Money laundering is the process of laundering proceeds earned through criminal activities into clean money that appears to come from a legitimate source. In other words, it places illegally sourced funds into the standard financial cycle or money circulation process by disguising them as clean money (Ardizzi et al., 2014; Sobh, 2020). Money laundering involves processing funds from underground activities like terrorism, cybercrime, drug trafficking, corruption, tax evasion, and quasi-legal activities such as concealment of income from public authorities (Habib et al., 2018; Karim et al., 2020; Tiwari et al., 2020). Converting illegitimate gain into legitimate income disrupts the legal process of money supply and corrupts financial institutions, which in turn benefits criminal networks (Ardizzi et al., 2014). Moreover, according to Hendriyetty and Grewal (2017), money laundering leads to an increase in shadow economic and criminal activities while reducing the tax collections required for the growth of a country (as cited in Tiwari et al., 2020). Likewise, Drayton (2002) and Dowers and Palmreuther (2003) stated that money laundering could stymie a nation's economic growth, lead to financial distortion, socioeconomic and monetary instability, higher corruption, and increased vulnerability to financial institutions (as cited in Habib et al., 2018; Loayza et al., 2019). This claim can be exemplified by the research done on 91 Italian states in which the total identified laundered cash from 2005 to 2008 was equivalent to 7% of Italy's GDP, of which three-fourths of the money was sourced from illegal trafficking activities. The remaining one-fourth was acquired through extortion (Ardizzi et al., 2014). Likewise, Loayza et al. (2019) highlighted in their paper that Colombia saw a phase in 2001 and 2002 when the total value of illicit income was equal to 12% of its GDP and the volume of laundered assets increased from 8–14% of Colombia's total GDP. As a result, illegal activities like as tax evasion, corruption, extortion, and drug trafficking result in income loss for the government, internal market instability, erosion of private-sector efforts, volatile currency and interest rates, and political upheaval (Ofoeda et al., 2020; Sobh, 2020).
Money laundering is primarily a three-step process that involves placement, layering, and integration stages (Sobh, 2020; Tiwari et al., 2020). The first step, known as placement, involves the introduction of illegal funds into the financial system; the second stage, known as layering, involves a series of fictitious transactions that mask the true source of the cash (Ardizzi et al., 2012; Ofoeda et al., 2020). In the third integration stage, illicit money is converted into a legitimate source of revenue by investing it in real estate, stocks, or businesses (Ardizzi et al., 2012). According to Loayza et al. (2019), an illicit process can be categorized into two types of activities: first, the production of illegal goods (such as drugs) that have value in the illicit market; and second, activities like kidnapping, extortion, robbery, and fraud that redistribute wealth among the various classes of people (from rich to poor) but do not contribute to the economy. The illegal money generated through these two types of illicit activities can be laundered through "front companies," gold dealers, currency exchange houses, insurance companies, shell companies, wire systems, offshore banking, automobile dealerships, casinos, lawyers, and accountants," among others (McDowell and Novis, 2001, as cited in Ofoeda et al., 2020, p. 4).
Determinants of Money Laundering
To manage the illegal activities of an area, it is necessary to comprehend the elements that contribute to or determine the likelihood of money laundering. According to Karim et al. (2020), the four variables of the fraud diamond theory—rationalization, pressure, capacity, and opportunity—drive the illegal behaviours engaged in the money-laundering process. Following the fraud diamond theory, high living standards, greed for power and money, bad habits or financial need, loopholes in the current system, insecure e-money facilities, loose control of access to information, poor supervision, wealth distribution, and urgency, as well as a propensity to commit fraud and use specific skills to carry out laundering activities, represent the pressure, opportunities, rationalization, and capability legs of the diamond theory, respectively (Lokanan, 2019).
To identify and prevent money-laundering operations, it is vital to understand the other characteristics that should be considered when developing AML legislation and risk-mitigating AI algorithms. In this context, Reganati and Oliva (2018) have shown that the factors determining illegal behaviour might differ by geography. In their paper, Reganati and Oliva (2018) demonstrated, for instance, that a region's education and corruption level influenced the mafia crime rate and money-laundering activities in northern and central Italy, whereas gambling and gaming habits heavily contributed to the presence of money-laundering activities in the southern region of Italy. Similarly, Amara and Khlif (2018) found that the rate of financial crime is strongly correlated with tax evasion and corruption as a nation's primary driver of financial crimes.
