Money पैसा anti-money
As artificial intelligence (AI) progresses in development, the utilization of AI increases. One way AI is expanding is in the world of money laundering detection. Due to this expansion, the creation of anti-money laundering (AML) AI has risen. AML AI is currently being used by banks to detect fraudulent transactions. However, while many factors affirm the success of AML AI, there have been flaws, such as inconsistent fraud detection. This has led many to wonder why anti-money laundering article intelligence still misses fraud.
Money laundering is not a new issue, as it has existed for centuries. Although there is no exact date as to when money laundering began, a research article published by the Financial Crime Academy (2024) titled The History of Money Laundering explains that the tactic started almost 2000 years ago when ““ancient chinese merchants started to “clean” their profits as a way to overcome regional trading bans” (Financial Crime Academy, 2024). The practice continues to this day and is currently one of the most common financial crimes. The term “money laundering” can be defined as “the process of running illegal proceeds through a supposedly legitimate business, to turn “dirty” money into “clean” money”.
Money Laundering presents illegally acquired money as valid, a concept that is elaborated on in this journal article written by researcher Michael Levi (2002) titled Money Laundering and Its Regulations, ” which explains that laundering mainly impacts the “financial system” (Levi, 2002), which includes banks, financial services, and insurance companies. Furthermore, it creates “serious reputable risks” (Levi, 2002) and increases crime rates all over the world.
Levi argues that without a change to money laundering, the economy will not be able to survive, and the History of money laundering agrees, as they explain that “800 million to 2 trillion dollars is laundered each year thanks to money laundering” (Financial Crime Academy, 2024). While many solutions have been proposed, banking institutions have turned toward the rise of AI in an attempt to identify a way to decrease money laundering.
Unlike money laundering, artificial intelligence is not a concept that has been around for thousands of years. It is a fairly new technology and is constantly being improved day by day. As explained in a research article from Lawrence Livermore National Laboratory (2024) titled The Birth of Artificial Intelligence, AI was first created in the early 1950s and continued to grow and expand to the technology we know it as today (Lawrence Livermore National Laboratory, 2024).
The term “artificial intelligence” is defined as “a program that partly acts like a human.” This human-like technology is capable of possessing immaculate problem-solving skills. As illustrated in a journal article published by researchers Michael Levi and Peter Reuter (2006), titled Money Laundering, these features caused AI to begin to be mixed with AML technologies to create a software or program that is able to detect fraudulent transfers of money and activity (Levi & Reuter, 2006). Furthermore, adding to Levi and Reuter, a journal article by the researchers Elizabeth Rosenburg et al (2019), titled Financial Technology and National Security, explains that in order for these programs to work, AML AI must find “patterns in large amounts of data” (Rosenburg et al, 2019). These technologies are also great resources for banks and other government officials (Rosenburg et al, 2019).
Additionally, a research article titled Using artificial intelligence to combat money laundering published by Scientific Research Publishing (2024) agrees with Rosenberg on the fact that these programs are beneficial to all kinds of institutions, including banks, since they experience some of the largest cases of money laundering fraud (Scientific Research Publishing, 2024).
For the most part, older methods of money laundering are easily detected by anti-money laundering AI. For example, criminals are less likely to launder cash as it is easily detected, so many use online banking (Levi, 2002). Emily Sachs, an AML analyst at two major financial institutions and the author of a research article titled How Machine Learning can Prevent Money Laundering, adds on to Levis’ claim, explaining that using AML AI is more cost-effective and it is easier to adapt to changing conditions (Sachs, 2023). However, this AI technology does not always produce accurate outputs for newer methods of money laundering, as it doesn’t always detect fraudulent activity.
For example, Mikhail Reider-Gordon (2011), a researcher who published a research journal article titled US and International Anti-money Laundering Developments, argues that many criminals stole citizens’ identities to bypass AML AI (Reider-Gordon, 2011). Reider-Gordon does agree with Levi that using more AML AI leads to an improvement, but he expresses concern for civilian safety through the development process, as “criminals used the stolen identities to smuggle and steal drugs” (Reider-Gordon, 2011). It is important to continue to crack down on the issues of money laundering to ensure the safety and security of locals and citizens.
AML AI technology is not limited to a specific region; in fact, it is found all around the world. Peter Reuter (2024), a professor at the University of Maryland, specializes in finance and economics, explains in his research article titled Policy Professor’s International Study Reveals Weaknesses in Global Efforts to Combat Money Laundering that, “money laundering is an annoying threat that impacts economies worldwide” (Reuter, 2024). He conducted a study that concluded that many countries are not taking this issue seriously (Reuter, 2024).
However, Donna Achimov (2024) of FRNTRAC (a Canadian anti-money laundering company) and author of the research article titled Canada’s anti-money laundering body to leverage AI challenges Reuters results, declares that countries like Canada care about money laundering, as recently two of the three biggest banks were fined $9 million for suspicious transaction reports (Balu, 2024). Though these transitions were caught, it took many months and resources to identify them. However, by identifying a specific KPI in AML AI that is causing this yield to improve, more personalized solutions can be proposed, which can cut down the catching time.
