Discourse Using Blockchain Technology for the Enforcement of Money Laundering Crimes in Indonesia

PurposeMoney laundering has affected the economy in different ways, where the fraudulent activities are either domestic or abroad, resulting in financial instability globally. Anti-money laundering (AML) system is applied to detect and report any suspicious transactions. There are numerous approaches, techniques and algorithms in AML that are applied to fight against money laundering. This study aims to understand, identify and document the AML techniques applied to detect and prevent money laundering activities.Design/methodology/approachA systematic literature review is applied for searching articles based on methods used for AML from the electronic database platform. For review, data is considered from journal articles, books and conference proceedings with a time framework from 2014 to 2024.FindingsIn total, 53 papers were selected in the domain of money laundering concepts, issues and techniques of AML. The review articles are on the techniques of AML, such as machine learning, data mining, graph networks and artificial intelligence, which are applied to detect and prevent money laundering issues.Originality/valueMoney laundering, being a global issue, is a threat to the economy and society. Detecting money laundering activities is utmost required; this study contributes in selecting the articles that are involved in the application of techniques of AML in detecting and preventing money laundering activities. The results of this study can provide support instruments to identify the better AML techniques that are useful for practitioners and industry experts working in the AML domain. Further research can be explored with other AML techniques.

Various countries today are threatened by money laundering as a transnational crime known as borderless crime. This is because the impact of money laundering is not ordinary. The crime of money laundering has a considerable impact disrupting the stability of the economy and social life and even damaging the world economic order. However, various legal problems in dealing with money laundering often occur in Indonesia. With these various problems, it shows that Indonesia needs a legal reformulation related to money laundering as classified as a transnational crime. Based on this background, this research will discuss the problems of how money laundering can be classified as a transnational crime, the problems with its law enforcement, and the legal policy of money laundering as a transnational crime reformulation in Indonesia. This study uses a descriptive normative research method with a qualitative approach. The results show that the idea of reformulation in law enforcement of money laundering as a transnational crime in Indonesia is through the reconstruction of Mutual Legal Assistance or MLA between the Indonesian government and various countries in the world and the application of other international instruments, such as extradition and confiscation. Then regarding the problem of money laundering in law enforcement, it is necessary to reformulate the authority of the Corruption Eradication Commission or KPK to prosecute money laundering crimes originating from the criminal act of corruption because the Anti-Money Laundering Law has not yet clearly regulated the law enforcers who are authorized to carry out prosecutions for the Crime of Money Laundering. So to overcome this law enforcement problem, it is necessary to reform the money laundering law.

Artificial intelligence (AI) is being actively implemented in anti-money laundering (AML) systems due to its potential to improve the detection of suspicious transactions. The article examines AI's effectiveness in detecting and reducing financial crimes of private military companies. The research employs machine learning (ML) algorithms and neural networks, anomaly detection methods, and economic impact assessment. A combination of supervised and unsupervised learning methods enables the creation of accurate predictive models for detecting money laundering anomalies. The results show that AI models outperform traditional rule-based systems, reducing false positives by 30% and increasing high-risk detection by 25%. This proves the advantages of AI over conventional anti-money laundering methods, which often cannot adapt quickly. The research emphasizes the transformative impact of AI on anti-money laundering systems, optimizing accuracy and resource allocation. Further research should focus on improving AI algorithms and their application in new financial technologies.

PurposeA fundamental element of international anti‐money laundering (AML) systems is the requirement that financial institutions file suspicious transaction reports (STRs) with financial intelligence units. Although the Financial Action Task Force (FATF) has established global standards, there is a range of national laws, practices and experiences with STR systems. The purpose of this paper is to assess the effectiveness of national STR systems, by using Switzerland as a case study.Design/methodology/approachPrimary source documentation, complemented by observations at FATF meetings, are relied on to evaluate STR systems.FindingsThe FATF ratings of a country's compliance with international standards are objective, expert driven and consistent in application, but are limited as performance measures in that they ignore the costs of AML and STR measures. The effectiveness of the Swiss STR system is questionable because of serious under reporting of suspicious transactions. The Swiss STR system is efficient to the extent that there is a high usage of STRs and that large amounts of money are automatically frozen under the mandatory reporting obligation.Research limitations/implicationsEvaluation of national STR systems is limited because of a lack of reliable statistics on the extent of money laundering.Practical implicationsThis paper is addressed to policy makers who are concerned with assessing the effectiveness of STR systems. Future research would deal with STR systems in developing countries and the role of STRs in uncovering the financing of terrorism.Originality/valueInsider/outsider description of the FATF mutual evaluation process. Compilation and interpretation of statistical data on STR systems, as performance measures.

