Skip to main content
Springer Nature Link
Log in

Trust the Machine and Embrace Artificial Intelligence (AI) to Combat Money Laundering Activities

  • Chapter
  • First Online:
Computational Intelligence for Modern Business Systems

  • 522 Accesses

Abstract

The research work is focused on examining the role of artificial intelligence (AI) in addressing challenges associated with money laundering in the banking sector. Money laundering is a global issue that threatens financial stability and international security, making anti-money laundering research crucial. Furthermore, just 0.2% of money laundered through the financial system is estimated to be seized. The crime is growing increasingly sophisticated and intricate, and the amount of the crime increases banks’ vulnerability. Researchers have begun to investigate the possibility of artificial intelligence approaches in this setting. However, a thorough assessment has identified a systematic knowledge deficit that systematically examines and synthesizes artificial intelligence techniques for anti-money laundering efforts in the banking industry. Therefore, this chapter is focused on a systematic review of key technologies categorized into artificial intelligence or machine learning (AI or ML), natural language processing (NLP), robotic process automation (RPA), and cloud-based solutions. However, various challenges concerned with these techniques, such as data quality, the nature of money laundering and data volume, and data heterogeneity, are also discussed. As a result, the findings add to the total knowledge base in anti-money laundering from the banking sector’s perspective. Additionally, future study directions were narrowed even further based on the limitations discovered.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
eBook
USD 129.00
Price excludes VAT (USA)
Hardcover Book
USD 169.99
Price excludes VAT (USA)

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Wronka C (2021) Impact of COVID-19 on financial institutions: navigating the global emerging patterns of financial crime. J. Finance Crime 2(1):1–3

    Google Scholar 

  2. United Nations (2021). Overview. [Online] United Nations: Office on Drugs and Crime. Available at: https://www.unodc.org/unodc/en/money-laundering/overview.html. Accessed 13 December 2021

  3. NCA (2019) National Economic Crime Centre leads push to identify money laundering activity. [Online] Nationalcrimeagency.gov.uk. Available at: https://www.nationalcrimeagency.gov.uk/news/national-economic-crime-centre-leads-push-to-identify-money-laundering-activity. Accessed 13 December 2021

  4. Pai S (2021) Anti-money laundering: current state of play. Governance Directions 73(2):56–60

    Google Scholar 

  5. Gottschalk P (2020) Sweden: Swedbank money laundering. In: Corporate responses to financial crime, pp 125–130. Springer, Cham, pp 2–19

    Google Scholar 

  6. Ravenstijn B (2000) The Netherlands: anti money-laundering programmes—the case of ING. J Money Laundering Control 3(2):6–16

    Google Scholar 

  7. Rose KJ (2020) De-risking or recontracting–the risk dilemma of EU money laundering regulation. J Risk Finance 3(1):3–6

    Google Scholar 

  8. Schneider F, Windischbauer U (2008) Money laundering: some facts. Eur J Law Econ 26(3):387–404

    Article  Google Scholar 

  9. Masciandaro D (1999) Money laundering: the economics of regulation. Eur J Law Econ 7(3):225–240

    Article  Google Scholar 

  10. Alexander K (2001) The international anti-money-laundering regime: the role of the financial action task force. J Money Laundering Control 1(2):1–12

    Google Scholar 

  11. Tyre C (2010) Anti-money laundering legislation: implementation of the FATF forty recommendations in the European Union. J Prof Law, 69

    Google Scholar 

  12. Force FAT (2001) Nine special recommendations on terrorist financing. Paris: FATF/GAFI, October, www.fatf-gafi.org/dataoecd/55/16/34266142.Pdf

  13. Han J, Huang Y, Liu S, Towey K (2020) Artificial intelligence for anti-money laundering: a review and extension. Digital Finance 2(3):211–239

    Article  Google Scholar 

  14. Helmy THE, zaki Abd-ElMegied M, Sobh TS, Badran KMS (2014) Design of a monitor for detecting money laundering and terrorist financing. Int J Comput Netw Appl 1(1):15–25

    Google Scholar 

  15. Le Khac NA, Markos S, Kechadi MT (2010) A data mining-based solution for detecting suspicious money laundering cases in an investment bank. In: 2010 Second International conference on advances in databases, knowledge, and data applications, pp 235–240. IEEE

    Google Scholar 

  16. White T (2009) Hadoop: the definitive guide. 1st edn, OReilly Media

    Google Scholar 

  17. Çeku SO, Kutllovci SS, Emini SA, Petrit S (2019) Money laundering as a form of economic criminality–the case of the Republic of Kosovo. Transactions 12(3)

    Google Scholar 

  18. Aziz S, Dowling M (2019) Machine learning and AI for risk management. In: Disrupting finance Palgrave Pivot, Cham, pp 33–50

    Google Scholar 

  19. Butler, T. and O’Brien, L., 2019. Understanding RegTech for digital regulatory compliance. In Disrupting Finance. Palgrave Pivot, Cham, pp. 85–102.

