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\begin{document}

\title{Predicting Mobile Money Transaction Fraud using Machine Learning
Algorithms}



\author[1]{Mark Lokanan}%
\affil[1]{Royal Roads University Faculty of Management}%


\vspace{-1em}



  
  \date{May 25, 2024}


\begingroup
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\maketitle
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\selectlanguage{english}
\begin{abstract}
The ease with which mobile money is used to facilitate cross-border
payments presents a global threat to law enforcement in the fight
against laundering and terrorist financing. This paper aims to use
machine learning classifiers to predict transactions flagged as a fraud
in mobile money transfers. Data for this paper came from real-time
transactions that stimulate a~ well-known mobile transfer fraud scheme.
This paper uses logistic regression as the baseline model and compares
it with ensembles and gradient descent models. The results indicate that
the established logistic regression model did not perform too poorly
compared to the other models. The random forest classifier had the most
outstanding performance among all measures. The amount of money
transferred was the top feature to predict money laundering transactions
through mobile money transfers. These findings suggest that more
research is needed to improve the logistic regression model. The random
forest classifier should be further explored as a potential tool for law
enforcement and financial institutions to detect money laundering
activities in mobile money transfers.%
\end{abstract}%



\sloppy


\textbf{Title} : Predicting Mobile Money Transaction Fraud using Machine
Learning Algorithms

\textbf{Author:} Mark Lokanan

\textbf{Corresponding Author} : Mark Lokanan

Mark Lokanan is a data scientist and white-collar criminologist and an
Associate Professor in the Faculty of Management at Royal Roads
University. He is a graduate from the School of Criminology, Simon
Fraser University, Canada.

Mark E. Lokanan Ph.D

Associate~Professor~ Faculty of Management \textbar{}~\textbf{Royal
Roads University}

T 250.391.2600 ext. 4386\#

2005 Sooke Road, Victoria, BC~~Canada~~V9B 5Y2 \textbar{}~royalroads.ca

Mark.Lokanan@royalroads.ca

\textbf{Introduction}

With the increasing popularity of mobile money services, there has been
a corresponding increase in fraud and money laundering cases. Mobile
money providers must therefore be vigilant in combating such activity.
One way to combat fraud is to require users to provide additional
information when making transactions, such as PIN or biometric data.
Money laundering cases can be more difficult to detect, but mobile money
providers can look for patterns of suspicious activity, such as
unusually large or frequent transactions. The Financial Action Task
Force (FATF) noted that mobile money payment presents a global threat to
money laundering and terrorist financing as it can be used to facilitate
cross-border payments without the need for a bank account. In response,
the FATF released risk-based approaches to countering the threat. The
guidance sets out recommendations for identifying and managing risk,
including the use of computational technology.

The use of machine learning (ML) and artificial intelligence (AI) is
increasingly seen to combat mobile money fraud and address anti-money
laundering (AML) compliance. Computational technology has always played
a role in the fight against financial crime, but the rise of ML and AI
is giving law enforcement a powerful new tool in the battle against
mobile money fraud. AI can help financial institutions to identify and
flag suspicious behaviour, such as large or unusual transactions, and
better understand their customers' needs and risk profiles. By
harnessing the power of AI, financial institutions can significantly
improve their ability to combat mobile money fraud and address money
laundering threats. This paper aims to use ML learning algorithms to
build a fraud detection model that will detect red flags of fraud and
money laundering from mobile money transactions. More specifically, this
paper will use a set of risk-based indicators to predict how likely a
transaction will be fraudulent.

This study provides several significant advances to the existing body of
research on methods for detecting suspicious transactions in mobile
money transfers. In theory, machine learning algorithms can circumvent
the challenges of attempting to identify illegal transactions by relying
on the more conventional rule-based benchmark methodology. In the
classic rule-based benchmark technique, identifying illegal transactions
is accomplished by using predefined criteria based on mathematical
conditions. The rule-based approach is time-consuming and costly and has
a high rate of false-positive results. ML addresses these issues by
enabling computers to learn from the data and make predictions. When
applied to mobile money, ML can be used to enable automated detection of
potentially fraudulent transactions. An example of this would be
training a ML algorithm on a dataset containing transactions that are
known to be fraudulent. Based on this learned experience, the algorithm
can be tuned to find future fraudulent transactions by looking for
patterns similar to those found in the training data.

Practically, we propose a novel data-driven method of fraud detection
that has been precisely tuned to the distinctive features of mobile
money transactions. This strategy uses ML to automatically identify
suspicious transactions in real-time, eliminating the need for extensive
human involvement. The ML approach, which uses real-time analysis, can
quickly spot transactions that could be fraudulent and stop them from
going through and reduce the number of fraudulent transactions.

The remainder of this paper is structured in the following manner.
Section two thoroughly analyzes the literature on ML and mobile money
transfers concerning fraud and money laundering. Section three discusses
the methodology and algorithms considered for the ML models. Section
four examines and analyzes the results. Section five concludes with
limitations in ML for fraud research and identifies opportunities for
further study.

\textbf{Literature Review on Mobile Money Fraud and Money Laundering}

Mobile Money Services (MMS) or Mobile Money Transfer Services (MMTS) are
unbanked financial services that operate primarily through smartphone
apps supported by mobile operators or banking institutions and are
frequently referred to as branchless banking services for their
users\textsuperscript{1,2}. They facilitate the fund transfer of
electronic cash using the users' mobile phones while not involving any
bank account in the process \textsuperscript{3,4}. A few common examples
of MMS or MMT services are: Tigopesa, M-Pesa, Simbanking, and NMB Mobile
offered by Tigo Tanzania Ltd, Vodacom Tanzania, CRDB Bank, and National
Microfinance Bank respectively \textsuperscript{5(p4)}. Ideally, MMT
enables person-to-person (P2P) payments for the customers, and the
services supported by the mobile money system involve participation from
various stakeholders like mobile users, regulators, mobile network
operators, telecom retailers, agents, and financial
institutions\textsuperscript{6}. The mobile users act as the customers
for the MMT services, while mobile network operators (MNOs) completely
facilitate the ecosystem of MMS in conjunction with telecom retailers
and agents, who are responsible for opening accounts for the customers,
conducting customer due diligence and other compliance activities like
KYC, and Know Your Customer. Financial institutions and regulators
assist MNOs in establishing financial inclusion and risk management
mechanisms, whereas MNOs limit banks to processing payment delivery,
clearing, and settlement \textsuperscript{6}. A bank can or cannot be
involved in the MMS depending on the adopted model of MMT
\textsuperscript{7}. These players collectively enable MNOs to implement
the new P2P payment facility for unbanked users.

