Money Mule Risk Assessment: An Introductory Guidance for Financial Crime Compliance Officers
Mohd Irwan Abdul Rani1*, Salwa Zolkaflil1, Sharifah Nazatul Faiza Syed Mustapha Nazri2
1 Accounting Research Institute, Universiti Teknologi MARA, Shah Alam, Malaysia
2 Faculty of Accountancy, Universiti Teknologi MARA, Shah Alam, Malaysia
*Corresponding Author: [email protected]
Accepted: 15 March 2022 | Published: 1 April 2022 DOI:https://doi.org/10.55057/ajrbm.2022.4.1.17
_____________________________________________________________________________________________
Abstract: Money mule phenomenon is a global threat that inflicts the financial system. It is one of money laundering concerns affecting the banking institutions and require enigmatic compliance effort. Criminals such as fraudsters, scammers and cyber attackers use money mules to launder their ill-gotten proceeds. The principal aim of engaging money mule is to hide the money trail and thus stymie any possible effort to investigate the underlying criminal activity. The proceed of fraud or scam is rarely directed to the criminal’s account. Therefore, it is vital for bank’s financial crime compliance officers to be able to perform money mule risk assessment when conducting money mule case investigation. The assessment begins with risk identification from Know Your Customer (KYC) information and transaction monitoring. The output from the money mule risk assessment would be useful in building up Suspicious Transaction Report (STR). This article introduces the concept of money mule risk assessment which would be beneficial to academicians and anti-money laundering practitioners.
Keywords: Money mule, money mule risk assessment, financial crime compliance officers, KYC red flags, transaction monitoring red flags.
_____________________________________________________________________________
1. Introduction
The credibility of financial system will be under threat if money laundering is not properly monitored and deterred. It is reported that the globally almost USD 1.6 to USD 4 trillion is laundered every year through financial system (Weeks-Brown, 2018). Banks such as HSBC, ING Bank and Deutsche Bank were fined by regulators for blatant violation of money laundering regulations (Sundarakani & Ramasamy, 2013; Yeoh, 2020). The fines imposed on financial institutions inevitably reduce their profits and erode their reputations, rendering their branding effort useless (Zeidan, 2013). On another note, repeated fines on banks from money laundering activities drain the motivation of employees to give their best performance.
Criminals are found to sophisticatedly abuse the loopholes in financial system to introduce their ill-gotten funds. The chagrin is made more shocking when it is learned that organized criminal groups collaborate among themselves to launder dirty moneys using banks (Rusanov &
Pudovochkin, 2020). Realizing certain banks are predisposed to slipshod money laundering
control whereupon eagerness to achieve profit target leads to abandonment of compliance or due diligence rules, these criminals become delighted at the prospect of taking this advantage. Some criminals could easily bypass the control by conspiring with unethical bank relationship managers (Wood, 2020). These are proceeds of dirty activities from malicious corruption, environmental destruction and fraud which deprived society from fair economic growth (Mugarura, 2016;
Reboredo, 2013). Uncountable victims were either directly or indirectly afflicted by the crimes that took place behind these money laundering activities.
Fraud and scams are some of the criminal activities whereby the proceeds are actively laundered through financial system. Fraud and scams are closely related to money laundering as they are identified among the commonest predicate offence (Jadavji, 2011). Millions were lost to fraud and scams with these funds could not be recovered from the perpetrators. Less than half of the reported fraud cases were able to recoup the funds stolen from fraudulent activities and cyberattacks (Sutton, 2015). This puts strain to financial institutions which are responsible to compensate the loss experienced by the victims. The sudden evaporation of stolen funds is largely attributed to the actions of money mule account holders who are key actors in layering the ill-gotten proceeds (Mutch, 2020).
2. Background of money mule
Money mule could be an individual or an incorporated entity that helps to transfer illegally acquired money using their own accounts (Esoimeme, 2021; Federal Bureau Investigation, 2021).
Mules are the unwitting accomplices that bridge the wedge between victims and criminals by subjecting their accounts to unlawful conduct (Florêncio & Herley, 2010). These recruited money mules could be doing the illegal transfers with or without the knowledge of the legal consequences.
It is quintessentially vital to understand that these money mule activities are subjected to law although the mules claim to not completely aware to the underlying crime (Abdul Rahman, 2020).
