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Predicting Money Laundering Using Machine Learning and Artificial Neural

Networks Algorithms in Banks

Article  in  Journal of Applied Security Research · August 2022

DOI: 10.1080/19361610.2022.2114744

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Title: Predicting Money Laundering using Machine Learning and Artificial Neural Networks
Algorithms in Banks

Author: Mark Lokanan

Royal Roads University

Abstract: This paper aims to build a machine learning and a neural network model to detect the
probability of money laundering in banks. The paper's data came from a simulation of
actual transactions flagged for money laundering in Middle Eastern banks. The main
findings highlight that criminal networks mainly use the integration stage to integrate
money into the financial system. Fraudsters prefer to launder funds in the early hours,
morning followed by the business day's afternoon time intervals. Additionally, the Naïve
Bayes and Random Forest classifiers were identified as the two best-performing
models to predict bank money laundering transactions.

Keywords: Machine learning; Artificial intelligence; Anti-money laundering; Banks

Introduction

As governments around the world have increased their scrutiny of the gaming and real

estate sectors, there has been a shift in money-laundering activities in the financial industry. An

International Consortium of Investigative Journalists investigation identified that more than US

$2 trillion in transactions were flagged by financial institutions between 1999 and 2017 (Yang et

al., 2020). In many of these instances, criminal networks were probing and detecting weaknesses

in the control and anti-money-laundering (AML) frameworks to launder illicit funds and

integrate them into ostensibly legitimate assets (Hutton, 2020). The end goal is the final

integration of the funds into legitimate business activities or investments. To address these

concerns, there have been calls to employ artificial intelligence (AI) to build learning algorithms

that detect money-laundering transactions. Regulatory technology in the form of computational

1
intelligence will be transformative in that AI techniques can help reduce false positives or type I

errors. The present paper attempts to examine the following questions:

1. How does the probability of money laundering vary by personnel and type of activities?
2. Which features are the strongest predictors of money laundering?

Contribution to Practice

In the past decade, banks have dramatically shifted the way they operate. The traditional

model of banking, which places reliance on human expertise to detect instances of money

laundering, is unable to keep up with the rapid pace of change taking place in the world of

finance. Banks must adopt cutting-edge technologies like machine learning (ML) and artificial

neural networks (ANN) to stay competitive and detect financial crimes. ML is a form of AI that

allows computers to learn from data, identify patterns, and make predictions. Banks are already

using this technology to detect fraud, assess risk, personalize customer service, and process and

interpret large amounts of data. ML technology is particularly well-suited for credit scoring and

money laundering detection tasks. In the ever-evolving world of finance, financial institutions

that use ML and ANN will be able to stay ahead of criminal networks and prevent launders from

infiltrating their systems.

As finance becomes more digitized, so too do criminals' methods to launder money. In

the past, banks could relatively quickly identify suspicious activity by looking for patterns in

cheque deposits and withdrawals. However, because of the proliferation of internet banking and

other forms of digital payment, it is now far more challenging to monitor unlawful financial

activities. Banks are fighting back using ML and AI to detect and block suspicious transactions

automatically. By analyzing large datasets, ML and AI can help banks identify trends that may

indicate money laundering. These methods, when combined with business intelligence, have the

potential to provide a potent instrument in the fight against money laundering. Computational

2
technology may help with customer due diligence by identifying account holders and signatures,

account numbers, the name of the bank, and the signature on the account.

The current article aims to predict money-laundering activities in the banking industry

using supervised ML classification techniques and a feedforward neural network model. The rest

of this paper is structured according to the following format: Section two provides a critical

overview of AML and the computational intelligence literature. Section three discusses the

methodology and research design. Section four discusses the data cleaning and processing

process. The algorithms used in this paper are also discussed. Section five analyzes and discusses

the findings. Section six provides a conclusion and highlights areas for future research.

The Literature on AML and Computational Intelligence

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

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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% to 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

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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

5
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

6
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

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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

8
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)

9
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

10
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.

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H1: Ceteris paribus, the likelihood of money-laundering events is an increasing function that
depends on gatekeepers and the timing of the transaction.

