Loading Now

Alt Text: Enhancing AML Compliance using NICE Actimize
Title: Enhancing Anti-Money Laundering (AML) Compliance using NICE Actimize: A Data-driven Approach
Caption: Enhancing AML compliance with data-driven approaches using NICE Actimize
Description: This paper explores the use of NICE Actimize for enhancing AML compliance through AI-driven approaches.

International Journal of Computer Techniques – Volume 11 Issue 2, April 2024

Santosh Kumar Vududala
Independent Researcher
Email: Sanqa19@gmail.com

Abstract

The global financial system is seriously threatened by money laundering, which makes illegal acts like corruption, fraud, and the funding of terrorism possible. Financial institutions use Anti-Money Laundering (AML) systems that track and identify questionable activities in order to mitigate these risks. Nice Actimize is a top AML technology that uses behavioral analytics, machine learning, and artificial intelligence (AI) to improve fraud detection and adherence to international regulatory standards, including the Financial Action Task Force (FATF) guidelines and the Bank Secrecy Act (BSA). The main features of Actimize’s AML architecture, such as watchlist screening, customer due diligence (CDD), real-time transaction monitoring, and case management, are examined in this paper. It also looks at Actimize’s benefits for lowering false positives, enhancing operational effectiveness, and guaranteeing regulatory compliance. Notwithstanding its efficacy, issues like complicated data integration and expensive implementation costs continue to exist. The paper ends with a discussion of upcoming developments in AI-driven AML systems, highlighting how blockchain surveillance and predictive analytics might improve financial security.

Keywords

Anti-Money Laundering (AML), Financial crime prevention, Money laundering detection, Financial fraud prevention, Compliance monitoring, Regulatory compliance, Risk-based approach.

References

  1. Levi, M., & Reuter, P. (2022). “Money Laundering and Its Regulation.” Annual Review of Criminology, pp. 5, 101–120.
  2. Zhang, Y., & Li, X. (2022). “Cloud Computing and Anti-Money Laundering: A New Paradigm for Financial Services.” Journal of Cloud Computing in Financial Services, 28(3), 341–359.
  3. Nguyen, T., & Ho, A. (2022). “The Future of Anti-Money Laundering: Leveraging AI and Machine Learning.” Journal of Financial Crime, 39(1), 55-73.
  4. Deloitte. (2022). The Role of AI in AML Compliance. Retrieved from www2.deloitte.com.
  5. Omar, M., Yusuf, A., & Khan, R. (2023). “AI in Financial Crime Prevention: Advances and Challenges.” International Journal of Financial Services Management, 21(3), 112-130.
  6. KYC360. (2021). AI-Driven AML Strategies: Reducing False Positives. Retrieved from www.kyc360.com.
  7. NICE Actimize. (2021). Case Study: IDB Bank’s AML Transformation with NICE Actimize. Retrieved from www.niceactimize.com.
  8. PwC. (2023). Emerging Technologies in AML Compliance. PricewaterhouseCoopers Report.
  9. Omar, S., Zhang, Y., & Li, X. (2023). “Deep Learning Approaches for Anti-Money Laundering: Opportunities and Challenges.” Journal of Financial Technology and Innovation, 5(3), 299–312.
  10. Zhang, Y., & Li, Q. (2022). “Scalability and Security in Cloud-Based Anti-Money Laundering Solutions.” Journal of Information Systems Management, 28(4), 389–403.
  11. Alsulwailem, Alhanouf Abdulrahman Saleh, and Abdul Khader Jilani Saudagar. “Anti-money laundering systems: A systematic literature review.” Journal of Money Laundering Control 23, no. 4 (2020): 833-848.
  12. D. V. Kute, B. Pradhan, N. Shukla and A. Alamri, “Deep Learning and Explainable Artificial Intelligence Techniques Applied for Detecting Money Laundering – A Critical Review,” in IEEE Access, vol. 9, pp. 82300-82317, 2021.
  13. Han, J., Huang, Y., Liu, S. et al. Artificial intelligence for anti-money laundering: a review and extension. Digit Finance 2, 211–239 (2020).
  14. M. E. Lokanan, “Data mining for statistical analysis of money laundering transactions”, J. Money Laundering Control, vol. 22, no. 4, pp. 753-763, Oct. 2019.

How to Cite

Santosh Kumar Vududala, “Enhancing Anti-Money Laundering (AML) Compliance using NICE Actimize: A Data-driven Approach,” International Journal of Computer Techniques, Volume 11 Issue 2, April 2024. ISSN 2394-2231.

Opportunities to Collaborate

We invite you to become part of our esteemed reviewer board or submit your research paper for publication.

Join Our Reviewer Board Submit Your Research Paper
Enhancing Anti-Money Laundering (AML) Compliance using NICE Actimize A Data driven ApproachDownload