

N. Argyriou E., Symvonis A. and Vassiliou V. (2014). A Fraud Detection Visualization System Utilizing Radial Drawings and Heat-maps . In Proceedings of the 5th International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2014) ISBN 978-989-758-005-5, pages 153-160. DOI: 10.5220/0004735501530160


in Bibtex Style

@conference{ivapp14,
author={Evmorfia N. Argyriou and Antonios Symvonis and Vassilis Vassiliou},
title={A Fraud Detection Visualization System Utilizing Radial Drawings and Heat-maps},
booktitle={Proceedings of the 5th International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2014)},
year={2014},
pages={153-160},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004735501530160},
isbn={978-989-758-005-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2014)
TI - A Fraud Detection Visualization System Utilizing Radial Drawings and Heat-maps
SN - 978-989-758-005-5
AU - N. Argyriou E.
AU - Symvonis A.
AU - Vassiliou V.
PY - 2014
SP - 153
EP - 160
DO - 10.5220/0004735501530160