Potential Money Laundering Research Articles

When Economic Sanctions Cause White-Collar and Corporate Crime: The Case of Hidden Russian Ownership Revealed by a Norwegian Insurance Firm

ABSTRACT Sanctions have criminalizing consequences. This paper addresses the issue of Russian businesses attempting to circumvent and evade sanctions by white-collar and corporate crime. The case presented in this paper concerns a Norwegian smolt production facility owned by Russians, who suddenly transferred ownership to a Norwegian because of the sanctions. It seemed that the Russians remained the real owners since the Norwegian paid nothing for the shares. While Norwegian police were reluctant to investigate the matter, a Norwegian insurance firm became worried that insurance premiums paid by the production facility in Norway could represent money laundering. This paper presents the case of potential money laundering by review of the literature on sanctions and by application of institutional theory. The fact that economic sanctions are problematic in themselves and in addition stimulate white-collar and corporate crime and even mafia business as exemplified in Iran, should be an important consideration against the trend of imposing sanctions on steadily more countries that are different from our countries.

Spatio-Temporal Network-based Bank Transactional Behavior Analysis to Detect Suspicious Activities

Money laundering refers to the act of disguising the proceeds of illegal activities as legitimate funds. This is a significant problem as it enables criminals to profit from illegal activities and finance further criminal endeavors. Money laundering is also linked to other crimes, such as drug trafficking, terrorism financing, and corruption. To combat money laundering, governments and financial institutions have implemented various measures, such as Know Your Customer (KYC) regulations, Anti-Money Laundering (AML) laws, and the use of financial intelligence units. However, the existing money laundering system is complex, making it difficult to detect and prevent money laundering activities. Many current systems rely on outdated technology and manual processes, which can be time-consuming and prone to error. Therefore, there is a need for effective detection and prevention systems that can identify suspicious transactions and patterns of behavior. This project aims to prevent and detect money laundering activities by identifying suspicious transactions and monitoring the movement of funds through the financial system. The proposed system uses Long Short-Term Memory (LSTM) to detect and prevent money laundering activities. By analyzing the data in a time-series format, LSTM can identify unusual patterns of transactions and flag them for further investigation. LSTM can also predict future trends in financial data, allowing for the detection and prevention of potential money laundering activities before they occur. The proposed transactional network and behavior analysis system provides a more efficient and accurate method for identifying potential money laundering activities. This system can ultimately lead to a more effective and efficient anti-money laundering system. Keywords: Anti-Money laundering, Long short-term memory, Legitimate funds, Transaction network, behavior analysis, Time series format

Data Processing Methods for Financing Terrorism: The Role of Microsoft Power BI in Money Laundering Detection

This article explores the imperative landscape of money laundering detection, emphasizing the role of advanced financial monitoring and the integration of Microsoft Power BI for in-depth data analysis. Addressing challenges in fraud detection, the study employs synthetic datasets from Kaggle, overcoming limitations in accessing real financial data. Through a meticulous process, the article demonstrates the power of Power BI in uncovering potential money laundering activities via visual analyses such as stacked column charts, line charts, pie charts, and clustered bar charts. The significance of appropriate data preparation, column selection, and visual representations is highlighted, offering a systematic approach for fraud detection in financial datasets. This research showcases the effectiveness of Power BI as a strategic tool, providing insights and methodologies that contribute to the ongoing discourse on fraud prevention in financial analytics.

Exploration of potential money laundering crimes with virtual currency facilities in Indonesia

PurposeThe purpose of this study is to exploration potential money laundering crimes with virtual currency facilities in Indonesia. Money laundering using crypto is the process of disguising the origin of money obtained illegally. Then, the perpetrator transfers it to a legitimate business. Virtual money then started to become a phenomenon in society since the emergence of cryptocurrencies as a form of technology development of e-commerce activities.Design/methodology/approachThis research method is normative law which is prescriptive. The data collection technique used is document study or literature study by collecting primary and secondary legal materials.FindingsThe results of this study show that the bitcoin virtual currency has the potential to act as a means of money laundering. There are technologies and online platforms that are moving with more sophisticated methods. Through bitcoin exchanges, it has the greatest potential for money laundering. The usage of virtual currency (cryptocurrency) by those who commit money laundering offenses is responsible for the actions’ severe negative effects on the State of Indonesia.Originality/valueTo the best of the author’s knowledge, this is the first study conducted in Indonesia that explores potential money-laundering crimes using virtual currency facilities.