Research Methodology

Data Collection

Data for this project came from a simulation of money-laundering activities in Middle

Eastern banks based on a real dataset. The data were simulated using similar features and data

points that mirror actual transfers of the original dataset. The features used for the simulation

were similar to the processes used in actual transactions. Both the production of money

laundering and non-money laundering were considered. Attempts were made to simulate all

aspects of money laundering and non-money laundering transactions and to provide a relatively

complete simulator. The simulation is based on financial institutions' three processes of money-

laundering techniques: placement, layering, and integration. In simulating each process, a rule

was created to represent cash-in transfers and one for transferred-out funds. An important feature

of the stimulated dataset is that it is flexible and produces a dataset with different parameters.

Feature Variables

Type of Transaction

The type of transaction is classified as either cash-in or transfer-out. The type of transaction was

coded as a categorical variable and then transformed into dummy variables.

Level of Crime

The level of crime refers to whether the money-laundering activities were committed by the

head of the financial institution or by a colleague working in the same institution. The level of

crime was coded as a categorical variable and then transformed into dummy variables.

Amount of Money

12
The amount of money is a random continuous variable and represents the actual amount of

funds processed through the transaction.

Date

Date is simply the day, month, and year of the transaction. The date was further reformatted

using the date and time format in Python as categorical variables representing the days of the

week and months of the year.

Time

The time of the transaction is a continuous measure rounded to the nearest hour.

Type of Money Laundering

The type of money laundering was coded to represent the three stages of the laundering process:

placement, layering, or integration. Each of these variables was later transformed into a

categorical variable.

Target Variable

Money Laundering

The target variable was whether money laundering occurred or not. The target variable was

coded to represent 1 when the transaction was classified as money laundering and 0 when it was

classified as non-money laundering. The formula to represent the target variable is shown in

equation 1:

𝑦 = {1, money laundering

0, no− money laundering}

Statistical Tool and Performance Metrics

Coding and analysis were conducted using the Python programming language in a

Jupyter notebook. Scikit-learn was the library of choice. Scikit-learn is a popular ML library

13
used to build and analyze ML algorithms. The Keras open-source software library was used to

perform the ANN analysis in the Jupyter notebook. The ML algorithm and ANN model were

evaluated using the accuracy scores. As this is a classification model, the confusion matrix was

employed to identify false positives or type 1 error. The algorithm with the highest predictive

accuracy was selected as the best classifier. A confusion matrix was used to describe the

performance of the best classification algorithm on the test set (or unseen data). A classification

report with the following performance metrics was also employed to evaluate whether the

model using precision, recall, and the F-1 score was also used to assess whether the model was

capturing the money-laundering category and not only the non-money-laundering classification.

The Receiver Operating Characteristic (ROC) evaluation metric was used to plot the trade-off of

the false positive (x-axis) against the true positive (y-axis). The Area Under the Curve (AUC)

performance metric distinguishes between the money-laundering and non-money-laundering

classifications. A completed random model will produce an AUC of 0.5, and a perfect model

will have an AUC of 1. In this regard, the higher the AUC, the better the model distinguishes

between the positive class and the negative class.

Data Preprocessing

In the data preprocessing stage, the dataset was checked for missing values. The level of

crime feature is missing in 38% of the observations. As this is a categorical variable, the mode

was used to impute the missing observations. All duplicate values were deleted from the dataset.

The specific and unique values were identified for all the features. In cases where Not a Number

(nan) values were identified, they were replaced with zero. Some features, such as source and

destination ID, were dropped from the dataset. There was no way to determine the location (i.e.,

latitude and longitude) from the data.

14
Feature Engineering

In the data preprocessing stage, the columns for some of the variables were changed to

make them more readable. For example, "isfraud" was changed to "moneylaundering" to reflect

whether a money laundering transaction occurred or not. The same was done for another type of

money laundering. The original data was coded from "type1," "type2," and "type3" to reflect

categorical features, namely, "type1" = placement, "type2" = layering, and "type3" =

integration. Feature selection was also used to identify the top five features. As this was a large

dataset comprising 2,340 observations, the data was divided into a 60/40 train/test split. A

standard scalar technique was used to normalize the numeric features. The model will not be

able to analyze dates and times as raw data. As such, the date and time data were first converted

into categorical variables for Exploratory Data Analysis (EDA) and then converted back to

numeric data for the ML and ANN algorithms to analyze. I further transformed the time into

three categorical variables reflecting the morning, afternoon, and evening. There were only

eight feature variables. Even though Spearman's rank correlation coefficient independent check

for attributes listed the top five features, all 14 features were employed to build the final model.

