Advertisement

Cognitive Data Science Automatic Fraud Detection Solution, Based on Benford’S Law, Fuzzy Logic with Elements of Machine Learning

  • Goran KlepacEmail author
Chapter
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 14)

Abstract

Developing fraud detection models always has been challenging area. Low frequency of fraudulent cases within data, indications instead of certainty contribute to very challenging area for data science method applying. Traditional approach of predictive modelling became insufficient, because relaying on few variables as a base of the fraud model are very fragile concept. Reason for that is fact that we are talking about portfolio with low cases of events, and from the other hand it is unrealistic to lean on few variables articulated through logistic regression, neural network or similar method that will be able to detect sophisticated try of fraudulent activities. Chapter gives proposal how to use data science in such situations where there are no solid bases but only potential suspicious regarding fraudulent activities. For those purposes Benford’s law in combination with other data science methods and fuzzy logic will be used on sample data set, and will be shown potentials of proposed methodology for fraud detection purposes. Chapter shows case study in domain of finance on public data, where proposed methodology will be illustrated an efficient methodology which can be usable for fraud detection purposes.

Keywords

Benford’s law Fuzzy expert system Cognitive data science Fraud detection Machine learning 

References

  1. 1.
    Abraham, A., Hassanien, A.-E.: Computational Social Network Analysis Trends. In: Tools and Research Advances. Springer, London, UK (2010)Google Scholar
  2. 2.
    Aharony, N., Pan, W., Cory, I., Khayal, I., Pentland, A.: Social fMRI: investigating and shaping social mechanisms in the real world. Pervasive Mobile Comput. 7(6), str. 643–659 (2011)Google Scholar
  3. 3.
    Akoglu, L., Vaz de Melo, P.O.S., Faloutsos, C.: Quantifying reciprocity in large weighted communication networks. In: PAKDD 2, Lecture Notes in Computer Science, vol. 7302, pp. 85–96. Springer, str, Berlin, Heidelberg (2012)Google Scholar
  4. 4.
    Altshuler, Y., Pan, W., Pentland, A.: Trends Prediction Using Social Diffusion Models. In: Social Computing, Behavioral-Cultural Modeling and Prediction: Lecture Notes in Computer Science series. vol. 97, p. 104 Springer, str, Berlin, Heidelberg (2012)Google Scholar
  5. 5.
    Baesens, B., van Vlasselaer, V., Verbeke, W.: Fraud Analytics Using Descriptive, Predictive, and Social Network (2015) Techniques, WileyGoogle Scholar
  6. 6.
    Benford, F.: The law of anomalous numbers. Proc. Am. Philos. Soc. 78, 551–572 (1938)zbMATHGoogle Scholar
  7. 7.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)zbMATHGoogle Scholar
  8. 8.
    Bolton, R.J., Hand, D.J.: statistical fraud detection: a review. Stat. Sci. 17(3) 235–249 (2002)Google Scholar
  9. 9.
    Bojadziev, G., Bojadziev, M.: Fuzzy Logic for Business, Finance, and Management, 2nd edn. World Scientific Publishing Co., Inc, River Edge, NJ, USA (2007)Google Scholar
  10. 10.
    Carrington, P.J., Scott, J., Wasserman, S. (eds.): Models and Methods in Social Network Analysis, pp. 248–249. Cambridge University Press, Cambridge (2005)CrossRefGoogle Scholar
  11. 11.
    Coser, L.A.: Masters of Sociological Thought: Ideas in Historical and Social Context, 2nd edn. Harcourt, New York, NY (1977)Google Scholar
  12. 12.
    D’Agostini G.D.: Bayesian Reasoning in Data Analysis: A Critical Introduction. World Scientific, New York (2003)Google Scholar
  13. 13.
    Duffield, G., Grabosky, P.: The psychology of fraud. In: Trends and Issues in Crime and Criminal Justice. Australian Institute of Criminology, vol. 199 (2001)Google Scholar
  14. 14.
    Easley, D., Kleinberg, J.: Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge (2010) University PressGoogle Scholar
  15. 15.
    Erdös, P., Rényi, A.: On random graphs. Publ. Mat. Debrecen 6 (1959)Google Scholar
  16. 16.
    Erdös, P., Rényi, A.: On the Evolution of Random Graphs. Publ. Math. Inst. Hung. Acad. Sci. 5 (1960)Google Scholar
  17. 17.
    Erdös, P., Rényi, A.: On the strength of connectedness of a random graph. Acta. Math. Acad, Sci. Hungar 12 (1961)Google Scholar
  18. 18.
    Fawcett, T., Provost, F.: Adaptive Fraud Detection. Data Min. Knowl. Discov. 13(3), 291–316 (1997)Google Scholar
  19. 19.
    Freeman, L.C.: The Development of Social Network Analysis: A Study in The Sociology of Science. Empirical Press, Vancouver, BC (2004)Google Scholar
  20. 20.
    Fuller, R., Carlsson, C.: Fuzzy Reasoning in Decision Making and Optimization. Physica-Verlag, Heidelberg, Germany (2002)zbMATHGoogle Scholar
  21. 21.
    Giles, D.E.: Benford’s law and naturally occurring prices in certain ebay auctions. App. Econ. Lett. 14, 157–161 (2007)Google Scholar
  22. 22.