The actual purpose of recruiting money mules by criminals is to utilize the former’s accounts in the process of obfuscating money flow (Hülsse, 2017). The interruption of money trail benefits the criminals by slowing or possibly cease at all the investigation work by enforcement agency. Ergo, it is inexorably important for criminals to hire money mules whose accounts would be used to launder the dirty funds without the fear of being detected (Abdul Rahman, 2020; Barrett, 2021;
Foy, 2021; Hutchings, 2014; Tang, 2020). Multiple networks are founded by fraudsters and criminals to hire money mules with different assigned roles (Nguyen & Luong, 2021).
It is stated that almost 90% of money mule activities are surprisingly discovered to have connection to cybercriminal activities (Esoimeme, 2021). The parsimony of awareness among the recruited money mules on possible abuse by criminals is identified as the main driving factor of the ghastly money mule phenomenon (Hashim & Abdul Rahman, 2020). The first money mule case was reported in Australia back in January 2005, of whom sixty-one money mules were arrested (Dunham, 2006). These money mules were recruited to work for a sham company World Transfers Inc, which the only job scope was to perform fund transfers using their own personal account. The first arrestee was Ryan Naumenko who laundered almost USD 23,000 for Russian Mafias (Howard et al., 2009, pp 42-43). As a finance officer, Naumenko could not sense the hidden phishing activities that profited almost USD 1 million daily. His daily task was to withdraw cash transferred into his account at ANZ branch at Narre Warren, and wired them to St. Petersburg or Latvia. The
laundered funds were clearly identified by authorities as proceeds from cybercrime activities, such as cyber-attack and scam.
In early 2000, the hackers used to attack businesses with intention to deface the latter and bruise their reputation (Hollander, 2000). It is a form of protest and demonstrating discontent when physical expression on mainstream medium is curtailed by authority, or relegated as public aggression. But over the time, the motivation shifted to a more sinister aim which could be engaged to gain monetary reward. The insidious motivation from curiosity and fun to illegal financial gain has reasoned more malware attack (Choo, 2011). The victims can either be general population or business establishments. According to Choo (2011), the trend is inspired by easy access to malware creation using toolkits to build malware. This spurs rampant cybercrime which started making money from selling stolen credentials, such as email and bank accounts. The malware is impregnated with phishing-based keylogger which absorbs user activity. Quietly without consent from the system owner, cyber criminals will be able to access the server or workstations and do their vice works which the technique is called Remote Administration Tools (RATs) (Kondalwar
& Shelke, 2014).
Cybercriminals spam potential victims to log on to fictitious interface that resembles the genuine online banking website (Moore et al., 2009). The victims who often oblivious to deception of phishing email will open it and follow the instruction stated by cybercriminals. Phishing email contains GIF or URL which can be confusing with message that would trigger bank client to quickly use the given link without authenticating the content (Pemble, 2005). This is known to be context aware phishing model, which victim is sent with innocuous emails they are expecting such as campaign emails from the bank (Jakobsson, 2005). It makes victim exercise less caution and provides high chance of success to phishing attacker. Behind this activity, the cybercriminal would copy the credentials and steal funds which would be funneled to money mule account. This is the onset of financial theft in which cybercriminals will log in to the system and pre-empted large fraudulent fund transfer with fabricated OBI (originator-beneficiary information) text (McGlasson, 2010). It is not practical to route the illicit fund from victim’s account to criminal. Instead, intermediary is engaged to wash the illicit funds and conceal the money trail. This is where money mule plays an important role.
The victim’s bank account will be emptied with modification of beneficiaries and transactions information. According to Custers et al., (2019), there are 2 models of laundering proceeds of malware cybercrime. The first model is direct spending of the funds available in the victim’s account. The spending could be focused on luxury goods that have sustained value, or more recently on bitcoin or other virtual currency. The second model enthuses the complex use of money mule which the stolen funds would be diverted to money mule account. The funds could be withdrawn or integrated into criminal’s account as clean money. Cyber-attacks are responsible to almost USD 100 billion losses to global financial industry, and this has prompted financial institutions to beef up their security system (Bernard, 2021). The first step is to engage experts in building comprehensive policy governance framework not only in IT sector, but across all business units and departments (Parthasarathy, 2021). As financial institutions and businesses heavily invest on firewall, system and human trainings, malware attempts become constipated. At this juncture, fraud and scam start to displace malware attack or phishing.
