Money Laundering in the Age of Cybercrime and Emerging Technologies

Abstract

Historically, money laundering has been the financial crime of most significant interest in the international sphere due to the abrupt amounts of money it involves, the damage it causes to the institution’s reputation, and the government spending to combat it. Numerous international organizations, such as the Financial Action Task Force (FATF) and the United Nations (UN), stipulate standards and norms to regulate its prevention and detection. With the emergence of the 4th industrial revolution, technologies such as artificial intelligence, the Internet of Things (IoT), intelligent apps, cloud computing, and cybersecurity play roles in opposite directions in committing and combating money laundering. While artificial intelligence provides innovative models and algorithms capable of predicting money laundering activity, the Internet of Things and intelligent apps seem to create new means for cyber money laundering where cybersecurity comes as a mitigating measure. Bearing this in mind, the current chapter presents an overview of the impact of emerging technologies and cybercrime in executing and fighting against money laundering.

1. Introduction

Money laundering is a key issue for governments and institutions worldwide due to its negative impact on the reputation of institutions and the economy. It is defined as the concealment of the illegal origin of money [1]. Money laundering is usually associated with activities such as corruption, bribery, terrorism, and arms and drug trafficking. But other activities less mentioned, such as human trafficking, tax evasion by companies/individuals, and the illegal sale of gasoline, are also common in money laundering crimes. Resources from illicit activities are spent in cash or immediately deposited in the financial system. When the amounts are small, it is expected to see launderers making small cash purchases without needing a financial intermediary. That is, the small amounts of cash can be used to purchase goods such as food and appliances or transferred to another person to pay a peer-to-peer debt, and thus, the money is automatically laundered. When the amounts are significant, and for security reasons, the launderers deposit the money in the financial system to exploit its vulnerability. Once in the financial system, the illegal money must go through a complex process to be laundered.

The primary stages of money laundering within the financial system are placement, layering, and integration. Financial institutions in member countries of international regulators such as the Financial Action Task Force (FATF) and the United Nations (UN) must detect money laundering in the placement and layering stages. The traditional methods for the above detection task are rule systems that depict a risky transactional profile. Their most significant disadvantage is the generation of high false positives and futile investigation costs due to being static systems that do not adapt to changing criminal behavior. Nevertheless, with the emerging technologies, new artificial intelligence techniques arrived to correct the weaknesses of traditional detection methods. For instance, Refs. [2, 3] show how implementing supervised and unsupervised machine learning algorithms in financial institutions has minimized their false positive rates and costs.

At the same time that criminals try to obscure the illicit origin of resources through layering and integration, their resources in the financial system are subject to another risk that is not discussed very often. Cybercriminals can hack savings and debit accounts, and the stolen money can be transferred to other placement accounts to have complete control over the resources. This hacking activity is illegal and executed in the cyber environment thanks to the new technologies that allow criminals to steal personal identity data and access keys. Then, the money stolen can be distributed to accounts from national or international banks to be layered even more into the financial system to obscure its illegal origin as much as possible. Finally, the criminal can integrate the money into legal activities online, such as making electronic transfers to pay for a car or house, paying credit loans, buying mutual funds, contracting private pension plans and insurance, etc. The latter is an example of money laundering developed entirely in the digital space, from obtaining resources throughout placement and layering to integration. In this way, cyber money laundering arises because at least one of its components occurs in the digital realm using technological means [4, 5].

The technology that makes cyber money laundering possible is the Internet of Things (IoT), which provides mobile applications that connect institutions’ servers with clients and mobile cell phones. An account in a particular bank’s mobile application is similar to accounts created on Facebook or Instagram. It is only needed to create a username and password. Before the emergence of mobile technology, clients made transactions at bank branches. The financial institution analyzed the legitimacy of the transactions received and was responsible for protecting and securing the resources. When using mobile applications, clients are responsible for protecting their access codes and passwords, but they are ordinary individuals, not cybersecurity experts. Hackers and cybercriminals leverage this ignorance to steal information and resources through various technological means.

As mentioned above, two emerging technologies act opposite regarding the cyber money laundering crime. While artificial intelligence provides supervised and unsupervised machine learning algorithms to detect transactions related to money laundering, the IoT contributes with mobile applications to facilitate the cyber money laundering originated by account hacking.

