Exploring Money Laundering - Its Impact On Economic Performance And The Role Of Advanced Technologies In Prevention
Author: LawSphereX
Category: Banking & Finance
Published:
Updated:
EXPLORING MONEY LAUNDERING: ITS IMPACT ON
ECONOMIC PERFORMANCE AND THE ROLE OF ADVANCED TECHNOLOGIES IN PREVENTION Mr. Suryavir Gahlawat, Research Scholar, School of Law, Sushant University, Gurugram, India. Dr. Anjali Sehrawat, Associate Professor, School of Law, Sushant University, Gurugram, India.
ORCID: 0000-0002-0739-25752*
ABSTRACT
This study examines the multifaceted issue of money laundering, a financial crime encompassing corruption, tax evasion, and terrorist financing, which poses severe risks to global economic and financial stability. Employing a mixed-method approach, the research first conducts a bibliometric analysis of 1,238 publications retrieved from the Scopus database to identify publication trends, prolific authors, contributing countries, and emerging thematic developments through Biblioshiny tools. In parallel, a qualitative case analysis of high-profile money laundering scandals explores their economic repercussions and assesses the effectiveness of risk-mitigation measures. The results indicates growing scholarly attention to money laundering, with distinct thematic clusters and county level contributions.
Citation analysis highlights the influence of prolific authors, while the case analysis underscores how large-scale money laundering activities destabilize national economies and highlight the critical role of advanced technologies in enhancing detection and prevention mechanisms. By integrating bibliometric and qualitative evidence, the study contributes a comprehensive perspective on the evolution of money laundering research, uncovers gaps in the existing literature, and offers recommendations to strengthen global antimoney laundering frameworks. Keywords: Money Laundering, financial and economic performance, Artificial Intelligence, Anti-money laundering, Bibliometric analysis, Qualitative analysis.
1.0 Introduction
Money laundering is a complex financial crime, it involves disguising the illicit origins of money generated through illegal activities such as corruption, tax evasion, and terrorist financing (Rusanov and Pudovochkin, 2021). The economic cost of money laundering is immense. It not only distorts financial markets but also reduces foreign direct investment (FDI), hampers tax revenue collection, and erodes public trust in financial systems (Vaithilingam & Nair, 2007). With the emergence of new financial instruments and platforms, especially blockchain and AI-based systems, the modus operandi of money laundering has become increasingly sophisticated (Tiwari et al. , 2024). Yet, the academic literature has not adequately kept pace with these evolving patterns, especially in the Indian context where high-profile scandals highlight systemic vulnerabilities.
Moreover, money laundering undermines economic stability and increasingly exploits technological advancements such as blockchain and AI-based systems, which have significantly transformed the methods used to obscure financial trails (Tiwari et al. , 2024). Despite these evolving threats, the research landscape and prevention strategies remain underexplored, particularly within the Indian context, where recent high-profile scandals reveal deep systemic vulnerabilities. This study is therefore motivated by the urgent need to address these economic and technological dimensions of money laundering and to bridge existing gaps in academic and policy-oriented research. To bridge this gap, the present study adopts a hybrid methodology-combining bibliometric analysis with qualitative case studies, to map the evolving research trends and real-world impact of money laundering.
The inclusion of qualitative case analysis is essential because it complements bibliometric trends by analysing how real-world money laundering scandals expose systemic loopholes and deeper understanding of the economic repercussions and evaluate the effectiveness of anti-money laundering measures in practices. This integration ensures that the research not only identifies thematic gaps in the literature but also connects them to lived realities, thereby strengthening both academic and policy relevance. The first part of the study analyses 1,238 research papers retrieved from Scopus (2009–
2024) using Biblioshiny, identifying publication trends, key themes, leading authors, countries,
and journals. The second part incorporates qualitative case studies of prominent Indian highprofile money laundering cases, such as the PNB scam and INX Media scandal, to map trends and evaluate their economic impacts.
The study addresses the following research questions (RQs), each targeting an important gap in the existing literature: RQ1 address the lack of systematic reviews on emerging trends and research focuses in the domain of money laundering between 2009 to 2024 (Ahuja et al. , 2023) RQ2: What are the leading authors and leading countries contributing in the area of money laundering. RQ3: What is the most frequent word cloud used in the research articles and how the word evolves into the money laundering? RQ4. What are the citation analysis and what are the author’s contribution to the journals? RQ5: What are the evolving themes surrounding money laundering? RQ5: What are the gaps and potential research directions in the context of money laundering. (Zolkaflil et al.
, 2019) The bibliometric findings of the study suggest that academic research predominantly centers on anti-money laundering (AML) compliance, cryptocurrency, and regulatory gaps in emerging economies, while case studies highlights the regulatory loopholes in India’s banking and FDI system. Moreover, this study makes a novel contribution by being the first to integrate bibliometric and qualitative methods to analyze both the academic and practical dimensions of money laundering’s impact on national economies, particularly in India. The findings not only enrich academic discourse but also inform policymakers about the urgent need for technology-driven AML frameworks, including the use of AI, blockchain, and machine learning tools to strengthen preventive and enforcement mechanisms.
The structure of the present article is organized as follows: The first section highlights the introduction and background information, which is followed by comprehensive review of relevant literature. The thirds section of the study primarily outline the research methodology and outcome of the study. Lastly the paper concludes by discussing the main insights derived from the results, that provides implication and directions for future research. The structure of the present paper is organized as follows: the first section presents the introduction, followed by a comprehensive review of relevant literature. The third section primarily outline the research methodology and highlights the key findings of the study. Lastly, the paper concludes by explaining the main insights derived from the results, that provides implications and directions for future research and practice.