Figure 1: The money mule scenario (source: author)
3. Money mule risk assessment
Banks and financial institutions are subjected to comprehensive risk assessment (CRA) by regulator of jurisdiction where it operates. It is the assessment of risks that the institution has and require adequate attention. The aim is to assess the risk exposure of its business units, the structure of the organization and most importantly controls that are in place to limit the risks (Sahajwala &
Van der Bergh, 2000). The risk assessment covers business factors such as market risk, capital, liabilities, earnings and others that could not be translated into figures such as operation, legal and reputation. Apart from the mentioned inventory of business-related risks, the assessment takes consideration of organization own inherent risk and effectiveness of its controls (Nicolas & May, 2017). Like any modern risk assessment on banks or its portfolios, dataset from bank’s record is examined to determine the risk and identify measures that should be adopted to stem possible exposure (Elsinger, Lehar & Summer, 2006). The principal objective is nothing other than to ensure financial stability of the bank and provide “macroprudential” approach to banking supervision.
Figure 2: Types of banking risks (Sahajwala & Van der Bergh, 2000)
One of many assessments that will be undertaken by regulator or central banker is the preparedness of bank’s financial crime risk assessment (The Wolfsberg Group, 2015). The financial crime risk assessment is performed on all banks to make sure that strong financial crime risk framework is established at the organization level with uninterrupted implementation of policies to protect the bank from the identified risks (Cox, 2014). The financial crime risk framework governs on how bank employees especially compliance officers execute risk assessment. The framework includes risks related to all aspects of money laundering and terrorism financing (The Wolfsberg Group, 2015). This requires the nurtured skills of identifying perceptible and obscure financial crime risks, followed by taking suitable actions that would help in minimizing the risks (Elsinger et al., 2006;
Naheem, 2015; Sahajwala & Van der Bergh, 2000).
Money laundering risk assessment comes as a subset of financial crime risk assessment. According to The Wolfsberg Group (2015), the main purpose of money laundering risk assessment is to permeate improvement in managing financial crime risk by detection the key risks and planning a good AML programme controls on participating institutions. Under tutelage of The Wolfsberg Group guidance document, participating banks and financial institutions would have better comprehension of risk appetite, potential risk exposure, control implementation and mitigation which will help their employees in making informed decision. In Malaysia, National Risk Assessment (NRA) is fulfilled every three years with three main purposes; to identify domestic and foreign AML/CFT threats, renew the strategy based on the risks and realign the resources (Bank Negara Malaysia, 2014). The outcomes of this risk assessment will be disseminated to all banks and financial institutions in Malaysia for improvement of their respective AML/CFT enterprise framework. Apart from the trends, the NRA will focus on latest typologies and recommendations that should be undertaken by banks to enhance their money laundering risk control.
To a lesser extent, risk assessment is applicable to compliance officers in learning to conduct investigation by taking cues from the concepts and tenets of CRA. Business executives in financial institutions acknowledge the importance of CRA as practical guidance to draft effective risk management and control at business unit level (Nicolas & May, 2017). The basic principle of money mule risk assessment is based on the proposed guidelines set by regulating bodies as described by The Wolfsberg Group (2015) and National Risk Assessment (Bank Negara Malaysia, 2014). Previous sources have argued whereby similar manifestation of risk assessment should be adopted from governing body guidelines such as in AML risk assessment (Chen, 2020; Mat Isa et al., 2015) and fraud risk assessment (Mansour, Ahmi & Popoola, 2020; Mohd-Nasir, Mohd-Sanusi
& Ghani, 2016; Popoola, Che-Ahmad & Samsudin, 2014). Therefore, the proposed money mule risk assessment will practically engage the same concept of red flags identification its core risk management and control.
Money mule risk assessment is an important assessment that financial crime compliance officer must focus on cases with money mule suspicion. The risk assessment is based on money mule red flags gathered from ambiguous risks found on money mule account holder’s profile parameters and account transactions (Abdul Rani et al., 2022). The first phase of assessment is proposed on mule account holder’s Know Your Customer (KYC) information especially static data of nationality, age, income, profession and address as part of continuous systematic monitoring of suspicious transaction (Chen, 2020; Chorafas, 2006). Some individuals that are categorized as
cash-intensive clients or high user of electronic transfer belong to a specific group that shares almost identical KYC. Besides, the KYC also provides an overview of money mule’s profile evaluation and establish the real picture of risk held by the account holder (Chorafas, 2006).
Specific and targeted model of KYC identification especially in risk assessment has proven to increase awareness in bank staff’s anti-money laundering practices (de Smet & Mention, 2011).
