Laundering System Research Articles

Effectiveness of the UK AML System and Strategies for Improvement

As the cost of anti-money laundering (AML) compliance in the UK continues to rise, financial crime rates have not fallen. This study assesses the effectiveness of the UK’s anti-money laundering (AML) system and proposes strategies for improvement. The research examines the current legal framework, including the Proceeds of Crime Act (POCA) and the Money Laundering Regulations 2017 (MLR2017), assessing their role in preventing and combating financial crime. Analyzing case studies and statistical data, the findings suggest that while the UK AML system has been effective in identifying and deterring money laundering, significant challenges remain. Insufficient sharing of information between agencies, uneven allocation of resources, and inconsistent regulatory enforcement hinder overall effectiveness. To address these shortcomings, the study makes several strategic recommendations: strengthening inter-agency cooperation, leveraging advanced technological solutions for data analysis and reporting, improving training and awareness programmes for financial institutions, and improving the legal framework to adapt to emerging threats. Implementing these recommendations could strengthen the UK’s anti-money laundering (AML) efforts, making them more robust and responsive to the evolving financial crime landscape. Ultimately, this study aims to contribute to the current debate on the effectiveness of AML and to make recommendations for improving the AML system’s response to the complexities of money laundering in order to strengthen the UK’s position in the global fight against money laundering.

Anti-Money Laundering in the Digital Economy: Institutional, Financial, and Educational Channels

This paper aims to address the practical challenge of enhancing the effectiveness of anti-money laundering systems within the context of the digital economy. Specifically, it examines how institutional, financial, and educational channels can be optimised to combat illicit financial activities. This research responds to the growing need for a comprehensive anti-money laundering framework that can adapt to the evolving dynamics of the digital economy, where financial crimes are becoming increasingly sophisticated. A methodological framework is proposed to assess and forecast the effectiveness of these channels, employing the principal component method and Saaty hierarchy analysis. The study incorporates both linear and nonlinear regression models to provide medium-term forecasts for the channels' characteristics and their contribution to anti-money laundering effectiveness. An integral indicator is developed to quantify the impact of these channels in combating illicit financial activities. Historical data are utilised to project trends up to 2025, revealing the expected influence of institutional, financial (investment), and educational channels on the anti-money laundering system. The results indicate that, without targeted interventions, the effectiveness of institutional and financial channels gradually decreases, whereas the educational channel maintains a positive trajectory. This analysis underscores the importance of strengthening institutional frameworks and financial oversight to ensure the sustainability of anti-money laundering measures in the digital economy.

The necessity of artificial intelligence in fintech for SupTech and RegTech supervisory in banks and financial organizations

In the rapidly evolving financial landscape, the integration of Artificial Intelligence (AI) into Supervisory Technology (SupTech) and Regulatory Technology (RegTech) has become increasingly vital. As banks and financial organizations grapple with the complexities of compliance, risk management, and regulatory oversight, AI offers transformative capabilities that enhance efficiency, accuracy, and resilience. This paper explores the critical need for AI in Fintech for SupTech and RegTech, focusing on its role in supervisory functions within the banking sector. A case study on AI-driven anti-money laundering systems is presented, along with a discussion of authentic laboratory research and survey results. The paper concludes by illustrating the potential impact of AI on financial supervision with graphs and data, underscoring its necessity in modern financial governance.

Digital Financial Services Effort in Enforcing Anti-Money Laundering through Open Banking Optimization

Technology has revolutionized finance, but seamless services increase fraud risks. As digital finance grows, anti-money laundering (AML) systems struggle with high transaction volumes. The main objective of this study is to verify whether open banking catalyzes the efficiency of transaction monitoring in AML. Another objective is to analyze the current agenda of regulators in Indonesia, which is related to the Indonesia Payment Systems Blueprint 2025 and customer data security. The Delphi technique pinpointed contemporary AML compliance technologies, encompassing money laundering, Regulatory Technology, and regulatory bodies. The article delves into a comparative analysis between Regulatory Technology and financial crime, which emphasizes several actions to eradicate money laundering, such as strengthening the AML system, customer screening, and allowing cross-data sharing between institutions. In addition, this paper explores RegTech's limitations and forthcoming hurdles regarding AML compliance. The finding shows that open banking catalyzes the efficiency of transaction monitoring in AML and supports the regulator's agenda to combat money laundering with some requirements.