The number of users using mobile money for small or large transactions
has increased drastically in the last decade \textsuperscript{1}.
Research estimates that this number is expected to rise with the
increasing dependency and usage of mobile phones in the
future\textsuperscript{8,9}. Due to their success and popularity, mobile
money systems are set to attract the attention of fraudsters interested
in laundering the proceeds of crime \textsuperscript{10}. Fraudsters can
launder money by seizing the details related to several mobile money
transfers during transmission or creation and saving the server's data
through phishing attacks or viruses, which can then be misused to
launder illicit funds \textsuperscript{8,11}. Similarly, the reprobate
end users of MMS can launder their dirty money through this system by
smurfing a large chunk of the illegitimate source of income into a small
number of mobile money transactions, using multiple accounts and phones
while avoiding the suspicious nature of the act \textsuperscript{3,6}.
Indeed, some speculate that this system could be used to fund terrorist
activities, though there is evidence that launderers have used mobile
transfers to launder funds for terrorist financing \textsuperscript{12}.
These findings have brought the need for more advanced technology to
identify and control the risks associated with the mobile money system.

\textbf{Detecting Mobile Money Fraud and ML Using Computational
Technology}

Technological innovation can be useful in mitigating various risks
associated with the MMS. Improving technological surveillance by
increasing the security, resilience, and scalability of MNO networks
used in MMT can reduce risks associated with mobile money fraud (MMF) to
some extent at the security and procedural levels \textsuperscript{13}.
Implementing the two-factor authentication model for securing
communications through SMS in MMT has proven very
effective\textsuperscript{14}. The most important contribution
technology can make, other than the new developments in the security
information and events management field, is through innovation or by
designing MMF and money laundering prediction or detection tools for the
MMS. In the following paragraphs, we will focus on using ML algorithms
and artificial intelligence to mitigate the risk of MMF and money
laundering in mobile money transactions.

\textbf{Machine Learning and Artificially Intelligent Algorithms}

Technology is pivotal in investigating and detecting fraudulent or
laundered mobile money transactions\textsuperscript{15}. ML, AI, and
data mining have proven effective in detecting MMF and money laundering
activities in the MMS \textsuperscript{16,17}. More specifically, ML
algorithms teach computers to learn human behaviour and detect patterns
in the data \textsuperscript{17,18}. Supervised ML algorithms like
logistic regression, decision tree, gradient descent, and random forest
have all been successfully used in detecting financial fraud from
labelled data \textsuperscript{16,18--22}. The following sections are
devoted to reviewing the literature on these algorithms.

\textbf{Logistic Regression}

Logistic regression will be used as the baseline algorithm to compare
with the other models. Logistic regression uses a linear combination of
input variables (\emph{x} ) to predict an output variable (\emph{y}
)\textsuperscript{20}. The output variable is usually (0 or 1),
representing the two possible outcomes of a binary classification task
(e.g., fraud or not fraud). The coefficients of the input variables
(\selectlanguage{greek}\emph{β} \selectlanguage{english}) are estimated using maximum likelihood estimation. The
Sigmoid function is a mathematical function that is the foundation of
logistic regression and takes an actual number and translates it into a
value between 0 and 1. The Sigmoid translation is important for ML
learning classification tasks because it allows the algorithm to easily
separate data points into different classes \textsuperscript{23}. The
sigmoid function is denoted in equation 1.

Where

f(\emph{x} ) is the value bounded between 0 and 1,

\textsubscript{X} is he derivative of the sigmoid function,

\emph{e} is the mathematical constant

f(\emph{x} ) = 1 / (1 + \emph{e} \^{}-\emph{x} ). \emph{eq} . 1

The output of the sigmoid function can be interpreted as a probability.
For example, if the output of the Sigmoid function is 0.8, this can be
interpreted as an 80\% chance that the data point belongs to one class
and a 20\% chance that the data point belongs to the other
class\textsuperscript{24,25}. Using the Sigmoid function, a logistic
regression model can be trained to predict the class to which a new data
point belongs. The logistic regression classifier uses the Sigmoid
function to estimate the probability that~\emph{y} ~= 1, given the size
of \emph{x} . Equation 2 denotes the logistic regression model.

Where

\emph{Y} = values between 0 and 1,

\(e\beta_{0+}\beta_{1+}X\) represents the independent features, and

\emph{B\textsubscript{0}} and \emph{B\textsubscript{1}} will give
different estimations of \emph{Pr}

\(\Pr\mathbf{(}Y\mathbf{=1|}X\mathbf{=}x\mathbf{)=\ }\frac{e\beta_{0+}\beta_{1+}X}{1+e\beta_{0+}\beta_{1+}X}\)\emph{eq} . 2

Logistic regression is a valuable technique for fraud classification
tasks \textsuperscript{20,24,25}. Research has shown that the logistic
regression performed relatively well and, in some cases, outperformed
other classifiers in fraud classification
tasks\textsuperscript{20,23,25,26} . Logistic regression has been used
in a variety of domains to predict fraud. For example, logistic
regression has been used in the financial sector to detect credit card
and insurance fraud \textsuperscript{23,27}. Others have used logistic
regression to predict medical billing fraud with reliable
results\textsuperscript{28,29}. Logistic regression models have several
advantages over traditional fraud detection methods. First, it is highly
scalable and can be applied to large data sets \textsuperscript{29}.
Second, it is highly effective at detecting fraud, with a success rate
that is generally much higher than traditional
methods\textsuperscript{23}. In addition, logistic regression models are
relatively easy to interpret, which makes them valuable tools for fraud
analysts \textsuperscript{20,25}. Finally, it is relatively easy to
deploy and use in production systems \textsuperscript{20,23}. However,
logistic regression is not without drawbacks; in particular, it can be
susceptible to overfitting if the data is not carefully
preprocessed\textsuperscript{24,27}. Even though model overfitting is a
problem, logistic regression is an excellent way to build a fraud
detection model that can be used as a benchmark to compare with other
classifiers.