After this introduction, the current chapter presents an overview of the impact of emerging technologies and cybercrime in executing and fighting against money laundering. This overview is presented through Section 2, which explains the traditional concept of money laundering and its execution methods. Section 3 describes the leading emerging technologies. Section 4 shows emerging technologies’ positive impact on money laundering prevention and detection. Section 5 explains the money laundering transformation due to emerging technologies and cybercrime. Finally, the chapter ends with the main conclusions and remarks.

2. Understanding money laundering

Money laundering is a complex process that involves intelligent individuals using various techniques and methods to conceal the illicit origin of money. Understanding this activity is vital for governments and financial institutions seeking to mitigate its adverse effects. The perpetuation of money laundering can damage the reputation of financial institutions and subject them to fines and penalties when authorities discover a crime before them [6]. Also, countries with high incidences of money laundering scare away the intention of investors who perceive insecurity and unstable asset returns.

Combating money laundering represents a budget burden for governments that invest in long and tortuous criminal investigations through the financial intelligence units. An efficient and precise fight against money laundering implies an understanding of its origin, which is an illegal activity, a conduct that is prohibited by social norms. The most common illicit activities that jump out when we talk about money laundering are drug/weapons trafficking, corruption/bribery, and tax evasion. Criminal groups commit drug/arms trafficking made up of civilians, while corruption/bribery is more frequently observed in politicians and government authorities. Tax evasion, on the other hand, can be observed in both legal entities and individuals. These illegal activities generate economic resources that are estimated in billions. According to Ref. [7], the drug market moves between 200 and 600 billion dollars in the USA. In Mexico, according to Ref. [8], the amounts involved in acts of corruption and bribery are estimated at around 84 million dollars and 1.68 billion dollars, respectively, especially when companies have contact with security authorities (police). Estimating an approximate range of tax evasion is complicated because this activity is carried out in the informal economy and involves unreported transactions. However, the OECD in 2019 for 90 countries reported approximately 4.9 trillion dollars related to tax evasion [9].

The masterminds behind these crimes rarely launder money with cash purchases due to the large amounts of money involved. Instead, criminals introduce these resources into financial systems to conceal their illicit origin. The introduction can occur in different ways. Suppose the primary beneficiary of the crime is an individual or natural person with business activity. In that case, the total amount is introduced into the financial system in segmented deposits until the total amount is covered. If the criminal is a legal entity, the company’s financial accounts are usually used to pass off the resources as capital or profits obtained. On other occasions, a shell company is created without capital and actual operation to open temporary financial accounts and make large deposits. When the money comes from unusual illicit activities such as theft and the sale of fuel, the individuals usually launder it through cash purchases because the amounts are low. On other less frequent occasions, these criminals, who are civilians, use intermediaries to introduce resources into the financial system. The intermediary is a friend or family member with a bank account that is used to divert attention from the intellectual actor. An example of the latter activity was observed in Mexico in 2019. Various groups of civilians assaulted and stole gasoline from exposed pipes, and some of them used family members to deposit the resources in banks and divert attention from them [10]. Something similar occurs with the resources coming from activities such as sex trafficking and child trafficking, commonly called human trafficking.

Introducing money into the financial system to launder does not have to be exclusively through bank deposits. The financial system also includes companies that manage investments, public and private pension institutions, insurance companies, and small finance companies. In this way, the incorporation of resources into the financial system also occurs through purchasing an investment fund, contracting a pension plan, or purchasing a car, life, or medical insurance. All these actions aimed at introducing the resource of an illicit activity into the financial system are known as the “Placement” stage of money laundering.

Once the resource is incorporated into the financial system, the next step is the “Layering” stage of money laundering, where the illegal assets are distributed and spread in different ways to obscure their origin and be cleaned. Criminals can distribute the resources by paying loans, buying insurance, contracting pension plans, buying investment funds and transferring them to other internal or external bank accounts. In this phase, the money continues in the financial system, becoming increasingly introduced, hiding its illicit origin through multiple layers of financial transactions. At this stage, institutions have the last chance to detect the money laundering activity and prevent it from leaving the system.

The final stage of the illicit resource in the financial system is “Integration”. At this stage, the resource leaves the financial system to be spent by the primary beneficiary; that is to say, the money already washed/cleaned is used in the formal economy. When the money launderer takes the entire resource from the system to spend on property, cars, jewelry, and donations, the resource is entirely outside the system, and the criminal usually disappears. At this stage, it is more difficult to detect money laundering because, in most cases, the financial relationship with the client is dissipated.