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

Importance of Compliance with the Obligation of the Suspicious Activity Report (SAR) against Money Laundering: Some General Considerations

In the fight against Money Laundering, the effectiveness of the task known as the Suspicious Activity Report (SAR), undoubtedly, constitutes one of the main protection shields for Obligated Subjects to their prevention with Risk-Based Approach (RBA). It stands as one of the most relevant sources of information for the analytical work of a Financial Intelligence Unit (FIU). It is the starting point for subsequent investigation, prosecution and application of criminal measures, and it is an incentive of great importance for decision-making in administrative, criminal and criminological policies for its confrontation in its dual profile: as a Risk, and as a Crime. And all this at an accelerated pace since, in addition to its seriousness, by the devastation it represents to the stability of a country’s socio-economic financial order, its implementation and effectiveness, is being permanently monitored and qualified by international agencies specialized in the matter, and whose reports work as a basic criterionto measure Country- Risk, and therefore a true radar for attracting lawful investments. Then again, it becomes necessary to understand the guidelines for an effective SAR, and understand, in order to mitigate its impact, the most common reasons for its non-compliance, as a consequence of infractions to specific legal frameworks, that can make the whole Anti-Money Laundering system of a country, ineffective. Definitely, the presentation of a SAR with Quality,Timeliness and Accuracy, directly affects the mitigation of the Risk of Financial Crime, in pursuit of the health of businesses and the economy.

A Case Study on Multi-Countries Money Laundering Scheme and A Proposed Automatic Detection System

This paper presents a case study on the Franco-Israeli syndicates orchestrating their cross-continental money laundering schemes. These money laundering schemes have been operating for over two decades, ever since China’s entry to the World Trade Organization in 2001. The paper reviews the operation of the money laundering schemes in detail and highlights the difficulties encountered by bankers and investigators in unearthing and investigating criminal activities within the banking systems. The paper then proposes an automatic anti-money laundering system, which is expected to address these difficulties. Preliminary experimental results show that the system successfully identifies the crux of these money laundering syndicates within a few days’ time, something which usually takes years of investigation to track down the suspects using traditional methods, as well as its ability to initiate pre-warning procedures to the banks and law enforcement agencies once suspicious transaction clusters are found. The paper concludes with a discussion on the legal implications encompassing the evidence projected by this system.

Fraud Transaction Detection For Anti-Money Laundering Systems Based On Deep Learning

This study addresses the escalating problem of financial fraud, with a particular focus on credit card fraud, a phenomenon that has skyrocketed due to the increasing prevalence of online transactions. The research aims to strengthen anti-money laundering (AML) systems, thereby improving the detection and prevention of fraudulent transactions. For this study, a Dense Neural Network (DNN) has been developed to predict fraudulent transactions with efficiency and accuracy. The model is based on deep learning, and given the highly unbalanced nature of the dataset, balancing techniques were employed to mitigate the bias towards the minority class and improve performance. The DNN model demonstrated robust performance, generalizability, and reliability, achieving over 99% accuracy across training, validation, and test sets. This indicates the model's potential as a powerful tool in the ongoing fight against financial fraud. The results of this study could have significant implications for the financial sector, corporations, and governments, contributing to safer and more secure financial transactions.

PERAN REGULASI TEKNOLOGI DAN KARYAWAN BANK UNTUK MENCEGAH TINDAK PENCUCIAN UANG DI BANK

Banks, commonly utilized as financial institutions, are often implicated in money laundering activities. The majority of these institutions report suspicious transactions to the Indonesian Financial Transaction Reports and Analysis Center (PPATK). This study seeks to assess the influence of implementing regulatory technology (regtech) and the involvement of Anti Money Laundering (AML) officers in preventing money laundering within a bank. Employing explanatory research to test hypotheses, the primary data collection involved a questionnaire distributed to 689 AML officers in Indonesia, with a 42.9% response rate from 296 participants. Multivariate analysis results indicate that electronic know-your-customer (KYC) lacks significance in preventing money laundering. Conversely, transaction monitoring, along with considerations of cost and time, demonstrates a positive and significant impact on thwarting money laundering. Additionally, there is a notable positive effect between bankers' competency and awareness in preventing money laundering. Both Regulatory Technology and bankers play crucial roles in averting money laundering. Notably, research concerning technology and bankers in the context of money laundering prevention remains limited in Indonesia. This study contributes valuable insights to existing literature on anti-money laundering and the progression of anti-money laundering systems.