The second phase of money mule risk assessment involves transaction monitoring, which the mule account’s transactions are assessed for risk indicators or red flags (Redhead, 2021). The transaction monitoring and its analysis lend support to identifying unusual activity which will typically call for suspicious transaction report (STR) filing (Veyder, 2003). It is the poignant phase where abnormal account transactions that pose money mule risks are reported to the regulator. In the practical implementation of transaction monitoring, a broad range of assessment could be engaged by financial crime compliance officers such as the latest cognitive-quantum suspicious entity detection (Shabbir, Shabir, Javed, Chakraborty & Rizwan, 2022).
Figure 3: Proposed money mule risk assessment
4. Conclusion
Money mule is a real threat to society especially to financial system. Public will continue to be harangued with devastating news of scams, money mule and loss from their criminal activities (Ang, 2022). There should not be spartan effort in combatting money mule crimes neither at the national level, nor at the banking institutions. All players should be on their guard to prevent the money mule phenomenon.
Financial crime compliance officers are responsible as the second line of defense against money mule risk exposure in banks. They should be well equipped with the knowledge, skills and competencies to perform money mule risk assessment. Financial crime compliance officers must be averse to the money mule risks and in the process of doing so, they are required to acquaint themselves with the common money mule red flags (Abdul Rani et al., 2022). This article introduces the fundamentals of money mule risk assessment which is hoped to contribute toward knowledge enrichment to the anti-money laundering practitioners and academic world.
Acknowledgement: The authors would like to express their gratitude to the Ministry of Higher Education for Accounting Research Institute HICoE research funding (600-RMC/ARI 5/3(030/2021), Accounting Research Institute, and Universiti Teknologi MARA for all supports and resources.
Money mule risk assessment
KYC Red flags assessment
Transaction monitoring red flags assessment
Reference
Abd Rahman, M. R. (2020). Online Scammers and Their Mules in Malaysia. Jurnal Undang- Undang Dan Masyarakat, 26, 65–72. https://doi.org/10.17576/juum-2020-26-08
Abdul Rani, M. I., Zolkaflil, S. & Syed Mustapha Nazri, S. N. F. (2022). The money mule red flags in anti-money laundering transaction monitoring investigation. International Journal
of Business and Economy, 4(1), 150-163.
https://myjms.mohe.gov.my/index.php/ijbec/article/view/17658/9295
Ang, R. (2022). 157 suspected scammers and money mules linked to nearly 500 cases under probe in Singapore. The Star. https://www.thestar.com.my/tech/tech-news/2022/02/11/157- suspected-scammers-and-money-mules-linked-to-nearly-500-cases-under-probe-in-spore Bank Negara Malaysia (2014). National Risk Assessment on money laundering & terrorism
financing. https://amlcft.bnm.gov.my/AMLCFT03.html
Barrett, C. (2021, March 3). Money mule scams target ‘Generation Covid’. Financial Times.
https://www.ft.com/content/82efa8c0-be53-4da7-879f-d2a9f3f47e5f
Bernard, S. (2021). Financial services firms have a lot to lose from a cyber attack. Zurich North America. https://insights.zurichna.com/financial-services-firms-have-a-lot-to-lose-from-a- cyber-attack
Chen, T.-H. (2020). Do you know your customer? Bank risk assessment based on machine
learning. Applied Soft Computing Journal, 86, 1–7.
https://doi.org/10.1016/j.asoc.2019.105779
Choo, R. K.-K. (2011). Trends & Issues in Crime and Criminal Justice. Trends & Issues in Crime and Criminal Justice, 408(February 2011), 1–6.
Chorafas, D. N. (2006). Know your customer and his or her profile. In D. N. Chorafas (Ed.), Wealth Management (pp. 24–45). Butterworth-Heinemann. https://doi.org/10.1016/B978- 075066855-2.50002-7
Cox, D. (2014). Handbook of Anti-Money Laundering. John Wiley & Sons Ltd.