Machine Learning Algorithms Employed

Naive Bayes Algorithm

The Naive Bayes (NB) theorem is one of the classifiers employed to predict money-laundering

activities in financial institutions. Recall from Lokanan and Liu (2021) that the NB theorem is

based on the probability that the output in class C given that X = x can be estimated by P(y|x)

from P(y), P(x), and P(x|y) and is represented by the following equation:

15
 P(y|x) = P(x|y) _ P(y)/P(x)

Where:

• P(y|x) is the posterior probability of the target variable (y) given predictor variables

(x, features).

• P(y) is the prior probability of class x

• P(x|y) is the likelihood which is the probability of predictor given class x

• P(x) is the prior probability of predictor

The NB classifier works well for big datasets that include a large number of features and

operates on the assumption that the input features are independent of one another for

multinomial distributed data. To build the algorithm for this model, GaussianNB was use along

with n_sample and n_features as the parameters.

Logistic Regression Algorithm

Logistic regression is one of the simplest and most established algorithms in ML

classification models. Despite its name, logistic regression is a linear model use for

classification when the target variable is binary or consists of multinomial indicators. Logistic

regression uses the sigmoid function. The sigmoid function maps the real value to a value

bounded between 0 and 1 (hence the logistic regression is used for classification models). The

parameters employed for the logistic regression algorithm is solver = liblinear. The basic

assumption with logistic regression is that of the linear function 𝛽0+𝛽1𝑋, which is transferred

using the sigmoid function 𝑆(𝑡); then, no matter what values 𝛽0, 𝛽1, 𝛽2 . . . and 𝛽k X take, 𝑦

(target variable) will always have values of 0 and 1 (e.g., fraud and nonfraud; money laundering

or no money laundering, spam or no-spam). Logistic regression models use this equation to

estimate the probability that 𝑦 = 1 given its size 𝑋 as follows:

16
 𝑃𝑟(𝑌=1|𝑋=𝑥) =𝑒𝛽0+𝛽1𝑋1/ 1+𝑒𝛽0+𝛽1𝑋

Random Forest Algorithm

Random forest (RF) is an ensemble classification method that is useful because it adds

additional randomness to the data. Unlike the NB algorithm, RF trains many strong decision

trees and combines their predictions through a bagging process. The RF model was trained on

the following parameters: criterion = "entropy," n_estimators = 100, and random_state = 123. A

diagraphic illustration of the RF model is shown in Figure 1 below. As you can see from Figure

1, there are two sets: a training set labelled in blue and a test set (unseen data) labelled in green.

After training the RF model on the training set (blue circles), the model is then evaluated on the

test set (green circles). The scores from the trees of the test set (in this case, two) are then

averaged to form the RF score for the classification model.

When using the Gini index to determine the branching off of nodes in the decision trees, the

mathematical formula for the RF algorithm for classification data is represented by the following

equation:

𝐺𝑖𝑛𝑖 = ∑𝑛𝑖−1(𝑝𝑛 )2

Where:
• Pn represents the relative frequency of the binary class, and ,
• n represent the number of classes

17
 Tree 1 Tree 2 Tree 3

Figure 1: Random Forest Algorithm

CatBoast Algorithm

CatBoast is an open-source gradient boosting library developed by Yandex (a Russian-

based search engine) and is easy to use. CatBoast is very useful for datasets where a large

number of the features are categorical variables. The CatBoast algorithm is based on gradient

boosting and ML; it works great when data comes from different sources. Hence, it is useful for

this dataset since the data was a concatenation of two sources of data: one that involves the

transition amount, type of laundering, and date and time, and the other that contains the people

(i.e., head or colleague) who were involved in the laundered activities. The parameters of the

CatBoast algorithm are iterations = 50, depth = 3, learning_rate = 0.1.