КОНЦЕПТУАЛЬНЫЕ ПОДХОДЫ РАЗВИТИЯ НАЦИОНАЛЬНОЙ СИСТЕМЫ ФИНАНСОВОГО МОНИТОРИНГА В БАНКОВСКОМ СЕКТОРЕ

This study considers the approaches of financial monitoring in the banking system for AML/ CFT purposes in relation to an individual, defines its role and place in the national anti-money laundering system. The paper presents the legislation that regulates the activities of organizations that carry out AML/CFT activities and describes the objectives of internal control. In addition, the role of the Central Bank of the Russian Federation in the sphere of regulation of the banking system is considered, and its control and supervisory powers are studied. This study summarizes the main financial transactions that are subject to mandatory control by banks, as well as the implementation mechanisms aimed at preventing the commission of dubious financial transactions. As a practical example of effective fight against money laundering, the statistics of the volume of cross-border transfers and the dynamics of revocation of licenses from commercial organizations are considered, and conclusions are also formulated.

Implementation of Bigdata Analysis in Banking: Debt Default, Anti-money Laundering, Investment Behavior Forecasting

With the development of computer science and technology, people live become more and more convenience. The bank's account security and investment system have also become more and more perfect due to the technology. In our article, we will focus on analyzing debt default system, anti-money laundering system, and investment prediction system. In order to know about the structure and operation of the banking system deeply. After studying and research, we understood the law and trend of global economic development in recent years, including the economic turning down because of COVID-19 from 2021, and the recovery from 2023. Besides, we learned several program systems such as ARIMA, AML and KYC solutions and so on, which can be also used in other business fields. In summary, the meaning of writing this article is not only develop our interests, but also learn more about the technology that touches out lives, So, it will be easier for us to manage our finance and understand the operation rule of society in future.

Automatic suppression of false positive alerts in anti-money laundering systems using machine learning

Criminal activities generate an estimated $2 trillion in laundered money per year, highlighting the need for financial institutions to detect and report suspicious activity to protect their reputation. However, rule-based models commonly used for this purpose generate a high number of false positives, draining compliance team time, and increasing investigation costs. However, the application of machine learning in conjunction with rule-based models presents noteworthy implications, encompassing the potential reduction in false positives and the concomitant risk of machine learning inadvertently suppressing true positive alerts. This paper proposes a framework called automatic suppression based on XGBoost for anti-money laundering (ASXAML) to enhance detection by reducing false positives. ASXAML leverages recursive feature elimination with cross-validation for optimal feature selection. Subsequently, Optuna is employed to fine-tune hyperparameters for the XGBoost model. Results indicate that ASXAML achieves an optimal balance between reducing false positives and avoiding missed money laundering events, with an 86% F-beta score and only 11% money laundering customers were incorrectly closed out of 1926 in the test data.

Selected aspects of crime involving virtual currencies

The article consists of two parts. The first discusses issues related to the functioning of the crypto-assets market in Poland and internationally and the planned changes in the regulations governing this market. They concern the legal status of digital tokens and their use in money laundering and terrorist financing, as well as the obligations of obliged institutions in the anti-money laundering system. The second part of the study focuses on procedural and non-procedural issues related to virtual currencies. The status of the digital artefact in criminal proceedings, operational work and the conduct of investigations with a view to combating cryptocurrency crime are discussed. The article concludes with demands addressed to law enforcement and law enforcement agencies. The aim of the article is to provide a comprehensive overview of the issues related to the use of virtual currencies in the commission of crimes, covering in particular AML and terrorist financing issues.