Custers, B. H., Pool, R. L., & Cornelisse, R. (2019). Banking malware and the laundering of its profits. European Journal of Criminology, 16(6), 728–745.
https://doi.org/https://doi.org/10/1177/1477370818788007
de Smet, D., & Mention, A. L. (2011). Improving auditor effectiveness in assessing KYC/AML practices: Case study in a Luxembourgish context. Managerial Auditing Journal, 26(2), 182–203. https://doi.org/10.1108/02686901111095038
Dunham, K. (2006). Money mules: An investigative view. The EDP Audit, Control, and Security
Newsletter, 33(8), 6.
https://doi.org/10.1201/1079.07366981/45802.33.8.20060201/91957.2
Elsinger, H., Lehar, A., & Summer, M. (2006). Risk assessment for banking systems. Management Science, 52(9), 1301–1314. https://doi.org/10.1287/mnsc.1060.0531
Esoimeme, E. E. (2021). Identifying and reducing the money laundering risks posed by individuals who have been unknowingly recruited as money mules. Journal of Money Laundering Control, 24(1), 201–212. https://doi.org/doi.org/10.1108/JMLC-05-2020-0053
Federal Bureau Investigation (2021, December 3). Don’t be a mule: Awareness can prevent crime.
https://www.fbi.gov/scams-and-safety/common-scams-and-crimes/money-mules
Florêncio, D. & Herley, C. (2010). Phishing and money mules. 2010 IEEE International Workshop on Information Forensics and Security, 1–5. https://doi.org/10.1109/WIFS.2010.5711465
Foy, K. (2021). Suspected ‘money mule’ (22) arrested as part of operation into €1.1m fraud. The Independent. https://www.independent.ie/news/suspected-money-mule-22-arrested-as- part-of-operation-into-11m-fraud-40151151.html
Hashim, R. & Abd Rahman, A. (2020). Peranan mule account dalam penggubahan wang haram di Malaysia: Satu kajian perbincangan melalui kajian kes. International Journal of Social Science Research, 2(4), 108-145.
Hollander, Y. (2000). Prevent web site defacement. Internet Security Advisor, 3(6), 2–4.
Howard, R., Thomas, R. & Winterfeld, S (2009). Cyber fraud: Principles, trends and mitigation techniques. In Howard, R., Thomas, R. & Winterfeld, S. (Eds), Cyber fraud: Tactics, Techniques and Procedures. CRC Press.
Hülsse, R (2017). The money mule: Its discursive construction and the implications. Vanderbilt
Journal of Transnational Law, 50, 1007-1032.
https://heinonline.org/HOL/LandingPage?handle=hein.journals/vantl50&div=32&id=&p age=
Hutchings, A. (2014). Crime from the keyboard: Organised cybercrime, co-offending, initiation and knowledge transmission. Crime, Law and Social Change, 62(1), 1–20.
https://doi.org/10.1007/s10611-014-9520-z
Jadavji, S. (2011). Fraud and money laundering: What’s the connection?. Acams Today.
https://www.acamstoday.org/fraud-and-money-laundering-whats-the-connection/
Jakobsson, M. (2005). Modeling and preventing phishing attacks. In A. S. Patrick & M. Yung (Eds.), Financial Cryptography.
Kondalwar, M. N., & Shelke, C. J. (2014). Remote Administrative Trojan/Tool (RAT).
International Journal of Computer Science and Mobile Computing, 3(3), 482–487.
Mansour, A. Z., Ahmi, A. & Popoola, O. M. J. (2020). The personality factor of conscientiousness on skills requirement and fraud risk assessment performance. International Journal of Financial Research, 11(20), 405-415. https://doi.org/10.5430/ijfr.v11n2p405
Mat-Isa, Y., Sanusi, Z. M., Haniff, M. N., & Barnes, P. A. (2015). Money laundering risk: From the bankers’ and regulators perspectives. Procedia Economics and Finance, 28(April), 7–
13. https://doi.org/10.1016/s2212-5671(15)01075-8
McGlasson, L. (2010). Agencies issue ACH, wire fraud advisory. Bank Info Security.
https://ww.bankinfosecurity.com/agencies-issue-ach-wire-fraud-advisory-a-2298
Mohd-Nassir, M. D., Mohd-Sanusi, Z. & Ghani, E. K (2016). Effect of brainstorming and expertise on fraud risk assessment. International Journal of Economics and Financial Issues, 6(S4), 62-67. https://www.econjournals.com/index.php/ijefi/article/view/2690
Moore, T., Clayton, R., & Anderson, R. (2009). The economics of online crime. Journal of Economic Perspectives, 23(3), 3–20. https://doi.org/10.1257/jep.23.3.3
Mugarura, N. (2016). Uncoupling the relationship between corruption and money laundering crimes. Journal of Financial Regulation and Compliance, 24(1), 74-89.
