Money Laundering Research Articles

Cryptocurrency and Political Campaign Finance: Opportunities and Risks

This paper explores the intersection of cryptocurrency and political campaign finance, focusing on the opportunities and risks of adopting digital currencies in political fundraising. The research seeks to answer the question: What are the opportunities and risks associated with using cryptocurrency in political campaign finance? The study adopts a qualitative methodology, relying on content analysis of existing literature and case studies of political campaigns that have employed cryptocurrency as a fundraising tool. Key findings suggest that cryptocurrency offers significant advantages, such as global fundraising potential, enhanced transparency through blockchain, and financial inclusivity. However, the risks include anonymity leading to regulatory evasion, foreign interference, and the potential for money laundering. These risks are compounded by inconsistent regulatory frameworks across jurisdictions. The paper concludes that a balanced regulatory approach is essential to leveraging the benefits of cryptocurrency while protecting the integrity of political processes. Future research should explore how regulatory frameworks can be refined to support innovation without compromising electoral transparency.

RMGANets: reinforcement learning-enhanced multi-relational attention graph-aware network for anti-money laundering detection

Given the anonymity and complexity of illegal transactions, traditional deep-learning methods struggle to establish correlations between transaction addresses, cash flows, and physical users. Additionally, the limited number of labels for illegal transactions results in severe class imbalance and other challenges. To overcome these limitations, we propose a reinforcement learning-enhanced, multi-relational, attention graph-aware framework to detect anti-money laundering and illegal trading activities. On the one hand, a data-driven, graph-aware layer establishes long-term dependencies and correlations between transaction graph nodes. Similarity among graph nodes divides the topological graph into three subgraphs. Learning from these subgraphs and converging nodes enriches local, global, and contextual details. Simultaneously, using repeated nodes across the subgraphs enhances interactivity between them, reduces intra-class ambiguity, and accentuates inter-class differences. On the other hand, a reinforcement learning module embedded in the graph-aware layer compensates for the missing details in node features caused by masking operations. Furthermore, the reconstructed loss function addresses significant classification inaccuracies by reducing the weight assigned to easily classified samples. Balancing these issues and individually supervising each component enables the detection framework to achieve optimal performance. The evaluation results demonstrate that our proposed model exhibits optimal detection performance and robustness, such as F1 of 93.85% and 94.39%.

Features of Credit Institutions Carrying out Activities in the Field of AML/CFT/PWF in the Context of the Application of Sanctions and Counter-Sanctions

The article analyzes the impact of sanctions and counter-sanctions on the procedure and standards of banking services, including on the implementation of credit organizations activities in the field of AML/CFT/PWF. Based on the study of relevant legal acts adopted by authorized Russian bodies and domestic legal doctrine, the author shows that in the current situation of inconsistency between applied models of legal regulation and the established principles of regulation of banking relations, credit organizations are forced to revise their previously successfully tested standards and ethical models of customer service. Therefore, the optimal procedure for the normative consolidation of anti-money laundering procedures in the context of the implementation of counter-sanctions have been determined. The trends in sanction (counter-sanction) compliance and aspects of its interaction with financial monitoring in credit organizations are considered, and a form of formalization of the sanctions (counter-sanction) compliance policy in credit organizations is proposed.

A Holistic Network Analysis of the Money Laundering Threat Landscape: Assessing Criminal Typologies, Resilience and Implications for Disruption