The legal-criminal aspects of Iran’s anti-money laundering law in light of the FATF recommendations

PurposeThe purpose of this study is to examine Iran’s anti-money laundering (AML) system from a legal and criminal perspective and to understand the obstacles for international cooperation and the extent to which it aligns with financial action task force (FATF) standards. In this regard, three aspects are examined, namely, enforcement and guarantees of preventive measures, penalty for ML offences and the burden of proof.Design/methodology/approachThis subject is examined through the legal–criminal perspective, which concentrates on the effectiveness of legal measures in tackling criminal issues by focusing on criminal law. The legal–criminal viewpoint considers criminal behaviour as a breach of societal norms and strives to combat it through legal channels.FindingsIran’s AML laws and regulations are partially compliant with the financial action task force (FATF) recommendations. However, the main obstacle is not the lack of sufficient laws, rather the lack of proper implementation of these laws. In addition, there are foundational shortages, such as the absence of a national risk document to guide an action based on current risks.Originality/valueNo comprehensive study has analysed Iran’s AML laws, referring to the three main aspects of enforcement and guarantees of preventive measures, penalties for ML offenses and the burden of proof. In general, there are few research papers on Iran’s AML laws owing to Iran’s high ranking in ML/TF. However, analysing Iran’s regulations can be helpful in taking a step towards effective international AML practices.

Towards Learning to Discover Money Laundering Sub-network in Massive Transaction Network

Anti-money laundering (AML) systems play a critical role in safeguarding global economy. As money laundering is considered as one of the top group crimes, there is a crucial need to discover money laundering sub-network behind a particular money laundering transaction for a robust AML system. However, existing rule-based methods for money laundering sub-network discovery is heavily based on domain knowledge and may lag behind the modus operandi of launderers. Therefore, in this work, we first address the money laundering sub-network discovery problem with a neural network based approach, and propose an AML framework AMAP equipped with an adaptive sub-network proposer. In particular, we design an adaptive sub-network proposer guided by a supervised contrastive loss to discriminate money laundering transactions from massive benign transactions. We conduct extensive experiments on real-word datasets in AliPay of Ant Group. The result demonstrates the effectiveness of our AMAP in both money laundering transaction detection and money laundering sub-network discovering. The learned framework which yields money laundering sub-network from massive transaction network leads to a more comprehensive risk coverage and a deeper insight to money laundering strategies.

Survival Analysis Methods for Assessing the Anti-Money Laundering System Effectiveness

The article collects and systematizes statistical information to assess the anti-money laundering system effectiveness for 25 banks from 12 countries. The anti-money laundering system effectiveness was evaluated based on applying the survival analysis method by constructing tables of survival for banks subject to sanctions, determining the probability of deciding on the need to impose sanctions on banks, multiple assessments of Kaplan-Meyer, formalization of the Hazard rate instantaneous risk function. The anti-money laundering system effectiveness is compared based on the survival analysis in groups of banks around the world. Relevant factors influenced the assessment of the anti-money laundering system effectiveness based on the application of the principal components method by creating a scree plot and determining the factor loads of the statistical input base indicators in the study. A Cox proportional intensity regression model of dependence of the anti-money laundering system effectiveness on independent factors is constructed.

Strategic management, scenario analysis and competitive advantage analysis: New opportunities for anti-money laundering system reform

The article identifies and mathematically substantiates vectors of reforming the financial monitoring system based on the synergistic approach in the cross-country context by developing scenarios by selecting internal and external factors that stimulate money laundering. The key external and internal factors of the money laundering process intensification are indicated. Competitive advantages of the anti-money laundering processes, the achievement of which is possible in terms of key external and internal factors for activation of money laundering process are formed. The relationship between the key internal and external factors of money laundering intensification and competitive advantages by building a binary characteristic matrix is formed. The synergy effect made by the mutual influence of simultaneously acting internal and external factors – a quantitative feature of the further strategy of the financial monitoring system reforming in terms of cross-country analysis is calculated. An economic-mathematical model for evaluating and interpreting strategy for improving the financial monitoring system in terms of cross-country context based on integer optimization is developed. In the article, there is calculation of the number of competitive advantages received by each observed country, without considering the possible synergy effect of mutual influence of simultaneously acting internal and external factors on each other. Moreover, there is quantitative assessment of the synergy effect made by the emergence of additional competitive advantages due to a certain combination of simultaneous influence made by external and internal factors of the money laundering intensification. The “ideal” situation of the possibility of gaining all possible competitive advantages in reforming the financial monitoring system in terms of cross-country context by overcoming internal and external factors stimulating the money laundering is studied. The results of the cross-country analysis form a basis for the further formalization of the limits for quantitative evaluation of the developed strategies through a uniform distribution.