\textbf{Decision Tree}

Another useful machine learning algorithm for fraud detection is the
decision tree classifier \textsuperscript{19}. Decision tree employs a
tree structure for choice making, where the root symbolizes the
fundamental decision, edges display the decision node, leaves show the
class labels that convey the decision, and internal modes indicate
qualities picked based on information gain or Gini
Index\textsuperscript{18,30}. Typically considered a weak learner,
decision tree classification ability is boosted by using the gradient
boosting technique \textsuperscript{31}. Gradient boosting is an
ensemble learning technique that optimizes performance accuracy by
sequentially generating the decision tree so that it is always superior
to the previous one.\textsuperscript{16,18(p8)}. This project employs
the Gini Index to label the data. The mathematical formula for Gini
Index is shown in equation 2:

Where

\emph{fk} is the fraction of items labeled with \emph{k} in the set and
[?] \emph{fk} = 1.

\emph{I} \textsubscript{G} (\emph{f} )
=\(=\sum_{k=1}^{m}f\)\textsubscript{k} (1-\emph{f\textsubscript{k}}
)\emph{eq.} 4

Concerning fraud detection, a decision tree involves building a model
that can predict whether an observation is legitimately derived or
not\textsuperscript{32}. The decision tree model is based on a series of
yes-or-no questions, each narrowing down the possible outcomes (i.e.,
fraud or no fraud) \textsuperscript{33}. For example, a decision tree
for fraud detection might ask whether the transaction is consistent with
the customer's past behaviour. If the answer is no, it could be flagged
as potentially fraudulent. Once the model is built, it can be used to
classify new data points as either fraudulent or non-fraudulent.
Decision tree algorithms are highly effective in identifying fraud. They
are often used with other methods, such as rule-based systems and
ML\textsuperscript{18,19,33}. Classification algorithms based on
decision trees are a powerful way to find fraud because they can help
find even the most complex kinds of fraud.

\textbf{Gradient Descent}

Gradient descent is a machine learning algorithm that uses first-order
iterative optimization to find the minimum of a function. To locate a
function's local minimum using gradient descent, one must take steps
proportional to the function's negative gradient (or approximate
gradient) at the current point \textsuperscript{18}. Instead, if one
takes steps proportional to the gradient's positive, one approaches a
local maximum of that function, known as gradient
ascent\textsuperscript{34}. It is an optimization algorithm used to find
the values of parameters (coefficients) of a function (\emph{f} ) that
minimizes a cost function (\emph{c} ). The cost function is a measure of
how far away the predicted values are from the actual values. The
algorithm iteratively adjusts the coefficients until it converges on a
set of coefficients that minimizes the cost
function\textsuperscript{34,35}.

The algorithm is represented by the probabilistic formula where the
likelihood function~\emph{p} (\emph{x} , ?0, ?1) predicts the
probability of a binary outcome given a set of independent variables. In
this case, the algorithm is trained to predict whether an instance
belongs to class 0 or 1, which are represented by the labels ? = 0 and ?
= 1. The coefficients ?0 and ?1 represent the probability of the output
y to be 1 or 0 given \emph{x} . In other words, ?0 and ?1 are the log
odds of the output being 1 or 0 given \emph{x}
\textsuperscript{18,34}.~The gradient descent algorithm is popular for
machine learning applications, particularly in fraud detection, because
the algorithm can learn from data very quickly and effectively.
Additionally, the gradient descent algorithm can handle very large
datasets, making it ideal for fraud detection applications requiring
high accuracy. Numerous studies have been conducted on the efficacy of
the gradient descent algorithm for fraud detection. Most of these
studies have found that the algorithm effectively detects fraudulent
activities \textsuperscript{36,37}. Recent studies have found that the
algorithm was very effective in detecting known fraud cases in a dataset
of credit card transactions\textsuperscript{36,38}. Another study found
that the algorithm could successfully identify fraudulent insurance
claims with high accuracy\textsuperscript{37}. Furthermore, the gradient
descent algorithm is also relatively easy to implement, which makes it a
good choice for organizations that do not have a lot of resources or
expertise in fraud detection methods.

\textbf{Random Forest}

Random Forest is a machine learning algorithm for classification and
regression tasks and is learned base on the decision tree
concept\textsuperscript{22,23,39}. The random forest algorithm generates
a series of decision trees, each created using a randomly chosen subset
of the training data \textsuperscript{40,41}. Even though random forest
is a useful learning algorithm that can be used to solve both linear and
nonlinear problems, it is especially useful for addressing nonlinear
data \textsuperscript{16,39}. The predictions of the individual trees
are then combined to produce the final prediction. The random forest
algorithm is effective because it reduces the prediction variance while
maintaining the model's accuracy \textsuperscript{41,42}. Additionally,
the random forest algorithm, when pruned, is resistant to overfitting,
which means it can handle large datasets and generalize well to new
data. Because of these advantages, the random forest algorithm is a
powerful tool for machine learning applications.

The advantage of the random forest algorithm for classification tasks is
that it can help reduce the number of false positives generated by other
AI methods, such as neural networks \textsuperscript{40}. In addition,
the random forest technique is not difficult to construct, and it is
possible to execute the algorithm on large datasets with accurate
performance \textsuperscript{42}. These two features combine to make the
algorithm a powerful instrument for discovering patterns in data. In
particular, the random forest classifier is well-suited for detecting
fraud, often identifying unusual patterns in the
data\textsuperscript{19,41}. For example, fraudsters might create
multiple accounts with different email addresses and use them to make
small purchases to avoid detection. Alternatively, they might try to
return items they never bought to receive a refund. By looking for these
and other unusual patterns, the random forest algorithm can help to
detect fraud before it results in significant losses. In addition, the
random Forest classifier has also been used in other domains such as
loan default, credit risks, image recognition, and medical
diagnosis\textsuperscript{19,23,39,41}. The random forest classifier has
proven to be a versatile tool algorithm and has been used in various
domains.