In addition, Ferwerda (2009) revolutionized the research on money laundering by demonstrating that "a) the probability of being caught for money laundering, b) the sentence for money laundering, c) the probability of being convicted for the predicate crime, and d) the transaction costs of money laundering are negatively related to the amount of crime" (p. 1) and that constructing laws and policies based on these factors will aid in reducing crime. In addition to the four factors of the diamond theory, the issues of corruption, education, organizational culture, working environment, money lust, the strictness of laws, the strength of the adopted audit standards, and the gender, age, source of funds, and number of bank accounts held by account holders are the major determinants of money laundering that determine the likelihood of the presence of illegal activities.
Smart Analytics for Money Laundering Detection
This section will address the role of technology in combating money laundering challenges. Even though several academics have presented numerous anomaly-detection and money laundering risk (MLR) mitigation models, it stands to reason that the intended outcomes would be achieved if technological or software solutions were integrated with those models (Lokanan, 2019). Similarly, various researchers and practitioners have favoured technology to enhance the efficacy of anomaly detection and risk-mitigation models (Kansal, 2021; Singh and Best, 2019). These models need intelligent analytics technologies to identify suspicious activity via pattern recognition. Analytical methods such as link analysis and interactive data visualization have proven critical in identifying anomalous patterns and transforming them into visual representations for further human examination (Dilla and Raschke, 2015; Singh and Best, 2019).
More specifically, innovative analytics tools can be used to help detect and prevent money laundering. By analyzing transaction data, these tools can flag patterns that may indicate criminal activity (Ferwerda, 2009). These transactions include but are not limited to money laundering using cash transactions, electronic transfers and payments, bank accounts, investment-related transactions, offshore activities, secure and unsecured lending, and laundering involving intermediaries. The information detected from these transactions can then be used by authorized personnel to form the basis of an investigation. In some cases, smart analytics tools may even provide real-time alerts to authorized persons when suspicious transactions are detected and allow for quick and effective action to be taken, potentially preventing large sums of money from being laundered (Singh and Best, 2019). In conclusion, smart analytics tools can be valuable in the fight against money laundering.
However, analytics is not particularly useful if data, such as financial transactions, are created rapidly and in a large volume since analysts would find it difficult to make quick and accurate choices when dealing with such a dynamic quantity of data (Ferwerda, 2009; Singh and Best, 2019). Moreover, money launderers regularly use system vulnerabilities and current laws to launder dirty money; hence, the linear and pattern-based analysis will be unsuccessful unless the system learns from its previous patterns and creates a new algorithm each time something new is observed (Sobh, 2020).
Another issue to consider is the cost of implementation. The overall cost of implementing a money laundering detection algorithm in real-time applications depends on several factors, including the type of algorithm used, the implementation's complexity, and the deployment's scale (Ferwerda, 2009). For example, a simple rule-based algorithm could be implemented for a few hundred dollars, while a more complex machine learning-based algorithm could cost several thousand dollars. The cost also scales with the deployment size, so a large bank with millions of customers would incur much higher costs than a small financial institution (Dreewski et al., 2015). In general, however, the cost of implementing anti-money laundering measures is typically a small fraction of the overall budget for compliance and risk management (Dilla and Raschke, 2015).
Machine Learning and Artificial Intelligence for Money Laundering Detection
Despite these challenges, ML algorithms are popular and in demand because they can change as they read new data or patterns (Kansal, 2021; Lokanan and Sharma, 2022; Zhang and Trubey, 2019). For instance, Semmelbeck et al. (2019) used a random forest (RF) classification algorithm to identify the factors that could be important to detect whether a terrorist group is engaged in criminal activities or not and found that the temporal change in the organizational structure of the terrorist group is a red flag for money-laundering activities. Generally, ML algorithms used for detecting money-laundering activities can be of two types: supervised and unsupervised ML algorithms (Chen et al., 2018; Lopez-Rojas and Axelsson, 2012). Badal-Valero et al. (2018) proposed integrating Benford's rule with ML techniques such as logistic regression (LR), decision trees (DT), neural networks (NN), and random forests (RF) and employing any applicable approach depending on whether the data was balanced or imbalanced.
They found that Benford’s law in association with NN works best on unbalanced data, whereas RF and LR perform best with Benford’s law when balancing methods are applied or when Synthetic Minority Oversampling Technique (SMOTE) is applied to the data. Zhang and Trubey (2019) have compared the five supervised ML algorithms - DT, RF, support vector machine (SVM), artificial neural network (ANN), and Bayes Logistic Regression (BLR) against the standard Maximum Likelihood Logistic Regression (MLLR) and found that ANN performs best as a rare event classification algorithm. In contrast, SVM and RF can also generate comparable results when amalgamated with sampling methods. However, the challenge with supervised techniques is that the data must be devoid of biases and mistakes, the events in the historical data must be precisely characterized, and each input variable must be precisely recognized; otherwise, suboptimal results will be generated by the models (Zhang and Trubey, 2019).
