Direct Debit Frauds: A Novel Detection Approach

Gaetano Papale, Luigi Sgaglione, Gianfranco Cerullo, Giovanni Mazzeo, Pasquale Starace, Ferdinando Campanile

Abstract

Single Euro Payments Area (SEPA) is an initiative of the European banking industry aiming at making all electronic payments across the Euro area as easy as domestic payments currently are. One of the payment schemes defined by the SEPA mandate is the SEPA Direct Debit (SDD) that allows a creditor (biller) to collect directly funds from a debtor’s (payer’s) account. It is apparent that the use of this standard scheme facilitates the access to new markets by enterprises and public administrations and allows for a substantial cost reduction. However, the other side of the coin is represented by the security issues concerning this type of electronic payments. A study conducted by Center of Economics and Business Research (CEBR) of Britain showed that from 2006 to 2010 the Direct Debit frauds have increased of 288%. In this paper a comprehensive analysis of real SDD data provided by the EU FP7 LeanBigData project is performed. The results of this data analysis will conduct to define emerging attack patterns that can be execute against SDD and the related effective detection criteria. All the work aims at inspire the design of a security system supporting analysts to detect Direct Debit frauds.

References

  1. Allison, S. e. a., 2005. Exploring the crime of identity theft: Prevalence, clearance rates, and victim/offender characteristics. Journal of Criminal Justice, pp. 19-29.
  2. BEUC, 2011. Establishing technical requirements for credit transfers and direct debits in euro. (Online) Available at: http://www.beuc.eu/publications/ 2011- 00202-01-e.pdf [Accessed 24 October 2015].
  3. Blash, E. e. a., 2013. Revisting theJDL model for information exploitation. In: Information Fusion, 16th International Conference on., IEEE.
  4. CI, 2015. Creditor Identifier Overview. (Online) Available at: http://www.europeanpaymentscouncil .eu/index.cfm/ [Accessed 23 October 2015].
  5. Cicotti, G. C. L. a. a., 2012. QoS monitoring in a cloud services environment: the SRT-15 approach. In: EuroPar 2011: Parallel Processing Workshops. Springer Berlin Heidelberg, pp. 15-24.
  6. Cicotti, G. e. a., 2015. How to monitor QoS in cloud infrastructures: the QoSMONaaS approach., International Journal of Computational Science and Engineering, 11(1), pp. 29-45.
  7. Coppolino, L. e. a., 2015. Use of the Dempster-Shafer Theory for Fraud Detection: The Mobile Money Transfer Case Study. Intelligent Distributed Computing VIII. Springer, p. 465-474..
  8. Coppolino, L. e. a., 2015. Effective Visualization of a Big Data Banking Application. In: Intelligent Interactive Multimedia Systems and Services, Springer, pp. 57-68.
  9. D'Antonio, e. a., 2015. Use of the Dempster-Shafer theory to detect account takeovers in mobile money transfer services. Journal of Ambient Intelligence and Humanized Computing, DOI:10.1007/s12 652-015- 0276-9., p. 1-10.
  10. Dempster, A., 1968. A generalization of bayesian inference. Journal of the Royal Statistical Society, Volume Series B (Methodological), pp. 205-247.
  11. Duman, E. a. O. M. H., 2011. Detecting credit card fraud by genetic algorithm and scatter search. Expert Systems with Applications, 38(10).
  12. EPC, 2002. Euroland: Our Single Euro Payment Area!. (Online) Available at: http://www.europeanpayments council.eu/index.cfm/knowledge-bank/epc-documents/ euroland-our-single-payment-area/sepa-whitepaper-05 20021pdf/ (Accessed 15 January 2016).
  13. Ficco, M. e. a., 2011. An event correlation approach for fault diagnosis in SCADA infrastruc-tures. In: Proceedings of the 13th European Workshop on Dependable Computing. s.l.:ACM, pp. 15-20.
  14. Ficco, M. and Rak, M., 2012. Intrusion tolerance as a service: a SLA-based solution. Porto, Portugal, 2nd Int. Conf. on Cloud Computing and Services Science (CLOSER 2012).
  15. Ficco, M. and Rak. M., 2012. Intrusion tolerance in cloud applications: the mosaic approach. Palermo, Italy, 6th Int. Conf. on Complex, Intelligent, and Software Intensive Systems (CISIS 2012).
  16. FINEXTRA, 2010. Direct debit fraud at an all-time high; Bacs challenges figures. (Online) Available at: http://www.finextra.com/news/fullstory. aspx?news itemid=22028 [Accessed 11 January 2016].
  17. Finklea, M., 2010. Identity theft: Trends and issues. DIANE Publishing.
  18. Google inc., Google Maps Geocoding API. (Online) Available at: https://developers.google.com/maps/ documentation/geocoding/intro [Accessed 18 January 2016].
  19. Goswell, S., 2006. Iso 20022: The implications for payments processing and requirements for its successful use. Journal of Payments Strategy & Systems, 1(1), pp. 42-50.
  20. List, 2015. Epc List of SEPA Scheme Countries. [Online] Available at: http://www.europeanpaymentscouncil .eu/index.cfm/knowledge-bank/epc-documents/epc-list -of-sepa-scheme-countries/epc409-09-epc-list-of-sepascheme-countries-v21-june-2015pdf/ [Accessed 22 October 2015].
  21. Pardede, M. e. a., 2013. E-fraud, Taxonomy on Methods of Attacks, Prevention, Detection, Investigation, Prosecution and Restitution.
  22. Patidar, R. e. a., 2011. Credit Card Fraud Detection Using Neural Network. International Journal of Soft Computing and Engineering (IJSCE) ISSN, p. 2231- 2307.
  23. Project, 2014. Ultra-Scalable and Ultra-Efficient Integrated and Visual Big Data Analytics. (Online) Available at: http://leanbigdata.eu/ [Accessed 12 January 2016].
  24. Raj, S e. a., 2011. Analysis on credit card fraud detection methods. Computer, Communication and Electrical Technology (ICCCET), 2011 International Conference on., pp. 152-156.
  25. Romano, L. e. a., 2010. An intrusion detection system for critical information infrastructures using wireless sensor network technologies. In: Critical Infrastructure (CRIS), 2010 5th International Conference on., IEEE, pp. 1-8.
Download