Figure 1 shows the graphical process of money laundering expressed in its three stages.

Figure 1.

Money laundering stages.

The three stages mentioned above describe the money laundering process in the financial system in general terms. But in practice, the dynamic is more complex. Some perpetrators of illicit activities do not use the financial system primarily to launder money. They introduce their resources due to institutions’ security and the ease of transactions with current technologies such as online banking or mobile applications. These criminals, although they know that their assets come from illegal activities, show no interest in wanting to remove them from the system. They are risk lovers and trust that they will not be discovered, maintaining the relationship with the financial institution. On the other hand, the criminals who are aware of laundering activity keep their resources in the financial system for a short time for fear of being discovered. According to Ref. [1], 1–2 years is when launderers maintain their relationship with the institution before withdrawing the resources and disappearing.

3. The age of emerging technologies

The World Economic Forum, held in 2016, marked the official start of the 4th industrial revolution, a term that Klaus Schwab, the executive chairman of this organization, coined [11]. The fourth industrial revolution is characterized by the presence and spread of emerging technologies, that is, any technological development that is relatively new or in the process of improvement [12]. The commonly mentioned technologies are Artificial Intelligence, Blockchain, Biotechnology, Reality and Virtual Reality, Cloud Computing, Biometrics, DevOps, Internet of Things (IoT), Intelligent Apps (I-Apps), Big Data and Analytics, and Robotic Processor Automation (RPA). The most popular and adopted emerging technologies by the industrial community are artificial intelligence, big data and analytics, cloud computing, robotics, and the Internet of Things [13]. A brief explanation of the latter will be provided in the following paragraphs.

Artificial intelligence is a field of study that includes the development of algorithms, systems, and machines capable of replicating the cognitive functioning of the human brain. The main functions that it tries to mimic are learning and problem-solving [14, 15]. A few years ago, it was unimaginable that human skills such as image recognition, voice recognition, text analysis, issuing advice/recommendations, and behavioral research could contribute to improving the efficiency and productivity of the industrial sector. It is currently observed that the financial industry uses different artificial intelligence methods to understand the behavior of its clients and develop and launch proposals for new products and processes to increase income and market share. Specifically, the machine learning models that consist of algorithms/systems capable of learning by themselves from data have boosted the financial sector and put it on board the fourth industrial revolution. Within machine learning, there are two main subfields: supervised and unsupervised machine learning [16]. Supervised machine learning replicates human-guided learning, where trial and error is the great teacher. In financial institutions’ compliance and crime departments, supervised machine learning models such as random forest, XgBoost, and supervised neural networks have contributed to the detection of credit card fraud, cloning, and check fraud. On the other hand, unsupervised learning, which consists of unguided learning based on the observation of facts around the central phenomenon, has positively impacted the detection of crimes such as corruption, money laundering, and shell companies. An example of this can be seen in Ref. [17], where the authors show how the unsupervised machine learning algorithms such as the K-means, the neural gas and the principal component analysis significantly decrease false positive rates in the detection of money laundering and shell company activity.

Big data and analytics arose from the need to manage and extract knowledge from large amounts of data. Previous computing systems could not quickly collect terabytes, petabytes, or even exabytes of data in different formats or structures, much less extract knowledge in real-time. The deficiencies mentioned are corrected with technologies such as Hadoop, Spark, NoSQL databases, machine learning algorithms, and data visualization techniques [18]. It is now possible to see banking institutions capable of collecting millions of daily transactions and making them available almost immediately for analysis. Visualization technologies such as Power BI, Tableau, Looker Studio and Micro Strategy are instantly fed from these transactions and display a variety of graphs that allow insights and foresight to be obtained immediately.

Cloud computing consists of services offered through the network and managed by a provider that controls the infrastructure and software. Examples are email services, storage accounts, applications such as Databricks; and platforms such as Microsoft Azure and Amazon Web Services, which are generally accessible through a web browser. Cloud computing has become the most adopted technology in the industry in the current era [19]. It is a strong ally for many organizations that struggle with the deficiencies of on-premise servers. Although on-site servers have advantages for companies, such as fixed costs and physical closeness to stored data, their disadvantages outweigh them. The main weaknesses identified in on-site servers are: they are expensive to scale, more office space is required, hardware failure/data loss, manual maintenance and high-priced power + cooling consumption. Cloud computing offers services at different levels of management and support. The company decides what percentage of the service is managed by the provider and itself. It also provides options to deploy the service, which can be mainly public, private, or combined. In the public modality, a company shares infrastructure and software in the cloud with other companies, while in the private modality, only the contracting company accesses these elements. An institution that does not want to share sensitive data in a public infrastructure can opt for a private modality but is subject to higher contracting costs. The costs can be reduced by contracting a hybrid modality where non-sensitive data is managed in a public infrastructure and sensitive data in a private one.