2 Conceptual Framework and Literature Review
2.1 Money Laundering and Economic Performance:
Money laundering has a intense and detrimental impact on economic performance (Davidescu & Manta, 2023). Furthermore, money laundering poses a significant threat to financial and banking institutions by increasing the potential for frauds, damage institutional reputation and compromising the integrity of banking systems (Aluko & Bagheri, 2012). The risks often associated with discouraging foreign direct investment, as international investors perceive nations with high levels of financial crime as unstable and legally uncertain and dangerous. Moreover, the money laundering erodes the government tax base as illegal financial flows that often escape taxation, which directly impact the state investment in essential services as a healthcare, education and infrastructure.
In many instances, large inflows of illegal money contribute to inflation and exchange rate volatility exclusively in emerging nations with less robust financial systems. In essence, widespread laundering activities weakens regulatory institutions, enhance corruption and undermine public trust in governance, all of which hinder long-term economic development and stability. Institutional theory was first proposed by Di Maggio and Powell, 1983 where he explained about the coercive isomorphism, which refers to the formal and external pressure from regulatory and enforcement bodies. For example, in the context of emerging and developing nations high levels of corruption and regulatory capture and a lack of institutional capacity undermine the effectiveness of AML (anti-money laundering laws).
2.2 Money Laundering and Its Types
The present section sheds lights on the type of money laundering methods. The money launders used to hide the illegal or black money by Smuggling, cash incentive business, shell companies, trade-based money laundering, tax evasion, gambling and virtual gambling are some of the popular types money laundering. In the Tax evasion process where the criminals uses havens or different methods to evade payment. According to the report published by Panama papers 214000 tax havens that involves wealthy people, public officials from 200 nations1. Similarly, shell companies is also a popular method of money laundering where the shell corporation is a corporation without active business operations or significant assets2. Moreover, trade based money laundering involves falsifying invoices or misrepresenting the volume or nature of transactions to move money across borders.
Overall, the above moneylaundering methods are used in combination to layer, integrate and conceal the origins of illicit funds.
2.3 Role of Technology
In the current landscape, technology plays a dual role in the realm of money laundering and anti-money laundering efforts. Advanced technologies such as artificial intelligence (AI), Machine Learning (ML) and blockchain are transforming the detection and prevention of financial crimes. On the other hand, technologies like cryptocurrencies have introduced new channels for laundering illicit funds, complicating regulatory oversight. According to the study proposed by Dyntu and Dukyi (2018), cryptocurrencies . Whereas, AI and machine learning (ML) provides innovative tools for enhancing AML processes. A study by Sambrow and Iqbal (2022), stated that AI-driven systems can process vast volumes of transaction data in real time, it enable to identify patterns and anomalies, that may indicate and check suspicious behaviour.
Machine learning models are increasingly used by financial institutions to detect unusual transaction and even predict fraudulent activity. Additionally, blockchain technology provides an transparent ledge that can improve the traceability of transactions and limit the scope for money laundering in digital ecosystems. However, the adopting of these technologies also introduce regulatory and ethical challenges. For example, decentralized finance (DeFi) platforms and peer-to-peer exchanges often operate without robust AML controls, increasing the risk of exploitation by criminal networks. Moreover, the rapid evolution of fintech and digital assets has often outpaced regulatory frameworks, leading to gaps in compliance and enforcement.
To address these challenges, policymakers must balance innovation with regulation, ensuring that the benefits of technology are harnessed to strengthen AML regimes while closing the loopholes exploited by bad actors.
3. Research Methodology
We study the emerging trends of money laundering using bibliometric technique. Bibliometric
1 h#ps://www.unit21.ai/blog/types-of-money-laundering
2 h#ps://www.investopedia.com/terms/s/shellcorpora>on.asp
is a popular and widely accepted technique that used to analyze the evolution of the mindset and to identify the important components such as authors, journals and countries in different research domain ( Cobo et al. , 2011, 2012; Morris and Van der Veer Martens, 2008). It is a systematic domain-based review which analysed an large amount of published research articles to highlight the statistical figures, trends , trends and identify knowledge gaps and future research avenues. The bibliometric is broadly categorized into performance analysis, science mapping and network analysis (Donthu et al. , 2021a). The present study will employ performance analysis to identify the emerging trends in money laundering. For performance analysis we use the Biblioshiny software app. To conduct bibliometric analysis, a datasets retrieved from the reliable and strong database (Linnenluecke et al. , 2022).
In this respect, Scopus is the most appropriate tool because the global research community uncontroversially acknowledges it and because it is widely used by academicians and researchers worldwide (Olawumi & Chan, 2018). The bibliometric study’s is organized and divided into three stages: 1. Data collection, 2. Software and data filtering and (3) stage mainly comprises of processing, evaluation and presentation; to identify the significant research articles.
3.1 Data Collection
The first step in bibliometric research is to select an appropriate and credible source, from which research articles is extracted (Morant et al. , 2017). In the present study, we selected Scopus scientific database for literature extraction. Scopus is considered as a widely extensive databased, which is primarily used by academicians and researchers worldwide (Olawumi & Chan, 2018). Moreover, Scopus is an interdisciplinary directory with over 69 million archives. The next step is selecting a query keyword, which includes a filter to prevent irrelevant literature (Cheng et al. , 2016). The query keyword for the current paper was {Money Laundering} and {Anti-money Laundering} in the title, abstract and keywords of documents extract 3000 research articles in the initial search. The final search string was mentioned in (Table 1).