GridSearchCV

Based on the performance metric used to evaluate the model, the single algorithm

outlined above will project the performance of the parameters that come with those algorithms.

For these parameters, the only choice is to try all the possible values and then choose the best

one. To further fine-tune the model and enhance the performance metrics, GridSearchCV ("grid

18
search") is used. The grid search approach generates the best candidate from a specified list of

parameters. There are two types of grid search: exhaustive and randomized. The exhaustive grid

search approach optimizes the parameters to be included in the model, whereas the randomized

grid search approach automatically selects the best parameters for the model. In this project, an

exhaustive grid search was employed to select the best parameters for the model. The exhaustive

grid search approach was chosen because all the possible parameters are evaluated, and the best

possible parameters are retained.

Artificial Neural Networks

The ANN method is a generalized model that processes many layers of data to make a

decision. As can be seen in Figure 2 below, ANN is a multilayered layer perceptron (MLP)

approach, where the input features are given values or weights. The MLP method is a deep

learning neural network approach composed of several perceptrons. As can be seen in Figure 2,

the MLP method consists of three layers: the input layer, which receives the signals from the

modes; the output layer, which calculates the weighted average of the single features; and the

output layer, which receives the weighted sum from the output layer to make a decision. In

classification problems, the decision will be based on the percentage or accuracy of the model to

predict the outcome. The input layers are typically the feature variables of the model. The input

layers pick up the signals (coefficients) and pass them on to the hidden layer, where the weighted

average for each feature is calculated and passed on to the output layer, which delivers the

results. ANN is like a black box. It is not supposed to be interpretable in terms of feature

importance; rather, it is a useful algorithm for predictive models and can be analyzed using the

same performance metrics as classical ML models.

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 X0

W H0
1
X1 Y1
W2
H1

X2
W3 Y0

H2
W4
X3

Input Layer Hidden Layer Output Layer

Figure 2: Multi-layered Perceptron Neural Network

Analysis of Findings

Univariate Analysis

Table 1 presents the descriptive statistics of the numerical features. It is important to note

that the maximum amount of funds laundered in a single transaction was $7.95 million. There is

also no significant difference between the average amount of funds laundered ($2.51 million)

and the standard deviation ($2.34 million).

Table 1: Descriptive Statistics of Numerical Features

Amount of Money
Money Laundering
Laundered
Count 2.34E+03 2340
Mean 2.51E+06 0.597863
Std 2.56E+06 0.490434
Min 1.33E+04 0
25% 3.36E+05 0
50% 1.16E+06 1
75% 4.69E+06 1
Max 7.95E+06 1

Table 2 shows the descriptive statistics of the categorical features. As shown in Table 2,

there are five categorical features. This type of laundering represents the stages of money
20
laundering: placement (type 1), layering (type 2), and integration (type 3). A closer look at Table

2 indicates that integration (type 3) was the top method used to launder funds. Note also that

there are more transfer-out than transfer-in transactions. Interestingly, more colleagues (or

employees) are involved in money laundering transactions than managers.

Table 2: Descriptive Statistics of Numerical Features

Type of
Type of Action Level of Crime Date Time
Laundering
Count 2340 1449 2340 2340 2340
Unique 2 2 151 475 3
Top transfer colleague 6/6/2019 15:30 type3
Freq 1580 919 26 12 1452

Figure 3 shows the most important features for predicting money-laundering activities in

financial institutions. As seen in Figure 2, the hour of the day has the highest positive impact in

predicting money-laundering transactions, followed by cash-in and transfer-out. These findings

make sense because the amount of funds entering and leaving the system is more likely to be

flagged by compliance officers if they are over the allotted amount or if there is any anomaly

with the transaction. Surprisingly enough, the amount of money does not seem to be an

important feature in predicting money laundering in financial institutions.

21
 0.3500
Importance 0.3000
0.2500
0.2000
0.1500
0.1000
0.0500
0.0000

Features

Figure 3: Feature Importance

Continuing from Figure 3, the timing of the laundering activities is significant. As can be

seen in Figure 4, launderers are more likely to launder funds through on-site transactions and

during business hours. Most of the laundered funds occurred in the morning hours, followed by

the afternoon time intervals. Not surprisingly, laundering activities do not occur during the nights

and evenings because these are outside business hours. According to these results, there is a clear

need for a heightened focus on monitoring transactions in the early morning hours of work days.