The Place and The Role of Financial Monitoring in Anti-Money Laundering System

Financial monitoring plays a pivotal role in the overall effectiveness of an anti-money laundering (AML) system. This article explores the place and role of financial monitoring in preventing and detecting money laundering activities. The authors highlighted the significance of effective financial monitoring in meeting regulatory compliance requirements. The definition of the role and place of financial monitoring in the fight against the legalisation of corruption proceeds are updated, considering the tasks and requirements set before Ukraine as a candidate country for the European Union and the challenges caused by the state of war. The article aims to analyse and provide an understanding of the importance of financial monitoring in the broader context of combating money laundering. The authors used different methods and approaches depending on the nature of the research, such as literature review, legal and doctrinal analysis, and comparative analysisб dialectical method, method of analysis and synthesis and method of terminological analysis. The findings of this research underscore the criticality of financial monitoring in safeguarding the integrity of the financial system and protecting economies from the harmful effects of money laundering. By understanding the place and role of financial monitoring within the broader AML framework, financial institutions, policymakers, and regulators can enhance their efforts to combat money laundering and ensure a safer and more secure economic environment.

Changes in credit score, transaction volume, customer characteristics, and the probability of detecting suspicious transactions

PurposeThis study aims to use a unique customer-information data set from a Norwegian bank to identify how small changes in firm-specific factors correlate with the risk of a client subsequently being involved in suspicious transactions. It provides insight into the importance of updating client risk based on changes in transaction volume and credit risk to enable effective resource use in transaction monitoring.Design/methodology/approachChanges in a firm’s bank use and accounting data were tested against subsequent flagged and reported customers to identify which changes led to a significant increase in the probability of engaging in a transaction identified as suspicious. Prioritizing resources to firms that remain suspicious after further controls can improve the risk-based approach and prioritize detection efforts. The main factors were customer probability of default (credit score), size and changes in customer characteristics. The cross-sectional data set contained administrative data on 8,538 corporate customers (219 with suspicious transactions that were subsequently flagged, 64 of which were reported). A binomial logit model was used.FindingsChanges in transaction volume and bank use are significant in predicting subsequent suspicious transactions. Customer credit score changes were significantly positively correlated with the likelihood of flagging and reporting. Change is a stronger indicator of suspicious transactions than the level. Thus, frequent updating of client risk and using a scale rather than risk categories can improve client risk monitoring. The results also showed that the current anti-money laundering (AML) system is size-dependent; the greater the change in customer size, the greater the probability of the firm subsequently engaging in a suspicious transaction.Research limitations/implicationsClient risk classification, monitoring changes in a client’s use of the bank and business risk should receive more attention.Practical implicationsThe authors demonstrate that client risk classifications should be dynamic and sensitive to even small changes, including monitoring the client’s credit risk changes.Social implicationsDirecting AML efforts to clients with characteristics indicating risk and monitoring changes in factors contributing to risk can increase efficiency in detecting money laundering.Originality/valueTo the best of the authors’ knowledge, this is the first study to focus on changes in a firm's use of a bank and link this to the probability of detecting a suspicious transaction.

A systematic review of anti-money laundering systems literature: Exploring the efficacy of machine learning and deep learning integration

Money laundering is a complex issue with global impact, leading to the increased adoption of artificial intelligence (AI) to bolster anti-money laundering (AML) measures. AI, with machine learning and deep learning as key drivers, has become an essential enhancement for AML strategies. Recognizing this emerging trend, this study embarks on a systematic literature review, aiming to provide novel insights into the implementation, effectiveness, and challenges of these sophisticated computational techniques within AML frameworks. A critical analysis of 26 selected studies published from 2018 to 2023 highlights the essential role of machine learning and deep learning in identifying money laundering schemes. Notably, the decision tree algorithm stands out as the most commonly utilized technique. The combined use of both learning models has proven to significantly increase the effectiveness of AML systems in detecting suspicious financial patterns. However, the optimization of these advanced methods is still constrained by issues related to data complexity, quality, and access.