The term KPI, as expressed by Penn State University, is defined as “a measure of performance that focuses the organization’s attention on what matters most for the success of its objectives” (KPI and Metrics). There are not a lot of identified lagging KPIs in AML AI, as the technology is still fairly new. However, a blog post titled Artificial Intelligence and Anti-Money Laundering by a team of researchers called Sanction Scanners explains that errors are normally caused when the AI is outdated, and identifies a lack of AML
AI development as a lagging KPI (Sanction Scanner, 2024). Furthermore, A research article published by J. Montgomery titled An AML case study in failure expresses that successfully integrating AML AI into new technological systems and ensuring it adapts properly to change will allow for efficient and accurate outputs. The article further identifies AML AI failed to adapt to change, and poor integration of AML AI with other software as two more lagging KPIs (Montgomery).
In addition to those three KPI, A blog titled 5 reasons why companies fail in AML compliance agrees with Sanction Scanner on the fact that a lack of AML AI development is a lagging KPI. Additionally, the article also explains that monitoring failures and bad data management are two more lagging KPIs (5 reasons why companies fail in AML compliance). Throughout the articles, there hasn’t been any mention of one specific KPI being identified as the most common.
Without the advancements of AML AI, society is blatantly allowing criminals to get away with fraud (Reuter, 2024). For now, many companies have started to rely on translation monitoring (TM), an AI that analyzes translation data and quickly identifies suspicious translations. Sanction Scanners argues that although this approach allows companies to achieve quick output, it requires further investigation, as AI cannot detect fraud (Sanction Scanner, 2024). Doron Goldbarsht, a professor at Cambridge University and the author of a book on money laundering AI titled “Leveraging AI to Mitigate Money Laundering Risks in the Banking System, ” agrees with the information published by the sanction scanner. He proclaims that it is difficult to determine the outcomes of TM, so it would be more reliable to find other, more specific solutions (Goldbarsht, 2023).
Work Cited
Balu, N. (2024, January 8). Canada’s anti-money laundering body to leverage AI bets to crack down
Down on bad actors. Reuters. Retrieved September 7, 2025, from https://www.reuters.com/world/americas/canadas-anti-money-laundering-body-leverage-ai-bets-crack-down-bad-actors-2024-01-08/
The birth of Artificial Intelligence (AI) research | Science and Technology. (n.d.). Science and
Technology. Retrieved September 6, 2025, from https://st.llnl.gov/news/look-back/birth-artificial-intelligence-ai-research
Blog / Artificial Intelligence and Anti-Money Laundering. (2024, September 17). Sanction Scanner. Retrieved September 7, 2025, from
5 reasons why companies fail in AML compliance. Sanctions Lists & PEP Screening for AML
Compliance. (n.d.). https://www.sanctions.io/blog/why-companies-fail-on-aml-compliance
Goldbarsht, D. (2023). Leveraging AI to Mitigate Money Laundering Risks in the Banking System. In Z. Bednarz & M. Zalnieriute (Eds.), Money,
Power, and AI: Automated Banks and Automated States (pp. 51–69). Chapter, Cambridge: Cambridge University Press.
system/D472CACE4B016A1F1855B1596F988EEE
The History Of Money Laundering: Know The Important Origin Of Money Laundering. (2024, August 5). Financial Crime Academy. Retrieved
September 5, 2025, from
KPI and Metrics | Office of Planning, Assessment, and Institutional Research. (n.d.). Office of Planning, Assessment, and Institutional Research.
Retrieved September 3, 2025, from https://opair.psu.edu/planning-handbook/kpi-metrics/
Levi, M. (2002). Money Laundering and Its Regulation. The Annals of the American Academy Of Political and Social Science, 582, 181–194.
Levi, M., & Reuter, P. (2006). Money Laundering. Crime and Justice, 34(1), 289–375.
Montgomery, J. (2025, December 5). An AML case study in failure: How TD Bank’s choices opened floodgates to fincrime. AML Partners.
Policy Professor’s International Study Reveals Weaknesses in Global Efforts to Combat Money Laundering | UMD School of Public Policy. (2024,
May 28). UMD School of Public Policy. Retrieved September 7, 2025, from https://spp.umd.edu/news/policy-professors-international-study-
reveals-weaknesses-global-efforts-combat-money
Reider-Gordon, M. (2011). U.S. and International Anti-Money Laundering Developments. The International Lawyer, 45(1), 365–379.
Rosenberg, E., Harrell, P. E., Shiffman, G. M., & Dorshimer, S. (2019). Financial Technology And National Security. Center for a New American
Security.
Sachs, E. (2023, August 11). AML & AI | How Machine Learning Can Prevent Money Laundering. Persona. Retrieved September 7, 2025, from
Using Artificial Intelligence to Combat Money Laundering.” n.d. Scientific Research Publishing. Accessed September 7, 2025.
Authored By: Grace Singh
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