Paper Citation


in Harvard Style

Papale G., Sgaglione L., Cerullo G., Mazzeo G., Starace P. and Campanile F. (2016). Direct Debit Frauds: A Novel Detection Approach . In Proceedings of the 6th International Conference on Cloud Computing and Services Science - Volume 1: DataDiversityConvergence, (CLOSER 2016) ISBN 978-989-758-182-3, pages 380-387. DOI: 10.5220/0005933103800387


in Bibtex Style

@conference{datadiversityconvergence16,
author={Gaetano Papale and Luigi Sgaglione and Gianfranco Cerullo and Giovanni Mazzeo and Pasquale Starace and Ferdinando Campanile},
title={Direct Debit Frauds: A Novel Detection Approach},
booktitle={Proceedings of the 6th International Conference on Cloud Computing and Services Science - Volume 1: DataDiversityConvergence, (CLOSER 2016)},
year={2016},
pages={380-387},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005933103800387},
isbn={978-989-758-182-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Cloud Computing and Services Science - Volume 1: DataDiversityConvergence, (CLOSER 2016)
TI - Direct Debit Frauds: A Novel Detection Approach
SN - 978-989-758-182-3
AU - Papale G.
AU - Sgaglione L.
AU - Cerullo G.
AU - Mazzeo G.
AU - Starace P.
AU - Campanile F.
PY - 2016
SP - 380
EP - 387
DO - 10.5220/0005933103800387