Robotics technology found its space in the manufacturing area. Companies that produce physical goods through repetitive processes found robotics the ideal technology to mass and automate production [20]. The term “Robot” does not necessarily refer to a steel machine with a human appearance. For example, it can be a machine incorporated into the car assembly process. It can also be the small cars transporting things from one place to another within a factory [21]. Robotics technology allows us to carry out operations that would be heavy or difficult for humans, maintain the same production rate and work for long hours. Some robots can incorporate artificial intelligence algorithms into their sensors to perform tasks such as image recognition and detect when a machine fails in production or when the temperature rises outside the stipulated levels.

The Internet of Things is the collective network of devices and technology that facilitates connecting devices, users, and the cloud. Internet of Things technology involves smart devices, a user interface and IoT applications deployed in the cloud [22]. For example, when users request an Uber trip, they use the Internet of Things. In this case, the cell phone is the smart device, the Uber application on the cell phone is the user interface and the algorithm to search for the closest available driver is executed in the cloud. The same happens when the lights in the house are turned on and off with a cell phone, autonomous vacuum cleaners are operated, or our car notifies the next mechanical maintenance at the agency. In the industry, the IoT has been implemented through intelligent computer sensors to identify a machine’s malfunction, a water leak, incorrect temperature in food preservation, etc. In the financial sector, a clear example is the mobile applications that allow clients to make transactions instantly by connecting the client’s cell phone and the institution’s servers through the Internet. In summary, the Internet of Things has facilitated the daily lives of human beings and the control of productivity and detection of risks in the industrial sector.

Figure 2 shows the three elements involved in the Internet of Things technology.

Figure 2.

IoT components.

4. Positive impact of emerging technologies on money laundering detection

Undeniably, emerging technologies have improved individuals’ quality of life, changed production methods of goods and services, and created new interactions between individuals, cloud software, and devices/machines [23].

As for financial institutions, it can be seen how they adopt emerging technologies by leaps and bounds. The most common implementation is migrating their digital assets from on-site servers to cloud platforms. This allows them to process millions of daily transactions and extract knowledge through real-time analytics [24].

Compliance and risk departments charged with detecting financial crime have used systems of rules for many years to detect transactions related to money laundering [25]. A rule is a logical construct comprising conditions with certain thresholds that depict a risk profile [26]. For example, there could be a rule that states, “if the transaction amount is greater than 10,000 USD” and “if the individual making the transaction is under 18 years old” then it is classified as a “suspicious transaction” and, in this case, it is sent to deeper investigation. Another rule could state that “If the individual’s economic activity is high risk” or “If the individual is a shareholder in 3 or more companies,” then the transaction is flagged as suspicious. The problem is that these rules are static; in most cases, institutions do not regularly update the thresholds.

On the other hand, the criminal is intelligent and uses specific techniques and methods to launder money; he reads and learns from current regulations. Suppose the law stipulates that all transactions over 15 thousand dollars must be alerted. Then the criminal learns, and if his transaction is 20 thousand dollars, he decides to segment it into four transactions of 5 thousand dollars. The case of shell companies is similar. The legal representatives of these organizations know that regulation in financial institutions is strict and that there are strong national and international initiatives to detect their illegal market of fake invoices [27]. Hence, they decide to disappear in 1–2 years and create new ones to avoid being discovered. This mismatch between a static, unintelligent system and intelligent criminals causes that the rule system generates high rates of false positives and low rates of true positives. The false positives translate into high costs of futile research and the low true positives highlight the inefficiency of the rules system.