Anti-money laundering (AML) systems confront a persistent challenge: the high volume of false positives that impose substantial compliance burdens on financial institutions. Traditional rule-based approaches, while foundational, frequently generate alerts that necessitate extensive manual review, diverting resources from genuine illicit activities. Recent advances in Artificial Intelligence (AI) have demonstrated measurable improvements AI-enhanced AML models have reduced false positives by up to 40% in pilot implementations, while increasing true detection rates through adaptive learning. This analysis scrutinizes advanced Artificial Intelligence (AI) strategies engineered to enhance enforcement precision by significantly reducing false positive rates in AML operations. The discussion covers the integration of machine learning, deep learning, Natural Language Processing (NLP), and Explainable AI (XAI) techniques, assessing their capacity to discern complex patterns indicative of financial crime more effectively than conventional systems [1]. Furthermore, the examination addresses critical regulatory and ethical considerations, including data privacy, algorithmic bias, and the necessity for human oversight, aligning these technological advancements with established frameworks such as those from the Financial Action Task Force (FATF) and the European Union (EU) AI Act [2]. Observations indicate that AI-driven methodologies offer promising avenues for optimizing AML efficacy, providing their implementation accounts for technical barriers, operational integration, and evolving regulatory landscapes. The paper concludes with recommendations for policy and practice, advocating for a balanced approach that leverages AI's analytical power while preserving transparency and accountability in financial crime deterrence. Client risk classification models, for instance, demonstrate improved accuracy with accounting and credit data [1]. This paper contributes an integrative framework linking AI transparency, precision enforcement, and regulatory adaptability, offering a roadmap for balanced innovation in financial crime deterrence.

The establishment of a special institution that handles money laundering in Indonesia, called the Financial Transaction Reports and Analysis Center (PPATK), as a central institution in the anti-money laundering system in Indonesia is regulated in Article 18 of the Republic of Indonesia Law No. 8 of 2010 concerning Prevention and Eradicating Money Laundering. The Financial Transaction Reports and Analysis Center (PPATK) is also an independent institution that has the duty and authority to prevent and eradicate money laundering, and to assist law enforcement relating to money laundering that is directly responsible to the President. The formulation of the problem in this research is: how is the financial service provider (Bank) in an effort to help the Financial Transaction Reports and Analysis Center (PPATK) prevent the occurrence of money laundering crimes and what obstacles and how the efforts of financial service providers in an effort to assist the Reporting and Analysis Center Financial Transactions (PPATK) prevent money laundering. The research method used in this study is normative legal research, namely by describing existing problems which are subsequently discussed and studied based on legal theories and then linked to the applicable laws and regulations in legal practice. The conclusions in this study are as follows: Financial service providers (Banks) in an effort to assist the Financial Transaction Reports and Analysis Center (PPATK) to prevent the occurrence of money laundering crimes has the main task of helping law enforcement agencies in preventing and overcoming money laundering crimes by providing intelligence information resulting from the analysis of reports submitted to the PPATK. Barriers to financial service providers in efforts to help the Financial Transaction Reports and Analysis Center (PPATK) prevent money laundering, among others: the presence of loopholes in financial service industry regulations, barriers from other laws and regulations, obstacles in international cooperation both by executive and judiciary and inadequate resources to prevent and find out about money laundering activities, for example the absence of a financial intelligent unit.Keywords: Banking, PPATK and Money Laundering

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.