Further, these suboptimal results can be avoided by using the XGBoost ML algorithm. Jullum et al. (2020) demonstrate through their paper that XGBoost is useful in fighting suboptimal results as it considers nonreported alerts, normal alerts, and flagged alerts equally to develop a detection algorithm that predicts the probability of money laundering based on the senders’ or receivers’ background information, as well as their previous actions and transaction history. Other than the use of boosted algorithms, it is recommended to use unsupervised ML algorithms to overcome this drawback of supervised algorithms. According to Salehi et al. (2017), unsupervised data techniques are more helpful in identifying money-laundering patterns and can be instrumental in improving the learning capacity of classification methods. For example, Chao et al. (2019) used data-mining methods to monitor abnormal behaviours in trade-based money-laundering activities. Improvements were observed in management efficiency, which will be beneficial to restraining cross-border capital flow and arbitrage for emerging markets and developing economies. Another way to overcome the drawbacks of supervised learning is to combine visuals with the deep learning ML algorithm called graph learning or clustering algorithm (Dreewski et al., 2012; Weber et al., 2018). Indeed, Li et al. (2020) proposed using FlowScope—a multipartite graph and scalable algorithm—to plot the complete flow of monetary transaction money from source to destination. According to Li et al. (2020), FlowScope can outperform the state-of-the-art baselines in identifying the fraudulent accounts used in the synthetic and real-world datasets.
Electiveness of AI and Other Methods in Money Laundering Detection
Models based on ML and AI are increasingly being used in a variety of financial crime applications, including money laundering prediction. While rule-based methods have long been the standard for detecting and preventing money laundering, AI models offer a more sophisticated approach considering various factors. Studies have shown that ML and AI models can outperform rule-based methods in several ways, including accuracy, speed, and scalability (see Ba and Huynh, 2018; Jullum et al., 2020; Singh and Best, 2019). Money laundering models based on ML and AI are said to have higher predictive accuracy than the traditional rule-based approach to detection (Chen et al., 2018; Jullum et al., 2020). Other studies showed that ML and AI models could handle more volumes of data than traditional rule-based systems and could do so in a fraction of the time (Lokanan, 2019; Salehi et al., 2017; Sarker, 2022). Finally, AI models are more effective than rule-based methods at detecting previously unknown money laundering schemes (Singh and Best, 2019; Zhang and Trubey, 2019).
Even though AI is still in its infancy regarding AML compliance, several financial institutions are already adopting it for transaction monitoring. However, AI is not the only method currently used to detect suspicious transactions (Singh and Best, 2019; Sobh, 2020). Banks have long relied on rules-based systems to flag suspicious transactions, and these systems are continuously being refined and updated (Mathuva et al., 2020). In addition, banks are also increasingly using behaviour-based prediction models that focus on identifying anomalous patterns of behaviour. Money laundering detection models based on ML and AI are trained on historical data to look for red flags signs of money laundering, such as sudden changes in account activity or large transfers to high-risk jurisdictions. By combining different prediction methods, banks can create a more comprehensive approach to detecting and preventing money laundering.
Although a lot has been done using ML algorithms in the field of fraud detection and credit default, there is a dearth of scholarship on the application of ML and AI algorithms to detect laundering activities (Jullum et al., 2020; Lokanan and Sharma, 2022). The lack of scholarship can be attributed to the complexity of money-laundering events and the unavailability of quantitative data (Chen et al., 2018; Tiwari et al., 2020; Zhang and Trubey, 2019). As noted by Canhoto (2020):
[D]ue to the unavailability of high-quality, large training datasets regarding money laundering methods, there is limited scope for using supervised machine learning. Conversely, it is possible to use reinforced machine learning and, to an extent, unsupervised learning, although only to model unusual financial behaviour, not actual money laundering. (p. 1).
Lopez-Rojas and Axelsson (2012) believe that synthetic data, in the absence of real data, can be used to stimulate the required dataset for ML algorithms; however, the downside of this approach is that a biased dataset can be generated, depending on how it has been simulated. That said, synthetic dataset does provide an avenue to build and train algorithms to detect money-laundering activities. They advise using synthetic data for experimentation and Multi-Agent Based Simulation (MABS) until alternative mechanisms for developing more realistic user datasets become accessible. The present paper attempts to fill this gap by using a simulated dataset of banking data from Middle Eastern banks.
H1
Ceteris paribus, the likelihood of money-laundering events is an increasing function that depends on gatekeepers and the timing of the transaction.