Additionally, keyword validation was conducted by testing alternative terms such as "financial crime," "illicit finance," "terrorist financing," and related synonyms to ensure comprehensive coverage and to minimize potential omission of relevant literature. Articles were extract from 2009-2024. Following are the detail mentioned in Table 1 of the inclusion and exclusion criteria using PRISMA flowchart adopted from (Sousa et al. ,2019).
The rationale for selecting the timeframe from 2009 to 2024 is both strategically and contextually selected to capture the emergence and evolution of digital financial systems, including cryptocurrencies, online banking and fintech platforms. During this period the academic interest in money laundering has expanded beyond traditional financial institutions to more on technological and digital dimension. 3.1.1.Screening: The screening of the research articles established a proper list of inclusion and exclusion criteria before the bibliometric analysis. One of the key inclusion criteria was those research articles that published in English, as English is a widely recognized and universal language. It makes the content more accessible for review and analysis.
3.1.2 Data Filtering and Data Analysis with Biblioshiny Software
An initial raw datasets for 3000 research articles was compiled and transferred into an Excel file, to simplified the organization, identification and elimination of duplication entries, reducing the total of 2300 research articles. Subsequently filtering process of the research articles was applied based on the journal/ source name, retaining only those published in journals indexed in Quartile1 and Quartile 2 list for further analysis, the second round of manual cleaning resulted in a refined datasets of 1500 articles. In the last phase of screening, articles were further refined by evaluating titles and abstract to identify the studies particularly focus on the impact of money laundering on a nation’s economic and financial growth. This rigorous screening resulted in a final dataset of 1,238 articles selected for bibliometric analysis and interpretation.
3.1.3 Data Analysis
Bibliometric analysis for the present paper was done using Biblioshiny software, as it is one of the most used software for Bibliometric analysis (Donthu et al.,2021b; Nees & Waltman, 2017; Talwar et al., 2023). Biblioshiny software makes bibliometric visualization simple.
Table 1: PRISMA protocol details Criteria Description Source Scopus Years 2009-2024 Searching Terms/Search Strings ( Money Laundering ( ( "illegal money" OR "black money" OR "anti money laundering " OR " Prevention of anti money laundering act*" OR " financial institutions *" OR "banking institutions*" OR "Economic Impact*" OR " platform*" OR " regulatory bodies *" OR " artificial intelligence *" OR "bitcoin fraud *" OR " banking scams" OR "cryptocurrency*" OR "online scams" OR "" OR "FATF" OR "Regulatory bodies" OR "smuggling" OR "terrorism financing" OR "bitcoin scam* OR " *" OR "two-sided market*" OR "multi-sided market*" OR "cybercrim*" OR " government regulatory bodies *" OR " high profile bank scams *" OR " *" OR LIMIT-TO ( SUBJAREA , "SOCIAL SCIENCES AND HUMANITIES" ) OR LIMIT-TO ( SUBJAREA , "ARTS" ) AND ( LIMIT-TO ( DOCTYPE , "ar" ) ) AND ( LIMIT-TO ( LANGUAGE , "English" ) ) Articles Identified through database Search Inclusion Criteria Language – English, Source Type – Journals, Document Types - Articles, Subject Area –Social Sciences, Humanities, Economics, Econometrics and Finance.
Money Laundering impact on economical and financial growth of the nation. (Final set)
1238 for Bibliometric Analysis
4.0 Analysis
and Interpretation: Performance Analysis: The line graph in the figure 1 represents the annual scientific production of the research articles on the topic of money laundering from the time-period of 2009 to 2023. The overall charts shows the steady growth in the publications over the years whereas the most rapid growth increased from the time period 2018-2020. The study conducted by Isolauri and Ameer,(2022) study showed that the money laundering is transnational phenomenon that has gained the attention among researchers and academicians during past two decades. Figure 1-Scientific Production Source: Author’s own work Country Production Over Time: A Comparative Analysis The visual representation of the charts from the Figure 2 and Figure 3 highlights the scientific production on the topic of money laundering over a period of fifteen years, from 2009 to 2024 country.
The charts reflects the five important countries have shown an increasing trend in scientific production. The USA has the highest level of scientific production throughout the period. Followed by the nation like United Kingdom has shown a steady increase, starting from a relatively high level in 2009. Countries like Australia, Malaysia, and the Netherlands have experienced more rapid growth in recent years. The charts provides a valuable insights that the significant growth of scientific production in all five countries suggests a growing emphasis on research and publication activities. According to Kwiek (2021), factors such as research funding, scientific infrastructure, collaboration networks, and government policies have significantly influenced the scientific production of research articles in the United Kingdom and the United States.
These countries' higher levels of scientific output can likely be attributed to these factors. Overall, the charts reveal a global trend towards increased scholarly attention to money laundering. This growing emphasis on research suggests a greater recognition of the critical importance of understanding and combating this complex issue. Figure 2-Country Production Over Time Source: Author’s own work Figure 3- Scientific Production Source: Author’s own work Affiliation Productions Over time: The visual representation of the line graph in (Figure 4) and (Figure 5) explained the scientific production of five affiliations over a period of fifteen years, from 2009 to 2025. The production is measured in the number of articles published. The charts reflects the significant increase in the trend in the scientific production over years.