This paper examines the potential of nighttime light (NTL) data as an alternative data source to predict the number of money laundering events. The study is based on the assumption that money laundering as one of financial crime categories is linked to economic development, and previous research has explored the relationship between NTL and both economic data and crime. Panel regression analysis with random effects was used to investigate the potential of NTL data to estimate money laundering activity, which was measured using Suspicious Transaction Reports (STRs) data as a proxy variable. The results suggest that NTL data can be a promising tool for estimating money laundering activity, providing new insights into the use of alternative data sources in predicting this illegal activity. The findings of this research could also contribute to the development of more effective anti-money laundering strategies by law enforcement and policymakers.

One form of crime that has become a primary focus of criminal efforts is money laundering. The process of improvement continues to evolve until today, with recent changes outlined in Law Number 8 of 2010 concerning the Prevention and Eradication of Money Laundering. In addition to the national scale, efforts to combat money laundering are also carried out internationally. A significant step in international cooperation to combat money laundering is the establishment of the Financial Action Task Force (FATF) on Money Laundering. However, despite these collective efforts, there are still several challenges and obstacles in preventing money laundering globally. Differences in laws and regulations between countries, as well as the complexity of global financial pathways, are some factors that complicate the eradication efforts. Therefore, this research will focus on examining how the regulation of money laundering crimes differs between Indonesia and Malaysia and how the regulations compare in both countries. Specifically, this normative legal research generally focuses on the analysis of legal documents. The formulated issues can be outlined as follows: How is the regulation of money laundering crimes in Indonesia and Malaysia, and what is the comparison of the regulations on money laundering crimes in Indonesia and Malaysia.

The effectiveness of Anti-Money Laundering (AML) systems in emerging economies is a critical factor in safeguarding financial stability, promoting transparency, and combating illicit financial flows. Limited institutional capacity, insufficient regulatory enforcement, high levels of informality, and the quick digitalization of financial services are some of the particular difficulties emerging economies face while implementing AML. There are still gaps in legislative frameworks and actual enforcement, despite the fact that many nations have embraced worldwide AML standards established by organizations like the Financial Action Task Force. System efficiency is frequently decreased by poor agency coordination, a shortage of qualified workers, and inadequate usage of cutting-edge technologies. Promising advancements can be seen in recent reforms, capacity-building programs, and the use of fintech-based monitoring systems. In order to effectively detect, prevent, and prosecute money laundering activities in a globalized financial environment, this study looks at the advantages and disadvantages of AML systems in emerging economies. It emphasizes the need for improved technological integration, stronger governance, and international cooperation.Keywords: AML, Financial, FIU

Purpose This paper aims to understand and document the state of the art in the anti-money laundering (AML) systems literature. Design/methodology/approach A systematic literature review (SLR) is performed using the Saudi Digital Library. The outputs published as conference proceedings, workshop proceedings, journal articles and books were all considered. The final sample size after omitting out-of-scope selections was 27 documents, which mainly span from 2015 to 2020. Findings The sample is discussed based on a categorization, which demarcates solutions, machine learning, data sources, evaluation methods, implementation tools, sampling techniques and regions of study. Originality/value This SLR could serve as a useful basis for researchers and salient decision-makers, who are seeking to understand the nature and extent of the currently available research into AML systems.

This research is based on the fact of Indonesia's great efforts in combating money laundering and terrorism financing practices that brought Indonesia to be recognized as the 40th member of the FATF. The purpose of this study is to analyze the threats, vulnerabilities and risks of money laundering and terrorism financing crimes and describe the potential impact of Indonesia's involvement as a member of the FATF on the prevention, supervision and enforcement of these crimes. Using a descriptive qualitative approach, this study uses secondary data from related institution documents and a number of previous research journals as a basis for analyzing the problem under study. The results showed that money laundering and terrorism financing crimes are very high when viewed from the trend in the number of crackdowns in recent years. In addition, the vulnerability related to several problems that are still faced such as weak declarations on carrying cash, large objects of supervision, problems with data input in the system, not standardized supervisory infrastructure, limited authority of the DGT and not integrated reporting applications among related institutions. The potential impact of Indonesia's involvement as a member of the FATF on the prevention, supervision and enforcement of crimes is to increase the credibility of the country's economic management as the main investment attraction. Safeguarding Indonesia's international political interests in influencing international policy in accordance with Indonesia's national interests. And become a catalyst that encourages or "forces" Indonesia to focus on strengthening supervision and law enforcement through various efforts and strategies.