Fortunately, the emergence of artificial intelligence provided the financial sector with intelligent algorithms and models capable of mimicking and learning from the behavior of money launderers. Within the field of artificial intelligence, supervised and unsupervised machine learning algorithms have shown their effectiveness in improving the detection accuracy of this crime. In the literature, it is possible to observe detection models such as random forest, support vector machine, and XGBoost within the supervised paradigm and clustering techniques such as K-means from the unsupervised paradigm [2]. Supervised models allow money laundering to be detected when there is a history of confirmed money laundering cases collected by the institution. In such a scenario, the documented instances serve as a guide or supervisor that the institution must relate to transactional attributes to predict a future incident. Supervised models learn from real cases previously detected and establish relationships between the incident and the observed attributes. In this way, if the criminal changes his execution pattern, this modification is caught by the data and algorithm in real time. Unsupervised methods are helpful when no history of confirmed money laundering cases exists. In this scenario, money laundering is treated as a latent variable that is not directly observable but has a set of observable attributes. Most institutions work under these conditions because of the complications of collecting confirmed laundering cases. In this paradigm, unsupervised algorithms learn from the patterns and techniques surrounding criminal activity published by national and international organizations. That is, although the execution of money laundering cannot be observed directly, it is possible to follow surrounding variables such as the transaction amounts, whether the legal representative or natural person is a politician, the economic activity of the client, whether the payments coincide with the salary or declared assets, and the possession of virtual offices. In this way, if international regulators such as FATF and the United Nations publish statistical reports that confirm the relationship of shell company activity with the possession of virtual offices and inconsistency between transaction amounts and declared assets, then an algorithm such as K-means learns from these surrounding patterns and detects future latent crime.

The effectiveness of intelligence methods is enhanced when implemented on cloud platforms such as Microsoft Azure or Amazon Web Services (AWS). These services allow the processing of large amounts of transactions and the instant generation of alerts related to money laundering [28]. They are also equipped with programming languages such as Python, R and Spark and visualization applications such as Power BI that allow the extraction of hindsight, insights, and foresight.

Some intelligent applications allow transaction tracking via geolocation. This functionality enables institutions to observe the radius of action of clients and detect transactions that leave the usual area. From experience, it is observed that non-criminal clients usually carry out transactions via mobile applications either at their registered address or in places close to it. Money launderers often conduct transactions far from their registered addresses to dispel any relationship. Some even carry out transactions from another country because they are in the investigation process related to money laundering in their country of origin.

Text processing and Natural Language Processing algorithms have also contributed positively to the detection activity in the onboarding process. By regulation, financial institutions must comply with the “Know Your Customer” policy when a new customer wants to open an account or contract a product [29]. This policy includes collecting personal information from the client, such as age, average salary, date of birth, economic activity, address, official identification, etc. They also confirm whether the client is on the list of blocked people for having previously committed money laundering or terrorism [30]. Among these lists are the “Sanctions Lists” provided by the Office of Foreign Assets Control and the national lists provided by the Financial Intelligence Units of each country that are incorporated into the internal systems of financial institutions. Thus, when a customer in the onboarding provides her full name and date of birth, the text processing algorithms begin to predict the match of this input data with the information in the lists of blocked people.

5. Impact of emerging technologies on money laundering transformation

Up to this point, the positive impact that emerging technologies have had on detecting money laundering has been mentioned. But another side of the coin needs to be more debated and consists of emerging technologies as enhancers and transformers of money laundering. When a hacker creates a fake story, lies to clients, steals their keys/passwords and obtains their financial resources, he engages in an illegal activity. If the hacker transfers the stolen resources to other accounts to be withdrawn, money laundering arises with its placement, layering, and integration stages. This scenario is an example of cyber money laundering where the illegal activity of key/password theft was carried out digitally, and the stolen resource obtained was layered and laundered through the financial system [31, 32]. The typical victims of this crime are clients who manage mobile applications and are not diligent in taking care of their access credentials.

Identity theft is another illegal activity that is carried out when the criminal impersonates the client’s identity by stealing personal information such as name, address, security questions, fingerprints, etc. This activity is also carried out in mobile applications and Internet banking environments on clients who could be more attentive in taking care of their personal information [33]. The financial resources obtained after identity theft are transferred to other accounts in the financial system, withdrawn, and laundered almost immediately to avoid being discovered.