Sumy State University has consistently maintained the highest level of scientific production throughout the period. Teichmann International (Schweiz) AG, and Universiti Teknologi MARA, have experienced more rapid growth in recent years. The rapid growth Universiti Teknologi MARA highlights the potential for affiliations to catch up with established scientific leaders. The increasing number of scientific production in all five affiliations revealed a growing emphasis on research and publication activities in the area of money laundering. The varying rates of increase may be attributed to factors such as research funding, scientific infrastructure, collaboration networks, and institutional policies. In summary, the visual representation of the line graph shows a clear upward trend in scientific production for all five affiliations.
This suggests a growing emphasis on research and publication activities in the topic of money laundering.
Figure 4: Publication production over time Source: Author’s own work Figure 5- Relevant Affiliations Source: Author’s own work Most Contributing Authors In the Domain of Money Laundering: The visual representation of the charts represent in the (Figure 6) explained the author productivity in the area of money laundering. The charts showed that the TEICHMANN FMJ and NAHEEM MA are the most contributing authors, with 22 and 21 documents respectively, where as HAMIN Z and LEVI M follow closely behind with 17 and 16 documents each. Furthermore, the charts reflects the overall productivity which shows a decreasing trend in author productivity only a few authors published a good numbers of research articles where less authors published a small number of documents. In summary, this chart provides a valuable overview of the author productivity within a specific field or dataset.
It highlights the most prolific authors and reveals trends in authorship patterns. Figure 6: Relevant Authors Source: Author’s own work Most Cited Documents: The visual representation of the (figure 7) represents a globally cited documents in the area of money laundering issues. The charts highlights the most influential documents and reveals trends in citation patterns.
Figure 7: Cited Documents Source: Author’s own work Trend analysis: Money Laundering and Related Crimes: The visual representation of the chart provides the trend topics analysis report to crimes associated with money laundering over a fourteen years, range from 2010 to 2024. A charts showed a term frequency indicates a topic related to money laundering has gained a significant attention among researchers and academicians. In 2023, the terms such as "politically exposed persons," "law enforcement," and "financial institutions" emerged as the most frequently mentioned topics it suggests researchers have bridged the gap between money laundering and its links with political figures, and banking and financial institutions(Hinterseer, K. , 2002).
Notably, "money laundering," "anti-money laundering," and "terrorism financing" have consistently been prominent areas of focus throughout the decade. However, the visual representation of the charts represents a significant gained in research attention on money laundering and its associated with crime topics from 2020 onwards. The visual representation of the chart also highlights a sudden rise in terms related to cryptocurrency, it indicates that with the surge in digital transaction has showed a concern between money laundering and it association with cryptocurrency trading(Dyntu and Dykyi, 2018).
Additionally, "machine learning" and "KYC" (Know Your Customer) have gained a lots of attention among researchers ,it reflects a growing interest in technological solutions for combating financial crime (Sambrow and Iqbal,2022) The significant increase in the terms related to "blockchain" aligns with the rise of cryptocurrency and its potential applications in finance. While "terrorism financing" has experienced fluctuations, with peak interest in 2012 and 2016, "organized crime" and "corruption" have remained consistently relevant. In summary, the trend topic analysis report revealed a heightened awareness and focus on money laundering and related crimes. The emergence of cryptocurrency and blockchain as key topics underscores the evolving nature of financial crime and the need for innovative approaches.
The enduring prominence of traditional terms related to "money laundering" and "terrorism financing" highlights their significant association with money-laundering crimes. Figure 8: Trend Topics Source: Author’s own work Word Cloud Analysis: A visual representation of the charts highlights in the (Figure 9) and (Figure 10) represent a comprehensive analysis of the most frequently words related to money laundering and associated crimes. The graph charts provides a valuable insights into the current research landscape on money laundering. Based on the SCOPUS dataset of the money laundering literature, the most frequent words such as "money laundering" and "anti-money laundering" consistently dominate, emphasizing their central importance in the field. The interconnectedness between money laundering and corruption is evident, as "corruption" ranks as another prominent term.
Additionally, "compliance" and "organized crime" frequently appear, highlighting their significance in understanding and combating these complex issues. The evolving nature of financial crime is reflected in the emergence of "cryptocurrency" and "terrorist financing" as relevant terms. These topics underscore the need for ongoing research and adaptation to address emerging challenges. Overall, the chart provides a valuable overview of the key terms associated with money laundering and related crimes. Figure 9: Relevant Words Source: Author’s own work Figure 10: Word Cloud Source: Author’s own work TREE MAP
ANALYSIS:
(Figure 11) illustrates the visual representation of the tree map charts provides the frequency of words related to money laundering and associated crimes. Tree map analysis report shows that money laundering and anti-money laundering are the two most dominating words that emphasize central importance of the areas in research and practice. Moreover the charts highlight the linkage between money laundering and its associated crimes. Money laundering serves crimes such as drug trafficking, tax evasion etc. Moreover, the charts also reflects emerging trends, such as the growing relevance of cryptocurrency and blockchain, and the potential applications of machine learning, highlights the evolving landscape of money laundering and the need for innovative approaches.