Abstract Objective This article visualises money laundering, a crime spanning hundreds of different actors, methods and value instruments, as a network. Possible combinations of money laundering actions and schemes are analysed through a holistic visualisation to draw policy-relevant insights into their prevention. Methods A prior scoping review of money laundering typologies identified 793 activities that a money launderer can take to obfuscate illicit funds. These actions form the basis of a directed network graph showcasing all possible combinations of money laundering schemes. Three network-based analyses are then conducted: (1) centrality analysis, to determine the most ‘important’ money laundering actions as priorities for prevention, (2) resilience analysis to simulate iterative interventions against different money laundering actions to assess their impact on the wider network and (3) subgroup analysis to identify groups of commonly undertaken schemes (i.e. “typologies”). Results Money laundering is found to be a highly resilient process crime, with specific interventions often unable to prevent schemes from displacing to alternative activities to launder their funds. However, benefits of tactically displacing criminals to more easily detectable schemes are discussed. The formulation of money laundering typologies through more empirically robust processes is also motivated, in place of compartmentalised reports that remain the current standard practice. Conclusions Network analysis is motivated as an effective way of visualising complex process crimes with vast criminal opportunities such as money laundering, allowing policy-relevant insights to be drawn in terms of resource-allocation, strategy and prioritisation. Theoretical and policy implications of the current study are discussed in the context of crime scientific theories.

An Exploration of Selected Anti-Money Laundering Role-Players in Zimbabwe

Although Zimbabwe has established several institutions to combat money laundering and related crimes, there is a perception that inadequate measures are taken to apprehend offenders responsible for financial crimes. Institutions such as the Financial Intelligence Unit (FIU), the Zimbabwe Anti-Corruption Commission (ZACC), the Zimbabwe Republic Police (ZRP), the National Prosecuting Authority (NPA) and the Reserve Bank of Zimbabwe (RBZ) have done little to prove that the government of Zimbabwe is resolute in combatting money laundering. On the contrary, it increasingly appears that these institutions are poorly equipped and lack the necessary capacity to enforce and uphold anti-money laundering (AML) measures in Zimbabwe. Further, there appears to be a selective application of the law, with one set of rules for individuals or institutions that are perceived as political adversaries of the incumbent establishment and a different set of rules for the political elite. Consequently, the selective application of the law projects Zimbabwe as a jurisdiction that is somehow tolerant to money laundering, corruption and related financial crimes, thereby lowering and tarnishing the standing of the country in the global economic community of nations. This paper provides a regulatory analysis of the AML role-players in Zimbabwe in order to assess their functions in combatting financial crimes. It also analyses whether these role-players are effective and substantively executing their responsibilities therein. The authors argue that while Zimbabwe is well able to effectively combat money laundering through the even application of the law to all persons regardless of their political or economic standing, it is imperative that its AML institutions operate without fear, favour or prejudice. This is crucial in combatting money laundering and instilling confidence in the general public's perception of AML institutions in Zimbabwe.

Роль банків у процесах тінізації економіки України

Introduction. The relevance of the article is due to the growing role of banking institutions in the process of shadowing the Ukrainian economy, which negatively affects the financial stability of the state. Problem Statement. The shadow economy has been identified as one of the main threats to the economic stability and security of the state. The study of the role of banking institutions in illegal transactions by entities whose purpose is money laundering, using various schemes and methods of financial fraud, is considered from the standpoint of substantiating directions and effective tools for countering shadowing of the economy. The implementation of such measures at the national and corporate level in accordance with international norms will make it possible to strengthen control over banking transactions, increase stability of the banking and financial systems and, ultimately, improve the level of economic security of the country. Purpose. Analysis of the role of banking institutions in the shadow economy, justification of directions and tools for counteracting the participation of banks in the shadow economy. Methods. The methodological basis of the study is the fundamental provisions of economic theory, theories of management and state regulation of the economy, economic and mathematical modeling, the theory of money, finance and credit, scientific achievements on the shadow economy and measures to counteract its manifestations. Results. The study identifies the role of banking institutions and proposes a typology of forms of their participation in the shadow economy, including: legalization of proceeds of crime; use of correspondent accounts; use of fictitious companies and front persons; offshore accounts; lending and deposit operations of banks; securities manipulation; and other financial fraud. The impact of these mechanisms on the economic stability of the state and the banking system is analyzed. A comparative analysis of international experience in combating money laundering is carried out, and effective tools for preventing shadow operations are identified. The trends and factors of shadowing the Ukrainian economy are analyzed. To assess the impact of banking institutions on the spread of the shadow economy, it is proposed to use the Simpson distribution for shadow cash flows and discount rates with the participation of banking institutions. The instruments of counteracting the participation of banks in the processes of shadowing the Ukrainian economy are substantiated. Conclusions. Further research is planned to focus on the impact of modern digital technologies, such as blockchain and cryptocurrencies, on money laundering through banking institutions. A promising area is the development of innovative methods of financial monitoring and identification of suspicious transactions, in particular with the use of artificial intelligence and machine learning. It is also important to study the effectiveness of international cooperation and develop recommendations for improving the legislative framework for combating money laundering through banking institutions.

Insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networks

Distributed ledger technologies have opened up a wealth of fine-grained transaction data from cryptocurrencies like Bitcoin and Ethereum. This allows research into problems like anomaly detection, anti-money laundering, pattern mining and activity clustering (where data from traditional currencies is rarely available). The formalism of temporal networks offers a natural way of representing this data and offers access to a wealth of metrics and models. However, the large scale of the data presents a challenge using standard graph analysis techniques. We use temporal motifs to analyse two Bitcoin datasets and one NFT dataset, using sequences of three transactions and up to three users. We show that the commonly used technique of simply counting temporal motifs over all users and all time can give misleading conclusions. Here we also study the motifs contributed by each user and discover that the motif distribution is heavy-tailed and that the key players have diverse motif signatures. We study the motifs that occur in different time periods and find events and anomalous activity that cannot be seen just by a count on the whole dataset. Studying motif completion time reveals dynamics driven by human behaviour as well as algorithmic behaviour.

Machine learning, banking, industry, digitalization, innovation

Aim: The application of artificial intelligence (AI) offers an encouraging avenue for developments in the banking industry. Methods: The study adopted a scoping review of methodology to provide a comprehensive overview of relevant research regarding the potential uses and challenges of AI in banking. Consumer expectations have evolved, emphasizing features such as payment gateways, swift transactions, and resilient banking services. Results: Major utilization of the AI in banking industry includes enhanced customer engagement, satisfaction, and relationships, personalized financial guidance, smart digital wallets, regulation compliance and collaborative efforts, forecasting, tracking market trends, decision-making and problem-solving, anti-money laundering and fraud detection, automation of repetitive, large and time-intensive tasks, enhanced cybersecurity, and opportunity for digital financial inclusion. Conclusion: Users encounter challenges involving security issues, privacy concerns, and the complexity of technology in AI-driven banking services. Recommendation: While AI holds a crucial role in the banking industry, successful implementation demands ample allocation and consistent, predictable utilization of AI practices.

Harnessing Digital Strategies to Combat Cryptocurrency-Enabled Crimes: Addressing Money Laundering, Illicit Trade, and Cyber Threats

Purpose: This research explores how advanced digital strategies can be harnessed to combat cryptocurrency-enabled crimes, focusing on the use of blockchain analysis, artificial intelligence (AI), machine learning, and enhanced regulatory frameworks to detect, trace, and prevent illegal transactions on cryptocurrency platforms. Materials and Method: The study examines the challenges law enforcement and regulatory bodies face due to the pseudonymous nature of cryptocurrencies and their cross-border complexities. It analyses emerging digital tools that facilitate the de-anonymization of transactions and enable real-time monitoring of suspicious activities. Case studies from recent high-profile cryptocurrency crimes, such as the Silk Road shutdown and recent ransomware attacks, are utilized to highlight the effectiveness of these digital strategies. Findings: The findings indicate that a multi-layered approach, which combines technological innovations with global regulatory efforts, is essential for mitigating risks associated with cryptocurrency as a facilitator of cybercrime. Advanced analytics and regulatory techniques are identified as key resources for detecting and preventing illicit activities. Implications to Theory, Practice and Policy: The research demonstrates practical implications for law enforcement agencies in developing strategies that integrate advanced digital tools to improve their capabilities to manage and investigate cryptocurrency-related crimes. From a policy perspective, the study highlights the importance of creating adaptive regulatory frameworks that can evolve alongside cryptocurrency technology to effectively address the unique challenges it presents in combating cybercrime.