Implementation of organization and end-user computing-anti-money laundering monitoring and analysis system security control.

The Monitoring and Analysis Centre for the fight against money laundering is a valid financial information body which is responsible for collecting, analysing and providing financial information and conducting international exchanges of financial information for relevant undertakings. By constructing the analysis of the monitoring of the local and foreign currency and of the data transmission subsystem in the plan for the transitional period against In the light of the above, the Commission will continue to monitor the implementation of the acquis in the light of the progress made in implementing the acquis future new systems. The purpose of this paper is to study the research and implementation of the security control of the anti-money laundering monitoring and analysis system. This article studies the application of decision tree analysis technology in the anti-money laundering monitoring system of insurance companies to achieve the purpose of improving the anti-money laundering monitoring technology and capabilities of insurance companies. The emergence of data mining technology provides a new system solution for anti-money laundering monitoring. For insurance anti-money laundering, how to find potential money laundering cases in suspicious and large surrender transactions is key. The experimental data show that the decision tree method is the best predictor of the customer category between the insurance application and the surrender days. The decision tree analysis technology has greatly improved the security monitoring capabilities of the insurance in the anti-money laundering monitoring system. Experimental data shows that the security control capabilities of the anti-money laundering monitoring and analysis system make the accuracy of the entire model reach 95%, the accuracy of large and suspicious transactions reaches 88.6%, and the correct classification of customers is about 99.6%.

Freeports: Innovative trading hubs or centres for money laundering and tax evasion?

PurposeThis study aims to provide a critical overview of freeports’ trading operations and consider to what extent they may present a money-laundering and tax-evasion risk.Design/methodology/approachThis study reviews the literature surrounding freeports and offers an up-to-date narrative of their potential money-laundering and tax-evasion activities. The paper relies on secondary data from published sources referencing peer-reviewed papers and “grey-literature” material relating to freeports, offshore finance and anti-money laundering control.FindingsThis study demonstrates the attractive trading advantages offered by freeports to enable enterprise and innovation. However, the study reveals that the secretive offshore space in which freeports operate also helps to obscure beneficial ownership and illicit trade-based practices that frustrate authorities’ efforts to trace laundered monies and recover government taxes. Despite freeports’ trade offerings, stronger regulation is needed to prevent them from being abused for money-laundering and tax-evasion purposes.Originality/valueThis study provides an important insight into the money-laundering and tax-evasion risks presented by freeports and, in doing so, advances the contemporary debate on illicit activities occurring through offshore jurisdictions.

Detecting money laundering transactions with machine learning

Purpose The purpose of this paper is to develop, describe and validate a machine learning model for prioritising which financial transactions should be manually investigated for potential money laundering. The model is applied to a large data set from Norway’s largest bank, DNB. Design/methodology/approach A supervised machine learning model is trained by using three types of historic data: “normal” legal transactions; those flagged as suspicious by the bank’s internal alert system; and potential money laundering cases reported to the authorities. The model is trained to predict the probability that a new transaction should be reported, using information such as background information about the sender/receiver, their earlier behaviour and their transaction history. Findings The paper demonstrates that the common approach of not using non-reported alerts (i.e. transactions that are investigated but not reported) in the training of the model can lead to sub-optimal results. The same applies to the use of normal (un-investigated) transactions. Our developed method outperforms the bank’s current approach in terms of a fair measure of performance. Originality/value This research study is one of very few published anti-money laundering (AML) models for suspicious transactions that have been applied to a realistically sized data set. The paper also presents a new performance measure specifically tailored to compare the proposed method to the bank’s existing AML system.