22
Figure 4: Transaction time

Bivariate Analysis

Figure 5 presents the correlation matrix of the numerical features with money-laundering

activities. There is a moderately positive correlation (0.57) between the head of the bank and

money-laundering transactions. Conversely, there is an inverse or moderately weak relationship

(-0.57) between colleagues and money-laundering transitions. These findings indicate that the

more funds laundered by colleagues, the less likely the money-laundering transactions will be

successful. Note also from Figure 1 that the placement stage of the laundering process is

positively correlated (0.67) with money laundering. Funds that are transferred out have a

negative (-1) correlation with money laundering, which indicates that once the transactions

(cash-in) are placed in the financial system, they are integrated and become part of the criminal

network’s portfolio of assets.

23
Figure 5: Correlation matrix

Evaluating Model Performance

Evaluating classification models can be complex because of the different possible

performance metrics to consider. The present study is based on a binary classification model to

predict money-laundering transactions and is denoted as 1 (for money laundering) and 0 (for no

24
money laundering). The classification of a particular observation can fall within one of several

different outcomes, as shown below:

• Accuracy: {TP + TN}/{TP+ FP + FN + TN}

• Precision: {TP}/{TP + FP}
• Recall: {TP}/{TP + FN}
• F1 Score: 2 * Precision * Recall}/{Precision + Recall}
Where
TP= True Positive
TN = True Negative
FP = False Negative
FN = False Negative

Accuracy is simply the number of correctly classified observations (TP + TN) divided by the

total number of observations. Precision is how precise or accurate the model predicts the true

class (in this case, money laundering). Recall, or sensitivity is the positive rate of the true class

that has been correctively classified. The F1- measure is simply the harmonious mean of recall

and precision and might be a better measure if there is a need to balance the scores between

precision and recall in an imbalanced dataset (See Lokanan and Sharma, 2022; Kansal, 2021).

Table 3: Accuracy Score of Algorithms

Rank Algorithm Accuracy Score

1 Naive Bayes 77.46
2 Random Forest 77.46
3 Logistic Regression 77.24
4 CatBoost 76.71
5 GridSearch 76.71

Table 3 presents the accuracy score of the algorithms. As noted in this study, there was

not much difference in the respective scores. Quite notably, the NB and RF classifiers were the

two best-performing models, both with 77.46% accuracy. Significantly enough, grid search,

which involved hyperparameter tuning, did not improve the accuracy score of the model. Given

25
that the NB and RF had the highest accuracy rates, it is logical to look further into their

classification scores. As seen in Table 4 below, when compared to the RF model (.87), the NB

(1.00) classification did an excellent job of not labelling an observation as money laundering that

was not money laundering. On the other hand, RF did a better job of capturing more money

laundering observations (0.72 versus 0.63).

Table 4: Classification Report

Naive Bayes Classification Report

Classification Precision Recall F1-Score Support

0 0.64 1.00 0.78 369
1 1.00 0.63 0.77 567
Accuracy 0.77 936
macro avg 0.82 0.81 0.77 936
weighted avg 0.86 0.77 0.77 936

Random Forest Classification Report

F1-
Classification Precision Recall Score Support
0 0.66 0.83 0.74 369
1 0.87 0.72 0.79 567
Accuracy 0.77 936
macro avg 0.77 0.78 0.76 936
weighted avg 0.79 0.77 0.77 936

It is essential to have a look at the confusion matrix of the NB and RF classifiers in order

to get further knowledge. Figure 6, below, shows a side-by-side comparison of the NB and RF

confusion matrix. There are two possible outcomes from the predicted class: money laundering

and no money laundering. A closer look at Figure 6 shows that the NB classifier shows that the

model correctly predicted money-laundering transactions 39.4% of the time and no-money-

laundering transactions 38% of the time. Together, those numbers represent 77.4% classification

accuracy. On the other hand, the RF model correctly predicted money-laundering transactions

26
32.9% of the time and no-money-laundering transactions 43.8% of the time. Taken together,

those numbers represent 76.7% classification accuracy. The presence of money laundering was

present 38% of the time and absented 62% of the time. Conversely, the RF classifier predicted

money-laundering transactions 50.3% of the time and no-money-laundering transactions 49.7%

of the time. Interestingly enough, the false positive or Type 1 error, where the models predicted

no money laundering, but money laundering occurred, was 0% for the NB model compared to

6.5% for the RF model. When one considers the large percentage of true positives and negatives

for both models, they are fairly good classifiers to predict money-laundering transactions.