The presence of terms like "terrorism," "tax evasion," "fraud," and "bribery" further demonstrates the diverse range of criminal activities associated with money laundering. Smaller segments, including "sanctions," "proceeds of crime," and "Nigeria," reveal niche areas of focus within this broader field. Overall, the charts provide a comprehensive overview of the key themes and trends in money laundering research. It highlight the central importance of money laundering and anti-money laundering measures, while also it highlights the interconnectedness of the money laundering issues with criminal activities and the evolving nature of financial crime. Figure 11- Tree Map Analysis Thematic Analysis: The visual representation of the matrix diagram (Figure 12) that categorizes various themes related to money laundering and corruption.
Based on the positioning of the themes in the matrix, author provide the valuable insights from the matrix. According to the position, the charts primarily categorised into four themes, First, Niche themes the quadrant are positioned in the lower left corner, it revealed themes like shell companies, anti-money laundering, FATF, compliance, Its suggest that these are emerging or evolving themes in the context of money laundering. Moreover, the themes such as Russia, Ukraine and shell companies positioned in the lower left corner suggest the geopolitical events in the global landscape. Second, theme is known as basic themes, which are positioned in the upper right corner that includes the topic like money laundering, corruption, organized crime it indicates that these themes are both central and well-developed. They are fundamental to the discussion of money laundering and corruption.
Furthermore, the topic like machine learning, artificial intelligence and financial services are positioned in the middle of the matrix it indicates that the themes are moderately relevant and developing. The above themes are increasingly being used in the fight against money laundering and corruption. Last theme is known as motor themes, this themes located on the right side of the matrix, it indicates the underlying factors that contribute to money laundering and corruption such as political instability, economic inequality and technological advancements. Altogether, the chart provides a valuable overview of the key themes related to money laundering and corruption. It highlights the emerging, declining and evolving themes.
Moreover, the charts also indicates that technological advancements, such as machine learning and artificial intelligence, play an important role in the fight against money laundering and corruption.
Figure 12- Thematic Analysis Research Gaps: Based on the thematic and word cloud analysis, the present study sheds light on the research gaps. The word cloud analysis highlights the cryptocurrency, blockchain, machine learning, and artificial intelligence as evolving areas, but there is a lack of in-depth research on how the advanced technologies can be effectively applied to combat money laundering. Specifically, more studies are needed to explore the practical integration of machine learning and AI in identifying, preventing, and mitigating money laundering activities in real-world contexts. In addition, the thematic analysis mainly focus on geopolitical influences on money laundering, the presence of words like Russia, Ukraine, and shell companies suggests a gap in the research that investigates the impact of geopolitical events on global money laundering practices.
Lack of academic research work done in the domain of geopolitical instability and it impact on money laundering. So in future, academic scholars required to explore the role of geopolitical instability and international conflicts in facilitating or hindering money laundering activities across borders. Lastly, the interconnectedness of money laundering with other criminal activities, such as terrorism, tax evasion, fraud, and bribery, is evident, yet further research is needed to investigate the exact mechanisms through which money laundering facilitates these crimes. Additionally, how anti-money laundering (AML) measures strengthened to address the broader spectrum of financial and organized crime should be a focus of future studies.
The above research gaps explained opportunities for future studies to deepen understanding of the complexities surrounding money laundering, while also addressing the evolving technological, geopolitical, and economic dimensions that influence this global issue.
5.0 Case Analysis
Research Methodology
This study employs both bibliometric analysis and case studies to develop a comprehensive understanding of the research landscape and practical dimensions of money laundering; enables an in-depth exploration of the research landscape and prevention strategies in this field. In the present study we selected two major high profile money laundering cases -PNB and INX Media because they represent some of the largest and most consequential banking and foreign direct investment (FDI)-related scandals in India’s recent history. These cases resulted in an significant economic consequences, highlighted systemic weaknesses in regulatory oversight, and drew significant national and international attention (Goldman Sachs, 2019).
Their magnitude, complexity, and impact on public trust and financial markets make them particularly relevant for studying anti-money laundering frameworks and enforcement challenges in India.
5.1 Case analysis- 5W2H Framework
5W2H is a popular as well as quality management tools with an objective to examine the problems with the aim to reach a feasible solutions (Gayathri, 2018). 5W2H stands for 5Ws and 2Hs i. e Who, What, Where, When, Why, Hoe and How much. This tools provides a structured framework to gain a clear understanding of the various components of a problem. Structures Questions as follows in Table 2 Table 2-5W2H Framework When does it finish? How much time it has been taken? Etc etc 5W2H OF THE PNB BANK SCAMS In the PNB scam case description, as outlined in (Table 3), the 5W2H analysis has been applied to examine the various aspects of the case. This methodology helps to systematically evaluate the key components and uncover feasible solutions to the issues identified in the scam. Table 2-5W2H Framework (PNB) The “What” questions are broadly categorised intro three main section: What Happened?
On 14th Feb, PNB Punjab National Bank (PNB) revealed that it had uncovered fraudulent transactions worth . 77 billion (approximately Rs 11,400 crore) at one of its Mumbai branches. In its complaint to the Central Bureau of Investigation, the bank implicated the firms and individuals linked to billionaire jeweler Nirav Modi, along with his uncle Mehul Choksi were accused of organizing such massive fraud with the involvement of PNB bank officials. What is the problem? PNB employees issued fraudulent Letters of Undertaking (LOUs) – a type of bank guarantee in the international banking system that allows a customer to raise short-term credit from another Indian bank’s foreign branch. Using these fake LOUs, foreign branches of several Indian banks, including Axis and Allahabad Bank, extended dollar loans to PNB.