The legal profession in Kenya and its anti-money laundering obligations or lack thereof

PurposeThe purpose of this paper is to examine the inclusion of lawyers in Kenya’s anti-money laundering regime and the role they can play towards assisting in detection and gate-keeping of potential money laundering activities. Kenya is a transit point for trade-based money laundering. Accordingly, it is vulnerable to money laundering that can be facilitated by legal professionals, through their misuse by criminals. These professionals can be both enablers and perpetrators.Design/methodology/approachThe study is secondary in nature. It is based on reviewing relevant literature and analysing the Proceeds of Crime and Anti-Money Laundering Act and the Proceeds of Crime and Anti-Money Laundering Regulations. The legislation and the rules form the core of Kenya’s anti-money laundering regime.FindingsThe omission of legal professionals from Kenya’s anti-money laundering regime constitutes a big gap under its preventative mechanisms. Further, it makes them attractive to criminals because they are under no legal obligation to report potential money laundering activities. Ultimately, the inclusion of lawyers as DNFBPs is necessary. This would seal the extant regulatory gap and ensure enhanced awareness amongst the legal professionals of the money laundering risks that they face.Originality/valueGiven Kenya’s money laundering susceptibility, it is necessary and prudent to critically consider the inclusion of legal professionals in its anti-money laundering mechanisms. The paper seeks to make a practical and scholarly contribution in considering the issue and possibly trigger further discussions, as well as the necessary legislative and policy changes. This would positively enhance the success of Kenya’s anti-money laundering regime in detecting money laundering activities.

Deconstructing Digital Currency and Its Risks: Why ASIC Must Rise to the Regulatory Challenge

Digital currency is a ‘disrupter’ of financial services and currency markets, and as such presents new regulatory challenges. International regulatory responses to digital currency range from being largely ignored in some jurisdictions to being banned in others, with most jurisdictions charting a middle course of ‘wait and see’ while attempting to deal with pressing issues (such as taxation liability and potential money laundering and terrorism financing issues). This article explains digital currency, its benefits, its problems, its risks and the regulatory response so far. It analyses the extent to which the Australian Securities and Investments Commission (ASIC, the national securities regulator) may or may not have regulatory power and jurisdiction under existing Australian law, and the role of other relevant regulators and institutions. It concludes that digital currency may well be a ‘financial product’ under Corporations Act 2001 (Cth) s 763A (though many suppliers/issuers of that product will be website operators located outside Australia). If it is a financial product, ASIC would also have jurisdiction over issuers and markets that trade in that product. This conclusion could easily be fortified by legislative confirmation; however, it is suggested that ASIC should in all events test its powers to determine whether any legislative change is needed. Regulation by ASIC would add to recent moves to deal with digital currency by the Australian Transaction Reports and Analysis Centre (AUSTRAC) and the Australian Taxation Office (ATO). In all cases, this article argues that the time has come for Commonwealth regulation of digital currencies by ASIC as the relevant regulator. This would then trigger the obligations set out in the Corporations Act and the ASIC Act, including Australian Financial Services Licensing, Australian Market Licensing, standards of efficiency, honesty and fairness, disclosure provisions, possible market offences and corporate regulation generally. The suggested jurisdiction of ASIC would build on its existing role as well as the roles of the Australian Competition and Consumer Commission, the ATO and AUSTRAC.

Detection mechanisms under Kenya’s anti-money laundering regime: omissions and loopholes

PurposeThis paper aims to detect the methods that facilitate the identification of potential money laundering activities in Kenya. Kenya is a transit point for international drug traffickers and trade-based money laundering. Hence, it is vulnerable to money laundering and consequently, it is necessary to examine the potency of its first lines of defence and its weaknesses.Design/methodology/approachThe research is secondary in nature. It is based on reviewing relevant literature and analyzing the Proceeds of Crime and Anti-Money Laundering Act (POCAMLA) and the Proceeds of Crime and Anti-Money Laundering Regulations (POCAMLR). Both form the core of Kenya’s anti-money laundering regime.FindingsGenerally, the identified methods can facilitate identification of proceeds of crime and possible laundering activity. However, there are challenges in the provisions that could reduce effectiveness. These include intrinsic loopholes and implementation challenges, in the provisions relating to accountants, precious stone and metal dealers and the hawala system. Additionally, there is the key omission of car dealers and legal professionals from the mechanisms for detecting money laundering.Originality/valueGiven Kenya’s money laundering susceptibility, it is necessary and prudent to critically examine its mechanisms for detecting money laundering. The paper seeks to make a practical and scholarly contribution in filling this extant gap. This paper can trigger further discussions as well as the necessary legislative and policy changes. This would positively enhance the success of Kenya’s anti-money laundering regime in detecting money laundering activities.