Money laundering and compliance in professional football: the case of Belgium in light of the new EU AML single rulebook

In recent decades, football has evolved from a purely sporting activity into a global economic phenomenon. Along with this metamorphosis, questions of corporate governance and compliance have come to the fore when it comes to the governance of (professional) football clubs. A good example is the consequences of, and efforts to prevent, criminal activities in the form of money laundering. In recent years, the risks of money laundering activities through the professional football sector have often been highlighted by, for example, European Union institutions. In Belgium, an alleged money laundering scandal in professional football led to legislative measures. This contribution considers the application of Belgium’s preventive anti-money laundering framework to professional football in comparison to the novel EU AML Single Rulebook legal framework as applicable to professional football. It concludes that this legal framework is not a miracle solution to the problems facing the sector, and furthermore, the application of this framework raises several questions.

International Journal of Scientific Research in Computer Science, Engineering and Information Technology

This paper explores decentralized finance (DeFi), a fast-growing area powered by blockchain technology that offers a new alternative to traditional financial systems. DeFi removes the need for intermediaries like banks, making transactions more transparent, accessible, and often cheaper. This shift not only reduces costs but also helps improve financial access, particularly for people who are underserved by traditional banking systems. Key elements of DeFi, such as smart contracts and oracles, play a central role in automating processes and enabling peer-to-peer exchanges without needing middlemen. Despite its advantages, DeFi faces several challenges. Smart contracts can have security vulnerabilities, oracles may not always provide accurate data, and there is little consumer protection in place, which raises risks for users. Furthermore, DeFi’s decentralized and often anonymous structure creates regulatory difficulties, especially when it comes to complying with anti-money laundering (AML) and know-your-customer (KYC) standards, which are crucial for ensuring financial safety and preventing illegal activities. This paper examines these issues and proposes potential solutions, such as decentralized oracle networks, regulatory tools embedded within DeFi platforms, and improved scalability techniques. These solutions aim to enhance DeFi’s security while maintaining its core decentralized benefits. The paper concludes by discussing the future of DeFi, stressing the importance of balanced regulations that protect users without stifling innovation. Ultimately, DeFi holds the potential to reshape global finance, making it more inclusive, efficient, and accessible.

Has the application of sanctions against financial institutions involved in money laundering had a deterrent effect?

Money laundering is one of the crimes that has a wide impact, both economically and socially. With the increasing complexity of global financial networks, financial institutions must be more responsible in preventing money laundering practices. The existence of strong and transparent financial institutions is essential to maintain the integrity of the financial system. Using normative juridical research methods, this study analyzes the regulations that govern sanctions and law enforcement practices. The purpose of this study is to evaluate whether the sanctions applied have provided a deterrent effect to financial institutions that violate the law. The results of the study show that although there are several institutions affected by sanctions, in general, the implementation of sanctions has not been effective in providing a significant deterrent effect. These obstacles arise due to the lack of strict supervision, weak international cooperation, and inconsistencies in law enforcement. In addition, the proactive attitude of financial institutions in fulfilling reporting obligations also needs to be improved. Therefore, reforms in the sanctions system and improved law enforcement mechanisms are needed to create a stronger deterrent effect against financial institutions involved in money laundering

Proof Of Criminal Origin Related To Money Laundering In Mutual Fund Investment Activities In Supreme Court Decision Number 2937 K/Pid.Sus/2021

The crime of money laundering in the form of mutual fund investments is a serious crime that can threaten the stability of the financial system and economy. Perpetrators use mutual funds as a means to disguise the origin of illegal funds resulting from criminal acts, such as corruption, which are then invested as if they were legitimate funds. This not only harms the country financially, but can also reduce investor confidence in the mutual fund industry. Therefore, it is important to examine the criminal liability of perpetrators of money laundering in mutual fund investments in order to maintain the integrity of the financial system and provide a deterrent effect for perpetrators of similar crimes. This research is aimed at analyzing the legal regulation of criminal acts of money laundering in mutual fund investment activities in Indonesia, proof of predicate crimes related to criminal acts of money laundering in mutual fund investment activities in Supreme Court Decision Number 2937 K/Pid.Sus/2021, as well as criminal responsibility for perpetrators of these crimes. money laundering crime in mutual fund investment activities in Supreme Court Decision Number 2937 K/Pid.Sus/2021.The research method used is normative juridical research, which is supported by primary and secondary data sources. All legal materials were collected using library research techniques using document study data collection tools. Apart from that, field studies were also carried out using interview methods and analyzed qualitatively.The results of the research and discussion concluded that the legal regulations related to the crime of money laundering in mutual fund investment activities in Indonesia are as regulated in Law Number 8 of 2010. Based on Supreme Court Decision Number 2937 K/Pid.Sus/2021, it can be concluded that the defendant Benny Tjokrosaputro proven to have committed a criminal act of corruption together with other parties in managing investments at PT Asuransi Jiwasraya (Persero) which caused state losses amounting to IDR 16.807 trillion, as well as a money laundering crime of IDR 6.078 trillion, so he was sentenced to life imprisonment and payment of compensation to the state, based on valid evidence at trial and the fulfillment of the elements of the criminal act in accordance with the articles charged.

Implementation of KYC Principles for Indonesian Payment Services Provider

Payment Services Providing is a bright yet simple business to run for it requires a merely reliable system to execute transactions among banks as well as other entities. Nevertheless, these services are also utilized by criminals such as terrorists and/or money launderers. This paper is aimed to discuss the complex yet necessary execution of Know Your Customers Principle for a PSP. This is a library research to collect and analyze secondary data covering law and business literature published within 5 years as well as any legislation concerning PSPs. The research concluded that KYC Principles are mandatory for PSP with activities to manage funds such as in the form of e-wallet. Secondly, KYC Principles are not mandatory for PSPs with only activities as payment aggregator, billing provider and/or remittance services provider. At this point, it is suggested firstly to complete the regulations concerning KYC Principles for Indonesian PSPs. Secondly, utilization of KYC Services requires advanced technology concerning the way to obtain customers data as well as its storage and access availability.

Navigating the ethical and governance challenges of ai deployment in AML practices within the financial industry

The rapid deployment of Artificial Intelligence (AI) in Anti-Money Laundering (AML) practices within the financial industry presents significant ethical and governance challenges that must be navigated effectively. As financial institutions increasingly adopt AI technologies to enhance their AML efforts, concerns regarding data privacy, algorithmic bias, and transparency emerge. This review explores the ethical implications of AI in AML and offers governance strategies to mitigate risks while ensuring compliance with regulatory frameworks. One of the primary ethical challenges in deploying AI for AML is the potential for algorithmic bias. AI systems trained on historical data may inadvertently perpetuate existing biases, leading to discriminatory practices in transaction monitoring and customer profiling. This raises serious concerns about fairness and equity in the financial sector. Addressing algorithmic bias requires the implementation of rigorous testing and validation processes to ensure AI systems function impartially across diverse populations. Data privacy is another critical issue. The extensive data collection required for effective AML monitoring raises questions about the protection of sensitive customer information. Financial institutions must establish robust data governance frameworks that prioritize privacy and comply with regulations such as the General Data Protection Regulation (GDPR). Ensuring transparency in how data is used and providing clear communication to customers about data practices is essential for building trust. Effective governance frameworks are crucial in navigating these ethical challenges. Financial institutions should adopt a multi-disciplinary approach that includes ethical guidelines, compliance measures, and risk management strategies. Establishing oversight committees can help ensure that AI deployment aligns with ethical standards and regulatory requirements. Furthermore, ongoing training for employees on the ethical use of AI in AML can foster a culture of responsibility and accountability. This review highlights the need for a balanced approach to AI deployment in AML, emphasizing the importance of ethical considerations and governance structures. As the financial industry continues to evolve, addressing these challenges will be essential for maintaining trust, ensuring compliance, and leveraging AI’s potential to enhance AML practices effectively.