It is essential to mention that lack of diligence on the part of customers is not the only resource criminals use to commit these illegal cyber activities. Access credentials and identity theft can be carried out by email or telephone. Cybercriminals use techniques such as “spam” and “phishing” sent by email that pretend to come from the banking institution requesting access to data and personal information. Also, by email, the client can receive “Trojan” viruses that, when executed, display websites pretending to be the bank’s portal [34]. The telephone technique is one of the most used by criminals and cybercriminals who impersonate bank staff. Once the customer falls for the lie, criminals create false stories about an unacknowledged charge, authorized credit with lower-than-expected rates, and have the customer log into their mobile app or online banking to provide their credentials, access, or personal data. The call ends with the promise that the unacknowledged charge will be resolved in the next few minutes or that the credit will be deposited. But in reality, the following minutes are necessary for the criminal to enter the client’s accounts, transfer the resources to their personal or third-party accounts, and withdraw them immediately (laundering them) to cover up the fraud.

Check fraud is another cybercrime that takes advantage of mobile applications. To provide better service to their customers, banks enable the option of remote scanning of checks for subsequent deposits without having to go to the branch. The problem is that the Expedited Funds Availability Act forces institutions to make the resources available in the account the next day. However, it takes about a week for the institution to confirm whether the scanned check is legitimate or fake. If a fake check is deposited, the criminal will withdraw resources from the system long before the institution proves its falsity [35].

Blockchain technology within the management of cryptocurrencies offers the anonymity necessary to obscure the illegal origin of resources. Thus, criminals can exploit the cryptocurrency market to commit money laundering. They can use resources from illicit activities to purchase cryptocurrencies, hiding transaction information from national and international regulators. They can also commit illegal activity in the digital environment, such as stealing cryptocurrencies and making online exchanges to launder the resource [36].

All the acts mentioned above are illegal activities carried out in the digital environment and, by definition, are cybercrimes [37]. When these acts are accompanied by placement, layering, and integration within the financial system using emerging technologies, cyber money laundering takes place. Unlike everyday money laundering activities such as drug trafficking and arms trafficking, cybercrimes do not require the presence and physical effort of the criminal. This allows cybercriminals to simultaneously carry out several illegal actions to launder the money later using the same financial system from which it was stolen.

Technologies such as Internet banking, mobile applications, and email services that are part of the Internet of Things (IoT) have facilitated cybercrime and cyber money laundering. Also, blockchain technology has contributed to the increase in cyber money laundering incidents. Another emerging technology of the 4th industrial revolution, cybersecurity, has arisen to combat these adverse effects. According to [38], cybersecurity sets the minimum standards, policies, and norms that an organization must follow to combat and minimize the risk of cybercrime. Measures such as blocking the online portal after three attempts due to a forgotten password, implementing the mobile token, facial recognition, and access confirmations through numerical codes are examples of cybersecurity actions focused on avoiding cybercrime and cyber money laundering. Artificial intelligence can also be implicit in cybersecurity. For example, intelligent algorithms are implemented to detect whether a check is legitimate, comparing it with a database of previously cashed checks. The algorithm analyzes data in the scanned image of the check, such as name, check number, account number, address, and other data to compare. There are also artificial intelligence algorithms that are fed with information regarding the individual’s interaction with the devices, such as the number of clicks the customer made on the machine, the number of times they entered the online portal, the number of times they generated a token mobile, time and date of operation and more attributes to detect the occurrence of a cyber-attack. Although these cyber security measures are applied mainly in banks, they can also be observed in insurers, pension, and investment institutions.

6. Conclusions

This chapter showed a general overview of the transformation of money laundering in the face of emerging technologies. The crime of money laundering is of utmost importance for individuals, institutions, and governments due to the numerous adverse effects it entails. Although money laundering can be carried out in different scenarios, the financial sector is the favorite scenario and most exploited due to its vulnerabilities.

Even though cloud computing, artificial intelligence, and the internet have provided tools for preventing and detecting money laundering, they have also contributed to its transformation. Financial institutions must be aware that not only the known crimes of corruption, bribery, and arms/drugs/people trafficking generate resources to be laundered, but cybercrimes also enter the agenda of criminals, thus generating cyber money laundering. Likewise, money is not only introduced into the financial system as legitimate through traditional cash deposits but also through the purchase of insurance, contracting of private pension plans, purchases of investment funds via mobile applications, payment of online credits, use of prepaid cards, and electronic commerce.

Emerging technologies must be implemented with responsibility and awareness of the positive and negative impacts they imply. Companies’ data science, data engineering, and cybersecurity departments must work as a team to prevent the crime of money laundering from growing and evolving.

Conflict of interest

The authors declare no conflict of interest.