New payment methods: A review of 2010–2012 FATF mutual evaluation reports

Traditionally, the financial sector is often seen as the gatekeepers of the Anti-Money Laundering/Counter Terrorism Financing (AML/CFT) regime. In recent years, new payment methods, particularly stored value prepaid cards and mobile money transfer systems, are increasingly been seen as a widely accepted payment method. However, they have also been highlighted as potential money laundering and terrorism financing instruments. This paper aims to provide an improved understanding of the money laundering and terrorism financing risk environment and hopefully, new payment method providers are better placed to manage new and emerging threats. A review of the compliance levels in 65 mutual evaluation (and follow-up) reports published by FATF in English between 1st of January 2010 and 31st of December 2012 suggests that there are still compliance issues in areas that might afford exploitative opportunities for transnational crime and terrorist networks – after all, global standards are only as strong as their weakest link. This can have detrimental effects on a country's national security through increasing risks of money laundering and financing of terrorism (e.g. due to regulatory arbitrage), and wastage due to the implementation of inappropriate regulatory measures. We conclude with a three-pronged evidence-based AML/CTF approach, with the aim of helping governments and key stakeholders to improve knowledge of the nature and dimensions to the problem, and of suitable risk management and mitigation strategies that would enable scarce resources in fighting money laundering and terrorism financing threats to be more effectively allocated and, hence, make the most impact.

The increasing criminalization of economic law – a competition law perspective

Purpose – The purpose of this paper is to examine the trend towards the criminalization of hard core cartel conduct and to consider the appropriateness and effectiveness of extending the criminal law to this conduct. In addition, it will consider some of the legal implications, including the exposure of directors of companies to potential racketeering charges. Design/methodology/approach – The paper first examines cartel theory and the justification for prohibition. The paper then identifies the emerging trend toward criminalization of hard core cartel conduct, followed by an assessment of potential justifications for criminalization. Implications of criminalization, including the potential impact of organized crime legislation on offenders and regulators, will then be considered. Findings – There is a clear trend towards the criminalization of hard core cartels. The paper argues that this trend is appropriate, both because of the moral culpability it attracts and because of its potential to enhance general deterrence. The paper also argues that cartel conduct, in jurisdictions in which it is criminalized, will constitute “organized crime” as defined in the Palermo Convention and, as such, expose participants to potential money laundering and asset forfeiture consequences. Originality/value – This paper is of value to governments and regulators considering adoption or implementation of a criminal cartel regime and to practitioners in advising clients about potential consequences of cartel conduct within a criminal regime.

Financial Crimes Enforcement Network AI System (FAIS) Identifying Potential Money Laundering from Reports of Large Cash Transactions

The Financial Crimes Enforcement Network (FIN-CEN) AI system (FAIS) links and evaluates reports of large cash transactions to identify potential money laundering. The objective of FAIS is to discover previously unknown, potentially high-value leads for possible investigation. FAIS integrates intelligent human and software agents in a cooperative discovery task on a very large data space. It is a complex system incorporating several aspects of AI technology, including rule-based reasoning and a blackboard. FAIS consists of an underlying database (that functions as a black-board), a graphic user interface, and several preprocessing and analysis modules. FAIS has been in operation at FINCEN since March 1993; a dedicated group of analysts process approximately 200,000 transactions a week, during which time over 400 investigative support reports corresponding to over $1 billion in potential laundered funds were developed. FAIS's unique analytic power arises primarily from a change in view of the underlying data from a transaction-oriented perspective to a subject-oriented (that is, person or organization) perspective.

Parallelism speeds data mining

Increasingly, scientists and business people are mining mountains of stored data for valuable nuggets of insight using computerized data mining (also called knowledge discovery in databases, or KDD) techniques to learn more about customer behavior, discover new quasars, and catch crooks. Data mining recognizes patterns in data and predicts patterns from the data. Today's data-mining techniques, sometimes called 'siftware', range from OLAP (online application processing) tools that query multidimensional databases, to various statistical techniques, to advanced artificial intelligence techniques like machine learning, neural networks, rule-based systems and genetic algorithms. To efficiently mine gigabytes and terabytes of data in a timely fashion, a growing number of organizations are turning to parallelism to speed up the processing. The US Department of Treasury, for example, has fielded a data-mining application that sifts through all large cash transactions reported by banks and casinos to detect potential money laundering. The application runs on a 6-processor Sun server. >