These loans were used to fund PNB’s Nostro accounts, from which funds were then transferred to certain overseas entities. A Nostro account refers to an account maintained by an Indian bank (in this case, PNB) with a foreign bank. What is the scope of the problem? a. PNB is now responsible for bank guarantees amounting to Rs 11,400 crore, which it must repay to various institutions, including State Bank of India, Allahabad Bank, and Union Bank, with payments due in the coming months. b. The scam has had a significant impact on the banking, jewelry, and insurance sectors. c. It raises concerns about the credibility of Public Sector Banks and the effectiveness of regulators, including the Reserve Bank of India (RBI) and the Securities and Exchange Board of India (SEBI). The “What” questions are broadly categorised intro three main section: When did it happen?
29th Jan, 2018 PNB filed a fraud complaint against Nirav Modi group firms. On 4th Feb 2018, CBI issues a lookout notice against Nirav Modi. On 5th Feb 2018, PNB informed the PNB informed the stock exchanges about a Rs. 281 crore fraud involving the Modi group. When did it start? As per the case, the fraudulent activities began seven years prior when two junior Bank employees of PNB issued unauthorized guarantees to Nirav Modi’s group firms. The scam went undetected until January 2018, when a new employee requested collateral security for a fresh guarantee. The representatives of Modi’s firms claimed that collateral had never been demanded before, prompting an investigation that revealed hundreds of unauthorized guarantees issued over the years. When did the Fraud stop?
The fraudulent activities ceased in January 2018 after the new employee’s request for collateral led to an investigation, uncovering the scam. Is it a continuous or intermittent problem? Nirav Modi’s fraudulent activities has been ongoing got seven years. Additionally, PNB had previously faced a major scam involving the Winsome Diamond group in 2013. Fraudulent activities are not new to the banking sector, which has experienced scams of varying scales over time. Hence, in PNB bank case , this is a continuous problem. Is it linked to a specific customer usage? The PNB scam is linked to Nirav Modi case. He is the billionaire in this scams. Nirav Modi is the founder and creative director of Nirav Modi chain of diamond jewellery and retail stores. The “Who” questions are broadly categorised intro three main section: Who faced the problem: PNB faced a fraud amount of Rs 11,400 crores.
It is expected to bear the financial burden of the lost money, as per banking circles. Who reported the problem? The fraud issue was detected and reported by PNB to the RBI and the CBI. Who is the customer? Who is the final customer? The direct customers of PNB and other banks involved, including firms and individuals availing of banking services, are considered the customers. The final customers include parties affected by the fraudulent guarantees. Who are the stakeholders? Key stakeholders in the case including- direct customers of the bank, employees, Board of Directors, Auditors and Creditors and Government of India. Who are the parties involved? The main parties involved in the fraud include: • Former PNB employees who issued unauthorized guarantees • Nirav Modi and his firms
Allahabad Bank and Axis Bank, which honored the fraudulent guarantees. The “Where” questions are broadly categorised intro four main section: Where did the fraudulent activities happen? The fraud occurred within the operating environment of PNB, facilitated by internal processes and systems. Is it in-house or at the customer level? The issue can be classified as in-house, as PNB officials cleared SWIFT transactions on request. This raises concerns about the authenticity and integrity of PNB’s system for handling Letters of Credit. Where are the stakeholders located? The two PNB employees involved directly misused SWIFT, a global financial messaging service for cross-border transactions, bypassing the Core Banking System (CBS) that records and processes daily banking transactions. Which other locations should be considered?
Attention should also focus on the information exchange channels between PNB, SWIFT, and the other banks (e. g. , Allahabad Bank and Axis Bank) that honored the fraudulent guarantees.
Letters of credit. Fake letter of undertaking. SWIFT network The “How” questions are broadly categorised intro one main section: How did it happen? Operational risk (OR) indicates a failure in any of the banking systems, processes or people. The “Why ” questions are broadly categorised intro one main section: Why is this a problem? The internal banking system lacks robustness, making it vulnerable to fraudulent activities. Punjab National Bank (PNB) is now liable for the fraud amount, causing panic among customers about the safety of their deposits, despite the bank's assurance to honor all legitimate commitments.
The INX Media case revolves around allegations of corruption, money laundering, and financial irregularities. Violation: INX Media received more FDI than approved and made unauthorized downstream investments. Crime: Bribery and misuse of power to facilitate illegal approvals Why : Motivation behind the Scam. Motivation for Violations: Financial gain through kickbacks and leveraging power for personal benefit. Corruption Drivers: Weak enforcement of financial regulations, collusion between businesses and political figures, and lack of transparency. Who: Parties involved in INX Media scam. Key Individuals involved in this scam. Karti Chidambaram: Allegedly facilitated violations in exchange for kickbacks. P. Chidambaram: Accused of misusing his position as Finance Minister.
Peter and Indrani Mukerjee: Media barons accused of conspiring to secure illegal FIPB approvals. Investigating Agencies: Enforcement Directorate (ED) and Central Bureau of Investigation (CBI).
When did the scam started? The scam was started in 2007, during the FIPB approval process.
Legal proceedings, arrests, and investigations have continued over several years, with key arrests made between 2017-2019 Where did the scam happen? Location of Crime: The violation occurred during FDI approval for INX Media in India. Investigations and Legal Proceedings: Conducted across multiple locations in India, including Delhi, where the CBI and ED are headquartered. How the execution of the scam started? INX Media violated FDI terms by accepting funds beyond the approved limit and made unauthorized investments. Alleged bribes were routed through shell companies linked to Karti Chidambaram. How much the scam has a financial impact? The exact quantum of the bribes and unauthorized FDI is significant but varies in reports. The case involves the attachment of assets worth crores of rupee.