Compliance with anti-money laundering laws and their impact on banking operations in Pakistan: banking expert’s opinion

Purpose Despite sound anti-money laundering (AML) regulations and membership in the financial action task force, money laundering remains a severe concern faced by Pakistan because of its weak AML regime. This led to an investigation of the perceptions of banking expert’s regarding the AML law’s costs and complexity, mapping of compliance cost and measures taken by Pakistani banks in compliance with AML laws. Design/methodology/approach A socio-legal study approach was adopted based on library-based, and interviews using social media platforms to treasure the experts’ opinions regarding the impact of AML laws on baking operations in Pakistan. A semistructured interview-based questionnaire was handed out to Pakistani banking officers. Findings The findings revealed that, for banks to meet the AML obligation, they must overcome challenges including cost and complexity. Nevertheless, these costs are less expensive than the penalties assessed for noncompliance. Practical implications Following AML regulations can help the banking industry grow, which, in turn, attracts more foreign investment by boosting consumer confidence. However, it is difficult and expensive to comply with these requirements. The current research hopes that the financial regulatory authorities of Pakistan will adopt more effective measures for efficient compliance with AML laws. Originality/value The impact of compliance with AML laws on banking operations in Pakistan was examined in this study for the first time using banking officers’ opinions from various banks best of author’s knowledge.

Geospatial data in AML risk management: A review of applications and best practices

This review explores the role of geospatial data in enhancing Anti-Money Laundering (AML) risk management, focusing on its applications, best practices, and strategic implications for organizations. Geospatial data, combined with advanced analytical tools, provides critical insights into transaction patterns, customer locations, and geographic risk factors, enabling financial institutions to detect suspicious activities more effectively. The paper examines how geospatial data integration supports compliance with regulatory frameworks, bolsters transaction monitoring systems, and facilitates enhanced due diligence processes. Through a systematic review of existing literature and case studies, this paper identifies key applications such as identifying high-risk jurisdictions, mapping transaction flows, and detecting cross-border money laundering schemes. Best practices are highlighted, including leveraging geospatial analytics in conjunction with machine learning algorithms, optimizing data governance, and ensuring interoperability across systems. The findings suggest that organizations adopting these practices are better equipped to combat money laundering by improving risk assessment, reducing false positives, and enhancing investigative efficiency. Additionally, the review discusses the challenges of data privacy, regulatory compliance, and the technical complexities associated with geospatial data integration. The paper concludes by outlining future prospects, recommending further advancements in real-time geospatial analytics, cross-sector collaborations, and the adoption of emerging technologies to strengthen AML frameworks. This review serves as a comprehensive guide for financial institutions seeking to incorporate geospatial data into their AML strategies, driving both compliance and operational efficiency.