    Google Scholar 

  20. Arner DW, Barberis J, Buckey RP (2016) FinTech, RegTech, and the reconceptualization of financial regulation. Nw J Int’l L Bus 37:371

    Google Scholar 

  21. Bektenova GS (2018) Are Regtech, Fintech, Blockchain the Future?. KnE Soc Sci, 61–67

    Google Scholar 

  22. Grasshoff G, Gehra B, Villafranca V, Gittfried N, Grataloup V, Hefter K, Leiendecker J, Pauly O (2017) Transforming bank compliance with smart technologies

    Google Scholar 

  23. Gyamfi NK, Abdulai JD (2018) November. Bank fraud detection using support vector machine. In: 2018 IEEE 9th annual information technology, electronics and mobile communication conference (IEMCON), pp 37–41. IEEE

    Google Scholar 

  24. Heidarinia N, Harounabadi A, Sadeghzadeh M (2014) An intelligent anti-money laundering method for detecting risky users in the banking systems. Int J Comput Appl 97(22):1–14

    Google Scholar 

  25. Zhang Z, Salerno JJ, Yu PS (2003) Applying data mining in investigating money laundering crimes. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp 747–752

    Google Scholar 

  26. Gara M, Manaresi F, Marchetti DJ, Marinucci M (2019) The impact of anti-money laundering oversight on banks’ suspicious transaction reporting: evidence from Italy. Bank of Italy Occasional Paper, (491)

    Google Scholar 

  27. Geetha T, Malini A, Indhumathi M (2020) Robotic process automation. Int J Comput Techniques 7(5):1–12

    Google Scholar 

  28. Bostel A (2021) Building better transaction monitoring for anti-money laundering. J Financial Compliance 4(4):313–335

    Google Scholar 

  29. Palshikar GK, Apte M, Baskaran S (2014) Analytics for detection of money laundering. In: TCS technical architects conference (TACTiCS 2014) Affilication: tata consultancy services limited

    Google Scholar 

  30. Le Khac NA, Markos S, O’neill M, Brabazon A, Kechadi MT (2009) An investigation into data mining approaches for anti money laundering. In: Proceedings of international conference on computer engineering and applications (ICCEA 2009)

    Google Scholar 

  31. Romero Ugarte JL (2018) Distributed ledger technology (DLT): introduction. Banco de Espana Article 19:18

    Google Scholar 

  32. Zhu H, Zhou ZZ (2016) Analysis and outlook of applications of blockchain technology to equity crowdfunding in China. Financial Innov 2(1):1–11

    Article  Google Scholar 

  33. Kerimov A, Koibichuk V, Mynenko S (2020) Blockchain technology in bank’s anti-money laundering. Economic and social development: book of proceedings, pp 874–883

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Technology Editor, Cointelegraph, UK

    Guneet Kaur

Authors
  1. Guneet Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guneet Kaur .

Editor information

Editors and Affiliations

  1. LBEF Campus, Kathmandu, Nepal

    Sandeep Kautish

  2. Department of Mechanical Engineering, MCKV Institute of Engineering, Howrah, West Bengal, India

    Prasenjit Chatterjee

  3. Faculty of Organizational Sciences, Department of Operations Research and Statistics, University of Belgrade, Belgrade, Serbia

    Dragan Pamucar

  4. Department of Computer Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, India

    N. Pradeep

  5. Symbiosis Centre for Management Studies, Symbiosis International (Deemed University), Pune, India

    Deepmala Singh

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kaur, G. (2024). Trust the Machine and Embrace Artificial Intelligence (AI) to Combat Money Laundering Activities. In: Kautish, S., Chatterjee, P., Pamucar, D., Pradeep, N., Singh, D. (eds) Computational Intelligence for Modern Business Systems . Disruptive Technologies and Digital Transformations for Society 5.0. Springer, Singapore. https://doi.org/10.1007/978-981-99-5354-7_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-5354-7_4

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-5353-0

  • Online ISBN: 978-981-99-5354-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation