Skip to main content
SpringerLink
Log in

Importance of Self-Learning Algorithms for Fraud Detection Under Concept Drift

  • Conference paper
  • First Online:
International Conference on Artificial Intelligence and Sustainable Engineering

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 837))

  • 320 Accesses

Abstract

Fraud detection has been a difficult problem in the industry for the past many years which has caused a massive financial loss for individuals/organizations. Machine learning techniques have proved to be an efficient technique to identify and detect fraud. The major problem which exists in this domain is “Concept drift”. Fraudsters tend to evolve their habits over time which eventually leads to a pattern change. Machine learning models normally depend on the reliable set of labels which classifies whether the transaction is fraud or legitimate. When there happens a change in the patterns, the model tends to lose its performance in predicting new patterns. A model which adapt to this changing behaviour is inevitable in such scenarios. In this paper, we have reviewed several articles which discusses the problem of concept drift in fraud detection, and we have also surveyed different types of methods and techniques used so far by the researchers to deal with  it. Above that the paper also proposes a procedure and steps to make a machine learning model adaptive.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Laleh N, Abdollahi Azgomi M (2009) A taxonomy of frauds and fraud detection techniques. Commun Comput Inf Sci 31:256–267

    Google Scholar 

  2. Li J, Huang K-Y, Jin J, Shi J (2008) A survey on statistical methods for health care fraud detection. Health Care Manag Sci 11(3):275–287

    Article  Google Scholar 

  3. Bolton RJ, Hand DJ, Provost F, Breiman L, Bolton RJ, Hand DJ (2002) Statistical fraud detection: a review. Stat Sci 17(3):235–255

    Article  MathSciNet  Google Scholar 

  4. Gama J, Zliobaite I, Bifet A, Pechenizkiy M, Bouchachia A (2014) “A survey on concept drift adaptation,” ACM Comput Surv 46(4)

    Google Scholar 

  5. Žliobaitė I (2010) “Learning under concept drift: an overview,” Tech Report Fac Math Informatics, 1–36

    Google Scholar 

  6. Schoen C et al (2012) A survey of primary care doctors in ten countries shows progress in use of health information technology, less in other areas. Health Aff 31(12):2805–2816

    Article  Google Scholar 

  7. Sahin Y, Duman E (2011) “IMECS2011_pp442–447,” I

    Google Scholar 

  8. Control F, Industry HC (1996) “National Institute of Justice”

    Google Scholar 

  9. Khamassi I, Moez MS (2015) “Self-adaptive windowing approach for handling complex concept drift”. Cogn Comput, Springer, 7:772–790

    Google Scholar 

  10. Schlimmer JC, Granger RH (1986) Incremental learning from noisy data. Mach Learn 1(3):317–354

    Google Scholar 

  11. Widmer G (1996) Learning in the presence of concept drift and hidden contexts. Mach Learn 23(1):69–101

    MathSciNet  Google Scholar 

  12. Salganicoff M (1997) Tolerating concept and sampling shift in lazy learning using prediction error context switching. Artif Intell Rev 11(1–5):133–155

    Article  Google Scholar 

  13. Nick Street W, Kim YS (2001) “A streaming ensemble algorithm (SEA) for large-scale classification,” Proc Seventh ACM SIGKDD Int Conf Knowl Discov Data Min no. June, pp 377–382

    Google Scholar 

  14. Kolter JZ, Maloof MA (2007) Dynamic weighted majority: an ensemble method for drifting concepts. J Mach Learn Res 8:2755–2790

    MATH  Google Scholar 

  15. Tsymbal A (2004) “The problem of concept drift: definitions and related work,” Comput Sci Dep Trinity Coll Dublin, no. May

    Google Scholar 

  16. Jog A, Chandavale AA (2018) Implementation of credit card fraud detection system with concept drifts adaptation. Adv Intell Syst Comput 673:467–477

    Google Scholar 

  17. Ghomeshi H, Gaber MM, Kovalchuk Y (2019) EACD: evolutionary adaptation to concept drifts in data streams. Data Min Knowl Discov 33(3):663–694

    Article  Google Scholar 

  18. Sun Y, Wang Z, Bai Y, Dai H, Nahavandi S (2018) “A classifier graph-based recurring concept detection and prediction approach.” Comput Intell Neurosci 2018

    Google Scholar 

  19. Harel M, Crammer K, El-Yaniv R, Mannor S (2014) “Concept drift detection through resampling,” 31st Int Conf Mach Learn. ICML 2014, 3:2682–2694

    Google Scholar 

  20. Priya S, Uthra RA (2020) “Comprehensive analysis for class imbalance data with concept drift using ensemble-based classification.” J Ambient Intell Humaniz Comput 0123456789

    Google Scholar 

  21. Khamassi I, Sayed-Mouchaweh M, Hammami M, Ghédira K (2013) “Ensemble classifiers for drift detection and monitoring in dynamical environments,” PHM 2013—Proc Annu Conf Progn Heal Manag Soc, 199–212

    Google Scholar 

  22. Dietterich TG (2000) “Ensemble methods in machine learning,” Lect Notes Comput Sci (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 1857 LNCS, 1–15

    Google Scholar 

  23. Scholz M, Klinkenberg R (2014) “An ensemble classifier for drifting concepts an ensemble classifier for drifting concepts,” no. May

    Google Scholar 

  24. Krawczyk B, Minku LL, Gama J, Stefanowski J, Woźniak M (2017) Ensemble learning for data stream analysis: a survey. Inf Fusion 37:132–156

    Article  Google Scholar 

  25. Escovedo T, Da Cruz AVA, Vellasco MMBR, Koshiyama AS (2013) Learning under concept drift using a neuro-evolutionary ensemble. Int J Comput Intell Appl 12(4):1–16

    Article  Google Scholar 

  26. Silva B, Marques N, Panosso G (2012) Applying neural networks for concept drift detection in financial markets. CEUR Workshop Proc 960:43–47

    Google Scholar 

  27. Najafi M, “Incremental learning of ANN in the presence of concept drift”

    Google Scholar 

  28. e outros MGA, Castillo PA (2001) “Artificial neural networks design using evolutionary algorithms”

    Google Scholar 

  29. Widrow B, Lehr MA (1993) Adaptive neural networks and their applications. Int J Intell Syst 8(4):453–507

    Article  Google Scholar 

  30. Fortin FA, De Rainville FM, Gardner MA, Parizeau M, Gagńe C (2012) DEAP: evolutionary algorithms made easy. J Mach Learn Res 13:2171–2175

    MathSciNet  Google Scholar 

  31. Padmalatha E, Reddy CRK, Padmaja B (2015) Classification of concept-drifting data streams using optimized genetic algorithm. Int J Comput Appl 125(15):1–6

    Google Scholar 

  32. Smith M, Ciesielski V (2016) “Adapting to concept drift with genetic programming for classifying streaming data,” 2016 IEEE Congr Evol Comput CEC 2016, pp 5026–5033

    Google Scholar 

  33. A. N. Sloss and S. Gustafson, “2019 Evolutionary Algorithms Review,” pp. 307–344, 2020.

    Google Scholar 

  34. Pozzolo AD, Boracchi G, Caelen O, Alippi C, Bontempi G“Credit card fraud detection and concept-drift adaptation with delayed supervised information”

    Google Scholar 

  35. Lu F, Boritz JE, Covvey D (2006) Adaptive fraud detection using Benford’s law. Adv Artif Intell Proc 4013:347–358

    MathSciNet  Google Scholar 

  36. Somasundaram A, Reddy S (2019) Parallel and incremental credit card fraud detection model to handle concept drift and data imbalance. Neural Comput Appl 31:3–14

    Article  Google Scholar 

  37. Chen CH, Li Y, Hong TP, Li YK, Lu EHC (2015) “A GA-based approach for mining membership functions and concept-drift patterns,” 2015 IEEE Congr Evol Comput CEC 2015—Proc. no. May, pp 2961–2965

    Google Scholar 

  38. Lu N, Zhang G, Lu J (2014) Concept drift detection via competence models. Artif Intell 209(1):11–28

    Article  MathSciNet  Google Scholar 

  39. Elwell R, Polikar R (2011) Incremental learning of concept drift in nonstationary environments. IEEE Trans Neural Networks 22(10):1517–1531

    Article  Google Scholar 

  40. Mao H, Liu Y (2018) “Adaptive fraud detection system using dynamic risk features,” no. October

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. M.C.A., VTU Belgavi, CMR Institute of Technology, Bangalore, India

    S. Kotekani Shamitha

  2. M.C.A, CMR Institute of Technology, Bangalore, India

    V. Ilango

Authors
  1. S. Kotekani Shamitha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Ilango
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. National Institute of Technology Durgapur, Durgapur, West Bengal, India

    Goutam Sanyal

  2. University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Carlos M. Travieso-González

  3. Department of Computer Science and Engineering, G. L. Bajaj Institute of Technology and Management, Greater Noida, India

    Shashank Awasthi

  4. School of Engineering, Polytechnic of Porto, University of Porto, Porto, Portugal

    Carla M.A. Pinto

  5. Department of Planning and Development, National Institute of Technology Goa, Goa, India

    B. R. Purushothama

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shamitha, S.K., Ilango, V. (2022). Importance of Self-Learning Algorithms for Fraud Detection Under Concept Drift. In: Sanyal, G., Travieso-González, C.M., Awasthi, S., Pinto, C.M., Purushothama, B.R. (eds) International Conference on Artificial Intelligence and Sustainable Engineering. Lecture Notes in Electrical Engineering, vol 837. Springer, Singapore. https://doi.org/10.1007/978-981-16-8546-0_28

Download citation

Publish with us

Policies and ethics

Search

Navigation