Cryptocurrency and Money Laundering

Cryptocurrency, a form of virtual currency, is increasingly pervasive in the modern society. People can use Bitcoin, Ether or Dogecoin to buy a range of products and services with ease and convenience. Yet, beneath the popularity of cryptocurrency is an emerging socio-legal concern: money laundering. Using the information collected from web sources, this brief paper taps into how cryptocurrency money laundering works and what measures can be undertaken to curtail digital crime. In view of the extant information, the paper found that a mixer is required as a ‘middle man’ to convert identifiable crypto-tokens into unidentifiable clean ones thereby delivering them to new wallet(s). In this manner, the origins of these tokens can be obscured, and the new tokens can be accessed and used legally. Many platforms and channels can serve as the mixer to proceed with illegal activities, including casinos, dark web marketplaces, and p2p networks. In considering the ways to tackle such digital crime in jurisdictions with limited oversight, the paper proposes to adopt measures in the Anti-Money Laundering initiatives already implemented in Hong Kong, the United States, and Singapore – especially, the need to strengthen recordkeeping to enhance the traceability and trackability of cryptocurrencies. Meanwhile, laws and rules associated with digital crime shall also be reshaped for risk mitigation.

Optimizing Test Data Management Strategies in Banking Domain Projects

The steady rise in processing power over the past 20 years has resulted in an enormous volume of data. Furthermore, anybody may now easily create and consume material in any format thanks to recent advancements in Web technology. Large volumes of data are regularly gathered by banking systems, including trade finance data, SWIFT and telex communications, client information, details about transactions, risk profiles, credit card details, limit and collateral details, the compliance or Anti Money Laundering (AML)-related data, and limit and collateral details. Every day, thousands of choices are made at banks. These choices pertain to credit, default, beginning a relationship, investments, AML, and illicit funding, among other things. To make these crucial choices, one must rely on a variety of data and drill down capabilities offered by the banking systems. We created a set of specifications for the kinds of data that should be included in a product catalogue. We ascertained what data the departments need by using a survey and questionnaire of the retailer's staff. We ensured that there was no one standard for the information organisation and then put out our own plan. This enormous amount of data may be mined for information and intriguing patterns, which can then be used to the decision-making process. This article examines and summarises a number of data mining methods that have applications in the banking industry. An overview of data mining methods and procedures is given. It also sheds light on how these methods may be applied in the banking industry to facilitate and enhance decision-making.

Suspicious activity reporting in emerging economies: the case of Iran

PurposeCountries with high levels of corruption can experience collusion between powerful elites and accountants to conceal, disguise and clean the proceeds of criminality. This study investigates the willingness of accountants to report evidence of money laundering in an emerging economy, Iran, notwithstanding potential personal and professional risks implicit in such due diligence. It evaluates the relevance of personal characteristics of accountants to the propensity to report, and the implications for policy makers in terms of audit team composition.Design/methodology/approachThe methodology is quantitative. Data was gathered by means of a suspicious activity scenario-based questionnaire administered to 1,128 of Certified Public Accountants in Iran, of which 281 responses were received. Four hypotheses were tested relating to the implications, if any, of gender, age, education and working experience for the propensity to report red flags indicative of money laundering.FindingsData revealed that accountants were generally more willing to report activity indicative of money laundering than was anticipated in an environment perceived to be characterised by professional and personal risks. Older accountants are more risk averse and more likely to report suspicious activity than younger counterparts who tend to disregard borderline indicators of money laundering. A significant red flag indicator of money laundering is a client's reluctance to provide information regarding controlling shareholders, debtors and creditors or to explain contrived and opaque corporate structures. Audit teams may be more effective when gender-balanced: female accountants tend to be more willing to report suspicious activity than male counterparts, reducing the risk of interference by powerful elites.Research limitations/implicationsThe time frame over which the research was conducted was a single year; if it had been conducted over several years it may have revealed more nuanced and evolving reporting behaviour. The study was limited to Iran: a cross-comparison with another emerging economy or economies may have revealed useful contrasts.Originality/valueThe study contributes to behavioural accounting research in emerging economies. Limited empirical data is available regarding the influence of personal characteristics of accountants on their willingness to report suspicious activity in corrupt environments where personal safety and professional security may be at risk from powerful elites. It evaluates the implications of these for suspicious activity reporting policy, and for improving the effectiveness of the scrutineering role of audit teams. An innovative questionnaire was designed which may be suitable for future comparable research in emerging economies.