\textbf{Research Design and Experimental Setting}

\textbf{Data Generation and Stimulation}

Currently, there is a lack of data on fraud and money laundering
detection \textsuperscript{43}. One of the reasons cited for this
outcome is confidentiality and the sensitivity of the data. Researchers
have developed stimulators that use algorithms to generate synthetic
data from real-time observations to address this problem. Some of the
most prominent stimulators used by researchers are the Mobile Money
Simulator (PaySim) and Retail Store Simulator
(RetSim)\textsuperscript{43,44}. These simulators allow researchers to
generate synthetic transactional data that contains both legitimate and
fraudulent transactions. \textsuperscript{43} and
\textsuperscript{44}demonstrated using Agent-Based Simulation (ABS) and
Multi Agent-Based Simulations (MABS) that synthetic transactional data
developed by PaySim and RetSim are as useful as real transaction data
for detecting MMF and money laundering activities while retaining the
reliability and confidentiality of the actual transaction data.

The data for this project came from an MABS that was used to calibrate
real-time transactions. The data came from Lopez-Rojas and his
colleagues' work, who use MABS to develop agents representing clients
and merchants in PaySim and customers and salesmen in
RetSim\textsuperscript{43,44}. The data is simulated and uses a
real-world scenario based on a well-known fraud scheme to demonstrate
the superiority of simulated data over real-world data when establishing
adequate controls for fraud detection \textsuperscript{45}. The usual
behaviour was derived from the behaviour that was observed in the data
collected. This behaviour is enshrined in the agents' rules governing
the transactions and interactions between consumers and salespeople or
between customers and merchants. Based on patterns of actual
fraud\textsuperscript{43}, some of these agents were set up to commit
fraud.

\textbf{Data Description and Variables}

PaySim is used to simulate mobile money transactions in this dataset.
The simulations are based on a sample of actual mobile money
transactions that were taken from one month's worth of financial logs
generated by a mobile money service that was deployed in an African
nation. The first logs were given by a global firm that is the supplier
of the mobile financial service presently operational in more than 14
countries. The company provided the original logs. In total, 1048575
rows of data were collected that comprised nine independent features.
Table 1 depicts the features and target variables that represent the
dataset.

\textbf{Table 1:} Independent Features and Description\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{Features} & \textbf{Description} & \textbf{Measures} &
\textbf{Indicators}\tabularnewline
\midrule
\endhead
Step & Map the unit of time & Continuous & 1 step = 1
hour\tabularnewline
Type & Type of transfer & Categorical & Cash-in, Cash-out, Debit,
Payment, Transfer\tabularnewline
Amount & Amount of the transaction & Continuous &\tabularnewline
nameOrig & Customer who started the transaction & Continuous
&\tabularnewline
oldbalanceOrg & Initial balance before transaction & Continuous
&\tabularnewline
newbalanceOrg & New balance after transaction & Continuous
&\tabularnewline
nameDest & Recipient of transaction & Continuous &\tabularnewline
oldbalanceDest & Initial balance recipient before the transaction &
Continuous &\tabularnewline
newbalanceDest & New balance recipient after the transaction &
Continuous &\tabularnewline
\bottomrule
\end{longtable}

The dependent variable is fraud. ''Fraud,'' in this context, refers to
the transactions carried out by fraudulent actors inside the simulation.
More specifically, the fraudulent activity of the agents tries to profit
by seizing control of client accounts and laundering the cash by moving
them to another system. The funds are then cashed out of the system.
Fraud was coded as 1 = fraud and 0 = no fraud and is represented in
equation 1.

\(y\ =\{1,fraud,\) 0

\(\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ no-fraud\ \}\)\emph{eq} . 1

\textbf{Data Cleaning and Preprocessing}

Some features were redundant and had to be dropped before model
building. As a result, the features ''nameorig'' and ''nameDest'' are no
longer relevant and must be removed. There was no longitude or latitude
associated with these features to locate the destinations. There was no
variation in the feature ''isFlaggedFraud,'' and it was also dropped
from the model.

\textbf{Feature Scaling}

One of the most important aspects of preprocessing data for ML is
feature scaling. Preprocesing is especially necessary when the features
are on different scales and span a wide range of values. ML models are
highly sensitive to features with different scales and, if not handled
properly, can throw off the model and lead to sub-optimal performance or
even incorrect predictions. There are a few different ways to scale
features, but the most common is min-max scaling. This approach scales
all values to be between 0 and 1. Other methods include standardization,
which scales values so that they have a mean of 0 and a standard
deviation of 1. The data for this project contains features with
different scales and ranges. Given that the data was not normally
distributed, normalization with MinMaxScaler was used to normalize the
data. The formula used to normalize the data is shown in equation 5.

\emph{X-}\(\overset{\overline{}}{X}\) \emph{eq}. 5

\emph{X} -\emph{X}

Min Max

\{0 1\}

\textbf{SMOTe-ENN for Imbalance Data}

The dataset used for this study was highly imbalanced. The fraud to no
fraud ratio was 99\% (no fraud) and 1\% (fraud). One common approach
when working with imbalanced datasets is to upsample the minority class.
Upsampling can be done in various ways, but one popular method is the
Synthetic Minority Oversampling Technique (SMOTe) plus Edited Nearest
Neighbour (ENN). The SMOTe-ENN method combines the SMOTe and ENN
algorithms to improve the performance of the ML
classifiers\textsuperscript{46,47}. SMOTe creates synthetic minority
examples by interpolating between existing minority examples
\textsuperscript{48}. ENN then cleans up the resulting oversampled data
set by removing outliers \textsuperscript{49}. The SMOTe-ENN method has
been shown to be more effective than either algorithm alone
\textsuperscript{46,47,49}. SMOTE-ENN is particularly effective at
handling imbalanced data sets, often in real-world applications. As a
result, the SMOTe-ENN method is often used in fields such as credit
scoring and fraud detection\textsuperscript{47,49}.