The financial and reputational damage to PNB has been immense. The bank faces the burden of the ₹11,400 crore loss, coupled with eroded customer trust and regulatory scrutiny. Stakeholders impacted include PNB’s employees, customers, board of directors, auditors, and the Government of India. Other banks like Allahabad Bank and Axis Bank, which honored the fraudulent guarantees, were also adversely affected. The case exposed vulnerabilities in global banking practices, as the SWIFT network, a critical financial messaging system, was exploited for fraudulent activities. Similarly, the INX Media case reflected the misuse of official discretionary powers and the routing of illicit funds through shell companies abroad, facilitated by digital communication channels.
These cases not only inflicted severe financial and reputational damage on the institutions involved but also eroded public trust in the banking and governance systems. Stakeholders impacted include bank employees, customers, auditors, board members, and the Government of India. From an institutional theory perspective, the failure to conform to evolving norms—such as adopting advanced financial technologies and enforcing regulatory isomorphism—allowed informal practices to override formal mechanisms. To address these failures, key reforms are needed. These include integrating SWIFT with CBS for real-time transaction verification, enhancing employee accountability through regular training, and implementing robust internal audits and regulatory oversight.
Such measures are essential to restore institutional legitimacy, rebuild public confidence, and ensure systemic resilience against future frauds.
6.0 Discussion
The present study integrate two methodology, in the bibliometric analysis the study concluded that in today’s scenario money laundering related activities lead to a substantial threat to economic and financial systems, this serious issues has gained significant attention from researchers and policymakers across globe. The performance analysis of the bibliometric study stated that research articles publications has shown a remarkable growth over the period from
2009 to 2023. The upward trend in research articles established that the growing academic
engagement with money laundering and related crimes. Moreover, the comparative analysis of country-level scientific production between 2009 and 2024, found that United States of America have a maximum research output on money laundering, followed by the United Kingdom. However the performance analysis trends indicate that money laundering has become a key concern for policymakers in developed countries, As these nations have witnessed a rapid boom in digital and financial technologies such as cryptocurrency. With the rapid expansion of financial technologies the developed nations have intensified their focus on AML research, it highlights a broader global recognition of the importance of addressing the complexities associated with this crime. The second section of the study based on the qualitative study of the high profile moneylaundering cases.
The Punjab National bank and the INX Media cases are two high-profile money laundering scandals that emphasize the regulatory failures in India’s financial and bureaucratic structures. The PNB scams highlights the Indian banking infrastructure depends on the manual processes. The lack of integration between critical systems such as technology and Core Banking system. Moreover, the case also exposed the lack of robust internal external audits and whistleblowing mechanisms that could have raised the anomalies earlier. INX media scams emphasize how the political and bureaucratic misused their powers for personal and financial gains. Additionally, the case also raise critical questions about the effectiveness of India’s foreign direct investment regulatory framework and no proper mechanism to prevent the misuse of bureaucratic power.
7.0 Recommendations
The present study sheds lights on the challenges of money laundering activities and its negative impact on nation’s economic development. Both analysis (Bibliometric and qualitative) emphasize to integrate the advanced based technology including blockchain and AI-based fraud detection and the linking of SWIFT and Core Banking Systems (CBS) for real time transaction tracking infrastructure in the financial and banking systems. However, adoption of advanced technology must be incorporate by training employees. Secondly, PMLA act should be amended to incorporate stringent provisions that address and prevent the misuse of political power for facilitating money laundering activities. Additionally, financial institutions should encourage ethical organizational culture, supported by robust enforcement mechanisms. Overall, the PNB and INX Media cases have served as wake-up calls.
Government and policymakers must catalyze structural reforms that prioritize transparency, accountability, and resilience across India’s financial and bureaucratic systems.
8.0 Limitations
The present study provide valuable insights by integrating bibliometric and qualitative case methodologies. Despite the rigorous systematic literature review, the present study has certain limitations. First, the qualitative case highlights into two major cases, which may not fully represent the diversity or scale of money laundering issues across other sectors or regions. Similarly, bibliometric analysis was confined to articles published in Q1 and Q2 journals, potentially excluding relevant contributions from lower-tier or region-specific journals. Finally, the study is limited by its cross-sectional nature, capturing insights up to 2024 without realtime data updates. As financial technologies and regulatory landscapes continue to evolve rapidly, continuous research will be necessary to keep pace with emerging money laundering trends and countermeasures.
9.0 References
Ahuja, D. , Bhardwaj, P. and Madan, P. , 2023. Money Laundering: A Bibliometric Review of Three Decades from 1990 to 2021. Smart Analytics, Artificial Intelligence and Sustainable Performance Management in a Global Digitalised Economy, 110, pp. 55-72. Ahuja, S. , Chan, Y. E. and Krishnamurthy, R. , 2023. Responsible innovation with digital platforms: Cases in India and Canada. Information Systems Journal, 33(1), pp. 76-129. Aluko, A. , & Bagheri, M. (2012). The impact of money laundering on economic and financial stability and on political development in developing countries: The case of Nigeria. Journal of Money Laundering Control, 15(4), 442-457. Bantekas, I. , & Keramidas, G. (2006). International and European financial criminal law (p. 22). Lexis Nexis–Butterworths. Caiado, R. G. G. , de Freitas Dias, R. , Mattos, L. V. , Quelhas, O. L. G. and Leal Filho, W. , 2017.