    Grabosky, P., Duffield, G.: Red flags of fraud. In: Trends and Issues in Crime and Criminal Justice. Australian Institute of Criminology (2001)Google Scholar
  23. 23.
    Hand, D.: Statistical Techniques for Fraud Detection, Prevention, and Evaluation. Paper presented at the NATO ASI: Mining Massive Data sets for Security, London, England Sept 2007Google Scholar
  24. 24.
    Hill, T.P.: The significant-digit phenomenon. Am. Math. Monthly 102, 322–327 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Hampel, R., Wagenknecht, M., Chaker, N. (eds.): Fuzzy Control: Theory and Practice. Physica-Verlag, Heidelberg, Germany (2000)Google Scholar
  26. 26.
    Jamain, A.: Benford’s Law. Imperial College, London (2001)Google Scholar
  27. 27.
    Jaynes, E.T.: Probability theory. In: The logic of science. Cambridge University Press, Cambridge (2003)Google Scholar
  28. 28.
    Jackson, M.O.: Social and Economic Networks. Princeton University Press, Princeton, NJ (2010)zbMATHGoogle Scholar
  29. 29.
    Jennings, C.R., Poston, R.J.: Global Business Fraud and the Law: Preventing and Remedying Fraud and Corruption (May 2016 Edition). Practising Law Institute (PLI), Amazon Digital Services LLC (2016)Google Scholar
  30. 30.
    Jensen, F., Nielsen, T.: Bayesian Networks and Decision Graphs. Springer, New York (2007)CrossRefzbMATHGoogle Scholar
  31. 31.
    Kjarluff, U., Madsen, A.: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis. Springer, New York (2013)Google Scholar
  32. 32.
    Klepac, G., Kopal, R., Mršić, L.: Developing Churn Models Using Data Mining Techniques and Social Network Analysis, pp. 1–308. IGI Global, Hershey, PA (2015). https://doi.org/10.4018/978-1-4666-6288-9
  33. 33.
    Klepac, G.: Handbook of research on advanced hybrid intelligent techniques and applications. In: Bhattacharyya, S., Banerjee, P., Majumdar, D., Dutta, P. (eds.) Discovering Behavioural Patterns within Customer Population by using Temporal Data Subsets, Hershey, PA, p. 321–348 (2016)Google Scholar
  34. 34.
    Klepac, G., Kopal, R., Mršić, L.: Hybrid soft computing approaches. In: Bhattacharyya, S., Dutta, P., Chakraborty, S. (eds.) REFII Model as a Base for Data Mining Techniques Hybridization with Purpose of Time Series Pattern Recognition, p. 237–270. Springer India (2015)Google Scholar
  35. 35.
    Krambia, K.M.: Corporate Fraud and Corruption: A Holistic Approach to Preventing Financial Crises. Palgrave Macmillan (2016). ISBN 978-1349680818Google Scholar
  36. 36.
    Lauritzen, S.L., Nilsson, D.: Representing and solving decision problems with limited information. Manage. Sci. 47, 1238–1251 (2001)Google Scholar
  37. 37.
    Leonides, C.: Fuzzy Logic and Expert Systems Applications. Academic Press, New York (1998)Google Scholar
  38. 38.
    Milgram, S.: The small-world problem. Psychol. Today 1(1), 61–67 (1967)Google Scholar
  39. 39.
    Moreno, J.L.: Sociometry, Experimental Method, and the Science of Society. Beacon House, Ambler, PA (1951)Google Scholar
  40. 40.
    Newcomb, S.: Note on the frequency of use of the different digits in natural numbers. Am. J. Math. 4, 39–40 (1881)Google Scholar
  41. 41.
    Nigrini, M.: A taxpayer compliance application of Benford’s Law. J. Am. Tax. Assoc. 18, 72–91 (1996)Google Scholar
  42. 42.
    Pedrycz, W., Gomide, F.: Fuzzy Systems Engineering: Toward Human-Centric Computing. Wiley-IEEE Press (2007)Google Scholar
  43. 43.
    Pinheiro, C.A.R.: Social Network Analysis in Telecommunications. Wiley, Hoboken, NJ (2011)Google Scholar
  44. 44.
    Raimi, R.A.: The first digit phenomenon. Am. Math. Monthly 83, 521–538 (1976)MathSciNetCrossRefzbMATHGoogle Scholar
  45. 45.
    Remer, R.: Chaos theory links to morenean theory: a synergistic relationship. J. Group Psychother. Psychodrama Sociom. 59 (2006)Google Scholar
  46. 46.
    Roselle, B.E.: The Fraud Factor: Recognize It. Overcome It. Leader Press (2016). ISBN 978-0978564629Google Scholar
  47. 47.
    Simmel, G.: How is society possible? In: Levine, D. (ed.) On Individuality and Social Forms Univevrsity of Chicago Press, Chicago, IL (1908/1971)Google Scholar
  48. 48.
    Scott, J.: Social Network Analysis: A Handbook. Sage Publications, London (1987)Google Scholar
  49. 49.
    Spann, D.D.: Fraud Analytics: Strategies and Methods for Detection and Prevention, Wiley (2014)Google Scholar
  50. 50.
    Ward, J., Peppard, J.: The Strategic Management of Information Systems: Building a Digital Strategy. Wiley (2016). ISBN 978-0470034675Google Scholar
  51. 51.
    Zadeh, L.A., Kacprzyk, J. (eds.): Fuzzy Logic for the Management of Uncertainty. Wiley, New York (1992)Google Scholar
  52. 52.
    Zhang, M.: Social network analysis: history, concepts, and research. In: Fuhrt, B. (ed.) Handbook of Social Network Technologies and Applications, pp. 3–22. Springer, New York, NY (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Raiffeisen Bank AustriaZagrebCroatia

Personalised recommendations