Figure 1 shows the data before SMOTe-ENN resampling. Because of the
imbalanced nature of the data, the no-fraud observations are scattered
along the dotted red line. ML classifier modelling on imbalanced data
will lead to biased results, with the algorithms only reading the
no-fraud observations \textsuperscript{19,40}. Oversampling with
SMOTe-ENN will create new, synthetic data points similar to the existing
minority class.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1:} Data before SMOTe-ENN Application

Figure 2 shows the data after SMOTe-ENN oversampling. Note that the data
on the red line is more densely packed and is now moving in the same
direction as the blue line. This symmetry of the lines is caused by the
synthetic data points generated by SMOTe-ENN. These data points are not
identical to the actual data points but are close enough to be used to
train the model \textsuperscript{46,47}. The resultant effect is a more
accurate model, better able to classify the new data points. By
artificially generating additional data points, SMOTe-ENN can ensure
that the model can learn from the data and generalize to new inputs
(Chawla et al., et al., 2002). In this way, SMOTe-ENN can help to
improve the performance of ML models on imbalanced
datasets\textsuperscript{46,47}.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2:} Data After SMOTE-ENN Application

\textbf{Fraud Model Performance Measures}

The results were analyzed using the standard confusion matrix. A
confusion matrix is a table that helps calculate the accuracy of a
classification model and the precision, recall, and f1-score. The table
is made up of true positive (TP), false positive (FP), false negative
(FN), and true negative (TN) values \textsuperscript{15}. For a 2x2
binary classification, the confusion matrix as it pertains to this
project can be deciphered as follows:

\textbf{True Positive (TP):~} The algorithm predicts the fraud, and the
outcome is fraud.

\textbf{True Negative (TN):} The algorithm predicts no fraud and there
was no fraud.

\textbf{False Positive (FP):~} The algorithm predicted fraud, but there
was no fraud (Type 1 Error).

\textbf{False Negative (FN):} The algorithm predicted no-fraud, but
there was fraud (Type II Error)

Table 2 presents the performance measures used in the models for this
paper. Accuracy is the proportion of correct predictions over all
predictions. However, accuracy is not the best metric for an imbalanced
dataset. An improved single measure is the Matthews Correlation
Coefficient (MCC) \textsuperscript{50}. The MCC is a measure of the
quality of binary classification. The MCC considers true and false
positives and negatives and is widely regarded as a balanced measure
that can be applied even when the classes are of very different
sizes\textsuperscript{51}. The MCC is in the range {[}-1, 1{]}. A
coefficient of +1 represents a perfect prediction, a coefficient of 0
represents an average random prediction, and a coefficient of -1
represents an inverse prediction \textsuperscript{52}. The MCC has some
valuable properties that make it more suitable than other measures for
some purposes, most notably its ability to work well even when one of
the two classes is much more frequent than the other
\textsuperscript{50,51}. The formula for both the performance accuracy
and the MCC is shown in Table 2.

\textbf{Practitioner Measures}

To understand how well a classification model is performing, we need to
look beyond the performance accuracy. The accuracy may be high, but this
could be due to the model predicting the most frequent class all the
time, which produces inconsistent and unreliable results for an
imbalanced dataset. To get a better idea of model performance, the
confusion matrix can be used to calculate the performance of other
metrics. From the confusion matrix, we can calculate the precision,
recall, and F1-score. Precision is the number of correct predictions
divided by the total number of predictions. The recall is the number of
correct predictions divided by the total number of actual positive
cases. Generally, a classifier with a higher precision but lower recall
will miss some fraudulent items but will not incorrectly predict too
many items as fraud. A classifier with a higher recall but lower
precision will correctly identify more of the fraud items and
incorrectly predict more items as being a fraud. The ideal classifier
would have perfect precision and recall, but this is usually impossible
in practice. Instead, the goal is usually to find a balance between
precision and recall that gives the best overall results. The F-1 score
is the harmonic mean of precision and recall that achieves this
objective. A good classification model will have a high precision,
recall, and F1 score \textsuperscript{19}.

A more robust measure is the Receiver Operating Characteristics (ROC).
The ROC curve is a graphical tool used to evaluate the performance of a
binary classifier. The curve is generated by plotting the true positive
rate (TPR) against the false positive rate (FPR) at various threshold
values. The area under the ROC curve (AUROC) is a metric that can be
used to compare different classifiers. A classifier with a higher AUC
will have better discrimination, meaning it can better distinguish
between positive and negative observations. A perfect classifier would
have a TPR of 1 and an FPR of 0, resulting in a point in the upper left
corner of the ROC curve. Generally, the closer the ROC curve is to this
corner, the better the classifier performs. Classifiers that perform
similarly to random guessing will have a ROC curve close to the diagonal
line \textsuperscript{18,19}.

\textbf{Table 2:} Performance Metrics and Formulae\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{Performance Metrics} & \textbf{Formulae}\tabularnewline
\midrule
\endhead
Accuracy & (TP+TN)/(TP+TN+FP+FN)\tabularnewline
MCC & (TP*TN -- FP*FN) / [?](TP+FP)(TP+FN)(TN+FP)(TN+FN)\tabularnewline
Precision & TP/(TP+FP)\tabularnewline
Recall & TP/(TP+FN)\tabularnewline
F1-score & 2*((precision*recall)/(precision + recall))\tabularnewline
ROC Curve & Plot TPR (TP/TP+FN) and FPR (FP/TN+FP)\tabularnewline
\bottomrule
\end{longtable}

\textbf{Results and Analysis}

\textbf{Descriptive Results}

Table 3 provides information on the results of numerical features for
money laundering transactions. The average amount of money transferred
was \$159,000---about \$76,000, or 50\% of total laundered transactions,
made up most of the transfers. The highest amount laundered was \$10
million. The average initial balance before the transaction was
\$875,000, while the average new balance after the transaction was
\$895,000. Note also that the maximum initial balance before the
transfer and the new balance after the transfer was \$38 million. The
average initial balance of the recipient before the transaction was
\$978,000, while the average new balance after the transaction was
higher at \$1.1 million. Once again, it is worth noting that the maximum
initial balance and the new balance of the recipient after the transfer
was over \$40 million. These findings suggest that money laundering is a
significant problem, with large sums of money being transferred
illegally \textsuperscript{11,40}.