Figure 6: Confusion Matrix of Naïve Bayes and Random Forest Classifiers

Artificial Neural Network

The ANN model performed slightly better than the classical ML models. The accuracy of

the training and test sets is 78% and 80%, respectively. These results indicate that the ANN

27
model did an excellent job predicting money-laundering transactions. More importantly, the

model did not suffer from underfitting or overfitting the data. The ANN model is, therefore, very

good at generalizing from the test set. The precision score is 87%, and the recall score is 72%;

these findings indicate that the model is very good at predicting money-laundering transactions

and correctly identifying individuals who are laundering money through the financial system.

Figure 7 presents the results for the ROC curve for both the training and test sets. The AUC for

the test set is 78% (rounded), which indicates that the model performance was decent when

predicting whether there were money-laundering transactions or not.

Figure 7: The ROC Curve

Conclusion

Money laundering is all about converting dirty money into clean funds. The involvement

of financial institutions in money laundering cannot be underestimated. The ML and ANN

28
algorithms employed in this paper perform reasonably well in identifying and labelling money-

laundering transactions (see also Jullum et al., 2020; Tiwari et al., 2020; Zhang and Trubey,

2019). At the very least, compliance officers should use the findings presented here to scrutinize

the features related to the laundering of funds. Features such as the time of the day and the

amounts of money coming in and transferred out should be comprehensively monitored and

scrutinized with regulatory technology. Other factors that should be monitored closely are

international payments, sudden changes in the source of income, considerable anomalies in the

amounts of money transfers, and any other suspicious activity that should be immediately

scrutinized as part of the due-diligence process (Ba and Huynh, 2018).

Financial institutions should use the findings from this paper to maintain lower money-

laundering risks and conduct due-diligence background checks on the source of the cash-in funds

and the destination of the transfer-out funds (Ba and Huynh, 2018; Tran and Nguyen, 2017). The

machine-learning and ANN algorithms can be used to inform and continuously update money-

laundering risks for each customer and incorporate new features such as salary, occupation, and

source of income (Tran and Nguyen, 2017). Any abrupt changes in a customer’s profile will act

as red flags that would be eligible for scrutiny. Indeed, there should be proper training of bank

employees and frontline workers to ensure that they are capable of identifying hot spots

identified by the algorithms (Isa et al., 2015; Usman Kemal, 2014), while not ignoring their

qualitative capabilities and phenomenologically lived human experiences and expertise to

identify and report unusual activities (Usman Kemal, 2014). The findings presented in this paper

support the claim that there is scope to develop ML and AI models to detect illicit activities in

financial institutions.

29
 Financial institutions should use the findings from this paper to maintain lower money-

laundering risks and conduct due-diligence background checks on the source of the cash-in funds

and the destination of the transfer-out funds (Ba and Huynh, 2018; Tran and Nguyen, 2017). The

ML and ANN algorithms can be used to inform and continuously update money-laundering risks

for each customer and incorporate new features such as salary, occupation, and source of income

into the models (Tran and Nguyen, 2017). Any abrupt changes in a customer’s profile will act as

red flags that would be eligible for scrutiny. Indeed, there should be proper training of bank

employees and frontline workers to ensure that they are capable of identifying hot spots

identified by the algorithms (Isa et al., 2015; Usman Kemal, 2014), while not ignoring their

qualitative capabilities and phenomenologically lived human experiences and expertise to

identify and report unusual activities (Usman Kemal, 2014). The findings presented in this paper

support the claim that there is scope to develop ML and AI models to detect illicit activities in

financial institutions.

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