Towards sustainable development through the perspective of eco-efficiency-A systematic literature review. Journal of Cleaner Production, 165, pp. 890-904. Cassella, S. D. , 2018. Toward a new model of money laundering: Is the “placement, layering, integration” model obsolete?. Journal of Money Laundering Control, 21(4), pp. 494-497. Chang, S. , Pierson, E. , Koh, P. W. , Gerardin, J. , Redbird, B. , Grusky, D. and Leskovec, J. , 2021. Mobility network models of COVID-19 explain inequities and inform reopening. Nature, 589(7840), pp. 82-87. Chen, X. , Miraz, M. H. , Gazi, M. A. I. , Rahaman, M. A. , Habib, M. M. and Hossain, A. I. , 2022. Factors affecting cryptocurrency adoption in digital business transactions: The mediating role of customer satisfaction. Technology in Society, 70, p. 102059.
Cobo, M. J. , López‐Herrera, A. G. , Herrera‐Viedma, E. and Herrera, F. , 2011. Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for information Science and Technology, 62(7), pp. 1382-1402. Davidescu, A. A. , & Manta, E. M. (2023). The Effect of Money Laundering on the Economic and Financial Performance of an Economy: A Bibliometric Analysis. In Smart Analytics, Artificial Intelligence and Sustainable Performance Management in a Global Digitalised Economy (pp. 203-231). Emerald Publishing Limited. Donthu, N. and Gustafsson, A. , 2020. Effects of COVID-19 on business and research. Journal of business research, 117, pp. 284-289. Dyntu, V. and Dykyi, O. , 2018. Cryptocurrency in the system of money laundering. Baltic Journal of Economic Studies, 4(5), pp. 75-81. Dyntu, V. , & Dykyi, O. (2018).
Cryptocurrency in the system of money laundering. Baltic Journal of Economic Studies, 4(5), 75-81. Hinterseer, K. , 2002. Criminal finance: the political economy of money laundering in a comparative legal context (Vol. 15). Kluwer Law International BV. Isolauri, E. A. and Ameer, I. , 2022. Money laundering as a transnational business phenomenon: a systematic review and future agenda. Critical Perspectives on International Business, 19(3), pp. 426-468. Kwiek, M. , 2021. What large-scale publication and citation data tell us about international research collaboration in Europe: Changing national patterns in global contexts. Studies in Higher Education, 46(12), pp. 2629-2649. Kwiek, M. , 2021. What large-scale publication and citation data tell us about international research collaboration in Europe: Changing national patterns in global contexts. Studies in Higher Education, 46(12), pp.
2629-2649. Linnenluecke, M. K. , Marrone, M. and Singh, A. K. , 2020. Conducting systematic literature reviews and bibliometric analyses. Australian Journal of Management, 45(2), pp. 175-194. Montero-Navarro, A. , González-Torres, T. , Rodríguez-Sánchez, J. L. and Gallego-Losada, R. ,
2021 A bibliometric analysis of greenwashing research
a closer look at agriculture, food industry and food retail. British Food Journal, 123(13), pp.547-560. Morris, S.A. and Van der Veer Martens, B., 2008. Mapping research specialties. Annual review of information science and technology, 42(1), pp.213-295. Norris, M. and Oppenheim, C., 2007. Comparing alternatives to the Web of Science for coverage of the social sciences’ literature. Journal of informetrics, 1(2), pp.161-169.
Nova-Reyes, A. , Muñoz-Leiva, F. and Luque-Martínez, T. , 2020. The tipping point in the status of socially responsible consumer behavior research? A bibliometric analysis. Sustainability, 12(8), p. 3141. Olawumi, T. O. and Chan, D. W. , 2018. A scientometric review of global research on sustainability and sustainable development. Journal of cleaner production, 183, pp. 231-250. Rusanov, G. , & Pudovochkin, Y. (2021). Money laundering in the modern crime system. Journal of money laundering control, 24(4), 860-868. Sambrow, V. D. P. and Iqbal, K. , 2022. Integrating Artificial Intelligence in Banking Fraud Prevention: A Focus on Deep Learning and Data Analytics. Eigenpub Review of Science and Technology, 6(1), pp. 17-33. Su, W. S. , Hwang, G. J. and Chang, C. Y. , 2022. Bibliometric analysis of core competencies associated nursing management publications.
Journal of Nursing Management, 30(7), pp. 2869-2880. Tiwari, M. , Ferrill, J. and Allan, D. M. , 2024. Trade-based money laundering: a systematic literature review. Journal of Accounting Literature. Vaithilingam, S. and Nair, M. , 2007. Factors affecting money laundering: lesson for developing countries. Journal of Money Laundering Control, 10(3), pp. 352-366. Walker, J. , 1999. How big is global money laundering?. Journal of Money Laundering Control, 3(1), pp. 25-37. Waltman, L. , 2016. A review of the literature on citation impact indicators. Journal of informetrics, 10(2), pp. 365-391. Williams, P. , 1997. Money laundering. South African Journal of International Affairs, 5(1), pp. 71-96. Zolkaflil, S. , Omar, N. , & Syed Mustapha Nazri, S. N. F. (2019). Implementation evaluation: a future direction in money laundering investigation.
Journal of Money Laundering Control, 22(2), 318-326. Zupic, I. and Čater, T. , 2015. Bibliometric methods in management and organization. Organizational research methods, 18(3), pp. 429-472.