The findings of this study have implications for both the government and
the public. First, the findings indicate that much illegal activity is
not being detected or stopped \textsuperscript{40}. Second, large
amounts of money are not being taxed, which means the government is
losing a lot of revenue \textsuperscript{53}. Third, the findings prove
that an underground economy operates outside the legal
system\textsuperscript{53,54}. A large underground economy can have
several negative consequences, such as making it more difficult for
law-abiding businesses to compete and increase the chances of crime and
corruption\textsuperscript{55}. Regulators need to take action to
address the problem of the underground economy or risk people being
doubtful of the government's ability to regulate the financial system.

\textbf{Table 3:} Summary Statistics\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
& \textbf{amount} & \textbf{oldbalanceOrg} & \textbf{newbalanceOrig} &
\textbf{oldbalanceDest} & \textbf{newbalanceDest}\tabularnewline
\midrule
\endhead
\textbf{count} & 1.05E+06 & 1.05E+06 & 1.05E+06 & 1.05E+06 &
1.05E+06\tabularnewline
\textbf{mean} & 1.59E+05 & 8.74E+05 & 8.94E+05 & 9.78E+05 &
1.11E+06\tabularnewline
\textbf{std} & 2.65E+05 & 2.97E+06 & 3.01E+06 & 2.30E+06 &
2.42E+06\tabularnewline
\textbf{min} & 1.00E-01 & 0.00E+00 & 0.00E+00 & 0.00E+00 &
0.00E+00\tabularnewline
\textbf{25\%} & 1.21E+04 & 0.00E+00 & 0.00E+00 & 0.00E+00 &
0.00E+00\tabularnewline
\textbf{50\%} & 7.63E+04 & 1.60E+04 & 0.00E+00 & 1.26E+05 &
2.18E+05\tabularnewline
\textbf{75\%} & 2.14E+05 & 1.37E+05 & 1.75E+05 & 9.16E+05 &
1.15E+06\tabularnewline
\textbf{max} & 1.00E+07 & 3.89E+07 & 3.89E+07 & 4.21E+07 &
4.22E+07\tabularnewline
\bottomrule
\end{longtable}

As can be seen in Figure 3, there are five unique types of transactions:
cash-out, cash-in, payment, transfer, and debit. Cash-out is the most
frequent money laundering transaction, accounting for nearly 36\% of all
transactions. Cash-out is followed by payment (34\%), cash-in (22\%),
transfer (8\%), and debit (1\%). As expected, most transactions are in
the form of cash (70\%), with the remainder made up of payments,
transfers, and debit cards. As noted above, the average amount laundered
per transaction is \$159,000, with a standard deviation of \$265,000.
These results show that money laundering is usually done with large sums
of money, though the amount washed in each transaction varies
greatly\textsuperscript{6,40}.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3:} Types of Transfer

\textbf{Analytical Results}

\textbf{Baseline Logistic Regression}

In this study, we sought to compare the performances of several
different classifiers to determine which would be best suited for
predicting financial transaction fraud. Logistic regression was used as
the base model, then compared with the results of other classifiers. As
shown in Figure 4, all the features except cash-in, debit, and payment
type are statistically significant at a~\emph{p-value} ~of 0.05 and with
a confidence interval of 95\%. These findings suggest that the other
classifiers may be more accurate in predicting fraud than the logistic
regression model \textsuperscript{25}. More research is needed, but the
logistic regression results suggest that other classifiers should be
considered when predicting financial transaction fraud. To build the
different classifiers and to see how the model performs using the
classification metrics precision, recall, and the F1-score, the features
with~\emph{a p-value} ~\textgreater{} 0.05 will be dropped from the
dataset.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4:} Logistic Regression Results

The coefficients of the logistic regression model are in terms of
log(odd). The log(odd) in Table 4 lacks interpretation since it does not
directly give the odds of an event occurring. Instead, they show how
much each feature contributes to the model, meaning that they are more
likely to predict whether or not an event will occur. Based on the
logistic regression model, the amount involved in the transfer is the
most important feature in detecting fraud. The features that positively
affect fraud detection are amount, cash-out, and transfer. The features
which negatively affect fraud prediction are the initial balance of the
recipient before the transaction (i.e., oldbalanceDest) and the new
balance of the recipient account after the transaction (i.e.,
newbalanceDest). However, these coefficients cannot be directly
interpreted without first taking the exponential of the features. To
find the odds, we must take the exponential of the coefficients. For
example, if we take the exponential of-0.693, we get 0.5, which means
that for every unit increase in~\emph{X\textsubscript{1}} , the odds
of~\emph{y = 1} ~decrease by 0.5. Holding all other variables constant,
a one-unit increase in~\emph{X\textsubscript{1}} ~leads to a 50\%
decrease in the odds of~\emph{y = 1} .

\textbf{Table 4:} Odds of Logistic Regression\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\textbf{Features} & \textbf{Odds} &
\textbf{Change\_odd\%}\tabularnewline
\midrule
\endhead
amount & 7.97E+03 & 797239.66945\tabularnewline
oldbalanceDest & 2.01E-07 & -99.99998\tabularnewline
newbalanceDest & 5.63E-03 & -99.43677\tabularnewline
type\_CASH\_OUT & 1.64E+00 & 64.21473\tabularnewline
type\_TRANSFER & 4.29E+00 & 329.07871\tabularnewline
\bottomrule
\end{longtable}

Table 5 provides an overview of the odds of the features and how they
affect fraud. Note that the initial balance of the recipient before the
transaction and transfer type have the highest odds of predicting fraud.
Therefore, these two features are important when trying to predict
fraud. However, we should also consider other factors, such as the
amount of money being transferred, the country where the recipient is
located, and whether or not the recipient has a bank account. These
features can provide additional insights into whether or not a
transaction is fraudulent \textsuperscript{40}. For instance, if the
amount of money being transferred is very large, it is more likely to be
fraudulent. Similarly, this is another red flag if the recipient is
located in a country with a high risk of fraud.