https://doi.org/10.1108/JFRC-01-2014-0002
Mutch, J. (2020). Watch out for 'money mules' tactics by criminals, police say. The Boston News.
https://www.theboltonnews.co.uk/news/18647800.watch-money-mules-tactics-criminals- police-say/
Naheem, M. A. (2015). Money laundering using investment companies. Journal of Money Laundering Control, 18(4), 438–446. https://doi.org/doi.org/10.1108/JMLC-10-2014- 0031
Nguyen, T., & Luong, H. T. (2021). The structure of cybercrime networks: transnational computer fraud in Vietnam. Journal of Crime and Justice, 44(4), 419-440.
https://doi.org/10.1080/0735648X.2020.1818605
Nicolas, S., & May, P. (2017). Building an effective compliance risk assessment programme for a financial institution. Journal of Securities Operations & Custody, 9(3), 215–224.
Parthasarathy, P. (2021). How banks can address rising cyber security threats to protect customers.
The National News. https://www.thenationalnews.com/business/money/2021/09/15/how- banks-can-address-rising-cyber-security-threats-to-protect-customers/
Pemble, M. (2005). Evolutionary trends in bank customer-targeted malware early history. Network Security, 10, 4–7. https://doi.org/10.1016/S1353-4858(05)70288-9
Popoola, O. M. J., Che-Ahmad, A. & Samsudin, R. S. (2014). Impact of task performance fraud risk assessment on forensic skills and mindsets: Experience from Nigeria. International Journal of Business and Social Science, 5(9), 1-9.
Reboredo, F. (2013). Socio-economic, environmental, and governance impacts of illegal logging.
Environment Systems and Decisions, 33(2), 295-304. https://doi.org/10.1007/s10669-013- 9444-7
Rusanov, G. & Pudovochkin, Y. (2020). Money laundering in the modern crime system. Journal of Money Laundering Control, 24(4), 860-868. https://doi.org/10.1108/JMLC-08-2020- 0085
Sahajwala, R., & Van der Bergh, P. (2000). Supervisory risk assessment and early warning system.
In Basel Committee on Banking Supervision Working Paper (Issue 4).
Shabbir, A., Shabir, M., Javed, A. R., Chakraborty, C., & Rizwan, M. (2022). Suspicious transaction detection in banking cyber–physical systems. Computers and Electrical Engineering, 97(January 2021), 1–17. https://doi.org/10.1016/j.compeleceng.2021.107596 Sundarakani, S., & Ramasamy, M. (2013). Consequences of money laundering in banking sector.
Jurnal Teknologi, 64(2), 93-96. https://doi.org/10.11113/jt.v64.2243
Sutton, M. (2015). Majority of UAE online fraud victims unable to recover losses. Arabian Business. https://www.arabianbusiness.com/industries/technology/majority-of-uae- online-fraud-victims-unable-recover-losses-577750
Tang, L. (2020). Jail for woman who laundered S$ 128,000 from overseas scam victims after second chance from police. Today. https://www.todayonline.com/singapore/jail-woman- who-laundered-s128000-overseas-scam-victims-after-second-chance-police
The Wolfsberg Group. (2015). The Wolfsberg frequently asked questions on risk assessments for money laundering, sanctions and bribery & corruption.
http://www.bis.org/publ/bcbs275.pdf
Veyder, F. (2003). Case study: Where is the risk in transaction monitoring?. Journal of Financial
Regulation and Compliance, 11(4), 323–328.
https://doi.org/10.1108/13581980310810606
Weeks-Brown, R. (2018). Countries are advancing efforts to stop criminals from laundering their trillions. Finance & Development, 55(4). International Monetary Fund.
https://www.imf.org/external/pubs/ft/fandd/2018/12/imf-anti-money-laundering-and- economic-stability-straight.htm
Wood, V. (2020). UK banks accused of facilitating fraudsters and criminals after financial documents leaked. The Independent. https://www.independent.co.uk/news/uk/home- news/fincen-document-leak-uk-banks-fraud-crime-russian-oligarch-b509133.html
Yeoh, P. (2020). Banks’ vulnerabilities to money laundering activities. Journal of Money Laundering Control, 23(1), 122-135. https://doi.org/10.1108/JMLC-05-2019-0040
Zeidan, M. J. (2013). Effects of illegal behavior on the financial performance of US banking institutions. Journal of Business Ethics, 112, 313-324. https://doi.org/10.1007/s10551-012- 1253-2