Environmental crimes have a wide impact on the country's losses in social and economic development and threaten the sustainability of living things and the ecosystem. In addition, environmental crimes also produce follow-up crimes, namely criminal acts of money laundering (TPPU). Based on the Financial Action Task Force (FATF) July 2021 Report on Money Laundering from Environmental Crimes, environmental crime is one of the crimes that is estimated to generate the most profitable income in the world, which is around USD 110 to 281 billion annually. Unfortunately, the handling of money laundering cases related to environmental crimes in Indonesia remains suboptimal, as evidenced by the disproportionate number of environmental crimes addressed by money laundering authorities in this sector. This study examines how law enforcement of forestry and environmental crimes needs to be strengthened by a follow-the-money approach. This study used descriptive and qualitative analysis methods involving secondary data from various related institutions and primary data from unstructured interviews with several investigators in the field of environment and forestry. The findings of this study indicate that law enforcement in the areas of forestry and environmental protection, particularly in the application of anti-money laundering measures, remains suboptimal. This inadequacy is largely attributed to the limited experience of investigators in handling environmental cases. Whereas, adopting a follow-the-money approach in environmental investigations could significantly enhance the effectiveness of these efforts, allowing for a more comprehensive uncovering of past illegal activities. Additionally, this approach has the potential to identify a broader network of involved parties, including the identification of beneficial owners.

The multimedia communication technologies have been widely used in the anti-money laundering (AML) field to improve the efficiency and security of the business transactions. To reduce the cost of massive multimedia processing and communications, it is desirable to allow multiple financial industries (FIs) to share the AML resources, including human, computation, communication, and storage resources, and cooperate with each other to complete the transaction tasks. In this paper, the optimal AML resource management among peer FIs towards the maximal AML rewards is studied. Specifically, an AML resource allocation model (AMLRAM) based on semi-Markov decision process(SMDP) is proposed, where the system state is represented by a tuple, i.e., the number of High-Risk Operation (HRO), the number of Low or Moderate Risk Operation (L/MRO), and the current event type (i.e., the arrival of HRO or L/MRO suspicious transaction report which needs to be further checked, and the departure of HRO or L/MRO suspicious transaction report which has been checked and releases the occupied AML resource) in the AML field. The maximal long-term rewards of the system is derived, and the optimal AML resource allocation decision among peer FIs is made to achieve the maximal system rewards. Extensive simulations validate our analysis.

Money laundering poses a serious threat to financial stability and requires effective national frameworks for prevention. This study investigates how the quality of legal and institutional frameworks affects the effectiveness of national anti-money laundering (AML) systems and their implications for financial risk management. We conducted an empirical analysis of 132 jurisdictions in 2024 using the Basel AML Index (AMLI) and the WJP Rule of Law Index (RLI). The Random Forest method was employed to model the relationship between rule-of-law indicators and AML risk levels. Findings reveal a significant inverse relationship between rule-of-law indicators and AML risk levels, with an overall classification accuracy of 69.6%. The model performed best for low-risk countries (precision 75%, recall 92.31%), moderately for medium-risk countries (precision 65.22%, recall 78.95%), but failed to identify high-risk jurisdictions, suggesting a legal institutional “threshold” necessary for effective AML functioning. Key predictors included protection of fundamental rights and mechanisms for civil oversight, with strong negative correlations between AML risk and criminal justice impartiality (−0.35), civil justice fairness (−0.35), and equality before the law (−0.41). These results show that legal factors strongly affect AML risk and can guide regulators in improving risk-based standards, enhancing regulatory certainty, and managing financial risk.