\textbf{Table 5:} Odds of the Features Occurring\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{Features} & \textbf{odds}\tabularnewline
\midrule
\endhead
oldbalanceDest & 8.47E+12\tabularnewline
amount & 4.20E+08\tabularnewline
type\_TRANSFER & 6.11E+04\tabularnewline
type\_CASH\_OUT & 2.65E+04\tabularnewline
newbalanceDest & 3.42E-19\tabularnewline
\bottomrule
\end{longtable}

\textbf{Performance Accuracy and Matthew Correlation Coefficient}

The models were analyzed using the SK learn library after dropping the
features with p-values greater than 0.05. Table 1 presents the
performance accuracy and the MCC results for the different models
tested. Compared to the gradient descent and the ensemble classifier,
the benchmark logistic regression model did not fear much. The ensemble
classifier, a combination of multiple models, showed the best
performance in terms of accuracy and MCC. The superior performance of
the ensemble classifiers is because they can capture different patterns
in the data and weigh them appropriately, resulting in more accurate
predictions \textsuperscript{41,56}. In terms of computational cost,
gradient descent was the most expensive model to run, while logistic
regression was the least expensive \textsuperscript{34}. Therefore, if
computational cost is a concern, logistic regression may be a better
option despite its slightly lower accuracy. Overall, all the models
performed reasonably well, with ensembles being the best performers in
terms of accuracy and MCC.

As shown in Table 6, the MCC had poorer performance overall than the
model accuracy. The random forest model had the highest performance
accuracy (.89) and MCC (.78) on the test set, although it showed signs
of overfitting. The next best performing classifier was the decision
tree classifier, with a performance accuracy of .86 and an MCC of .75.
The gradient descent classifier had a similar accuracy (.82); but a
lower MCC (.67). These results suggest that while the random forest
model is more likely to overfit the data, it may still be the best
option for this dataset due to its higher accuracy. However, further
tuning of hyperparameters may be necessary to improve its performance.
These results suggest that, while gradient descent may be a
computationally efficient method, it is not as effective as other
methods in predictive power. In conclusion, the random forest model is
the best-performing model in terms of accuracy and MCC.

\textbf{Table 6:} Algorithms Performance\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Algorithm} & \textbf{Performance Accuracy} & \textbf{Performance
Accuracy} & \textbf{MCC} & \textbf{MCC}\tabularnewline
\midrule
\endhead
& \textbf{Train} & \textbf{Test} & \textbf{Train} &
\textbf{Test}\tabularnewline
\textbf{Logistic Regression} & 0.82 & 0.83 & 0.66 & 0.68\tabularnewline
\textbf{Gradient Descent} & 0.8 & 0.82 & 0.66 & 0.67\tabularnewline
\textbf{Decision Tree} & 0.83 & 0.86 & 0.71 & 0.75\tabularnewline
\textbf{Random Forest} & 1.0 & 0.89 & 0.99 & 0.78\tabularnewline
\bottomrule
\end{longtable}

\textbf{Classification Measures}

When dealing with imbalanced datasets, it is important to use measures
of performance that are more robust than simple
accuracy\textsuperscript{40}. The performance accuracy only measures the
FP and FN and does not provide enough information to properly assess the
model's performance. When dealing with imbalanced datasets, it is
important to use measures of performance that are more robust than
simple accuracy \textsuperscript{37}. In addition, we need to consider
the TN and TP rates because these measures provide a complete picture of
the model's performance. For example, if we have a high TN rate, our
model correctly identifies negative instances. On the other hand, if we
have a low TN rate, our model incorrectly identifies positive instances.
As a result, the TN and TP rates are significant for assessing the
performance of imbalanced datasets \textsuperscript{15}.

When choosing a machine learning model for fraud detection, it is
important to consider how well the model will perform in terms of
precision, recall, and the F1- score \textsuperscript{40,49}. These
measures are better indicators of how well a model predicts laundered
transactions than accuracy alone. However, it is important to remember
that all of these measures can be affected by choice of threshold. A
high threshold will result in fewer FPs and TPs, while a low threshold
will have the opposite effect. Therefore, tuning the threshold according
to the application's needs is important. In some cases, it may even be
necessary to use multiple thresholds to achieve the desired level of
performance \textsuperscript{20,42}. To offset the trade-off between
precision and recall, the threshold was tuned to ensure that it was
equal before running the classifiers.

Table 7 shows the results of the classifiers' performance. Note that the
decision tree classifier had the highest precision score (1.0), followed
by the random forest model (.96). In contrast, the logistic regression
and gradient classifiers had the highest recall scores (.96). The random
forest (.87) followed by logistic regression (.85) had the highest F1
scores. The gradient descent and decision tree classifiers had the
lowest F1- scores, respectively (.84). These results indicate that the
random forest is the best performing classifier overall because the
model achieved high scores across all three measures. Contextually,
these results can be interpreted to mean that the random forest
classifier correctly identifies a high proportion of positives and
negatives and achieve a high degree of overall accuracy. The logistic
regression and gradient descent classifiers also performed well,
achieving high scores in recall and F1. However, they did not achieve
the same high precision score as the random forest classifier. These
results suggest that the logistic regression and gradient descent
classifiers may misclassify more cases than the random forest
classifier.

\textbf{Table 7:} Algorithms Classification Metrics\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
& \textbf{Classification Metrics} & \textbf{Classification Metrics}
&\tabularnewline
\midrule
\endhead
\textbf{Algorithm} & \textbf{Precision} & \textbf{Recall} &
\textbf{F1-Score}\tabularnewline
\textbf{Logistic Regression} & 0.76 & 0.96 & 0.85\tabularnewline
\textbf{Gradient Descent} & 0.75 & 0.96 & 0.84\tabularnewline
\textbf{Decision Tree} & 1.0 & 0.72 & 0.84\tabularnewline
\textbf{Random Forest} & 0.96 & 0.80 & 0.87\tabularnewline
\bottomrule
\end{longtable}

As shown in Table 7, the decision tree classifier had the highest
precision score, which means that the decision tree model did an
excellent job predicting the fraudulent transactions. Note from Figure 5
that the type of transfer was the feature selected to split the tree. If
the amount laundered is [?]0.5, the tree splits at the amount that was
cashed out, and if the amount is [?] 0.5, the tree splits at the amount
the recipient had after the laundered transaction (newbalanceDest). The
`type of transfer' is an important feature in classifying a money
laundering transaction because it can show how sophisticated or
structured the laundering process is. For example, a cash-out is a
transfer with an influx of money into one account and then an immediate
withdrawal of those funds. This type of laundering is generally
associated with low-level or first-time offenders.

In contrast, a structured deposit is when funds are gradually deposited
into an account over time before being withdrawn. This type of
laundering is generally associated with more knowledgeable or
experienced offenders with access to multiple accounts. The decision
tree correctly classified 84\% of all cash-outs as money laundering
transactions and 100\% of all structured deposits as money laundering
transactions. These results indicate that the decision tree is an
effective classifier for identifying money laundering activities. Both
the accuracy scores for cash-outs and structured deposits are high,
suggesting that the decision tree could be improved by adding more
information, such as the customer's location or account history.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 5~:} Decision Tree Classifications Performance

\textbf{The ROC Curve}

The ROC curve is a commonly used metric for evaluating the performance
of classification models, mainly when dealing with imbalanced
datasets\textsuperscript{49}. As seen in Figure 6, the AUROC curve plots
the true positive rate (TPR) against the false positive rate (FPR) at
various threshold values and provides a visual representation of the
model's accuracy. The decision tree model was the best performing
classifier (.95), followed by the logistic regression model (.93). The
random forest and gradient descent classifiers had the lowest AUC score
of .92. These findings suggests that, while all four models are
effective at predicting laundered transactions, the decision tree model
is the most accurate. Furthermore, the logistic regression model is a
close second, making it a good choice for businesses that require a more
interpretable model. Finally, the random forest and gradient descent
models are still effective predictors of fraud, but they are not as
accurate as the other two models.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Figure 6~:} Results from the ROC Curve

\textbf{Feature Importance}

The features with the highest p-values in the baseline logistic
regression model were amount, oldbalanceDest, newbalanceDest, cash-out,
and transfer. These features were compared with the features from the
ensemble classifiers to examine which ones contributed more to
predicting laundered transactions. As shown in Figure 7, the amount
involved in the transfer was again the top feature to predict suspicious
transactions in mobile money transfers. These findings suggest that the
amount of a transaction is a good predictor of whether a transaction is
suspicious or not. The other features with~\emph{p} -values
\textgreater{} 0.05 did not contribute much to predicting suspicious
transactions. Altogether, mobile money transfer providers need to be
aware of transfers involving large amounts of money and those made
frequently or without a specific destination \textsuperscript{30}. By
considering these factors, mobile money providers can detect and prevent
suspicious behaviour on their platforms.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{Figure 7:} Feature Relevance

\textbf{Discussion and Conclusion}

The popularity of mobile money transfer services has grown rapidly in
recent years, driven by the increasing penetration of mobile devices and
the widespread adoption of mobile banking services. However, using
mobile money transfer services has also created new opportunities for
criminals to launder money. To provide more insights into this problem,
this study employs ML classifiers to predict mobile money transfer
laundering transactions \textsuperscript{32,40}. While all the
classifiers were very useful in predicting suspicion transactions, the
random forest model was the most consistent and best-performing model
across all the classifiers \textsuperscript{41}. The random forest model
is a powerful ML technique for fraud detection because it can
effectively learn from data with many features and can be tuned to
achieve high precision (.96), recall (.80), and F1-score (.87). The
findings from this study provide useful intelligence for mobile money
service providers and law enforcement agencies to fight against money
laundering.

Given the current global landscape, it is not surprising that the use of
mobile money has increased dramatically in the past few
years\textsuperscript{53}. For consumers and businesses alike, the
convenience and flexibility of mobile wallets have made them a popular
choice \textsuperscript{16}. However, the rise of mobile money has also
attracted the attention of criminals looking for new ways to launder
their ill-gotten gains \textsuperscript{1,18,32}. In many cases, mobile
money services are being used to facilitate money laundering by allowing
criminals to quickly and easily move large amounts of cash without
raising suspicion. For example, recent studies found that a significant
proportion of mobile money users have been involved in a transaction
that could be considered suspicious \textsuperscript{8,14,44,45}.
Considering these findings, more must be done to prevent mobile money
from being used for illegal purposes. While mobile money has brought
many benefits to the global economy, it is important to remember that
those with criminal intent can also exploit its services.

Financial institutions have constantly been pressured to prevent money
laundering and terrorist financing by implementing stringent compliance
measures \textsuperscript{12,53}. The mobile money ecosystem has only
added to this pressure, as financial institutions must now also contend
with the challenges posed by digital transactions. In response, there
has been a shift in emphasis from traditional, deterministic rules-based
methodologies toward more sophisticated computational techniques. This
shift is primarily because the sheer volume of transaction data makes it
difficult to flag and detect suspicious activities using rules-based
approaches. Computational techniques in the form of ML offer a more
effective way to monitor suspicious behaviour, as they can consider a
broader range of factors and larger datasets. Adopting ML techniques for
money laundering detection is a promising development in the fight
against illicit activities. These techniques could make catching people
trying to launder money through criminal networks much more accessible.
They could also help mobile money service providers and law enforcement
agencies stay one step ahead of individuals trying to launder illicit
funds.

\textbf{Limitations and Future Research}

While ML is a valuable tool for fraud detection, several limitations
must be considered when conducting research in this area. First, the
data sets used to train the algorithms may not represent the population
of interest, leading to inaccurate predictions. Second, ML models can be
biased based on previous studies that used flawed methodology. Third, it
is important to remember that ML is only one tool for detecting fraud;
other methods, such as human intelligence and expert analysis, may be
more effective in certain situations. Despite these limitations, ML is a
powerful tool that significantly impacts fraud research. With continued
advances in this area, even more, progress will likely be made in using
ML algorithms to fight against fraud. Future research can employ ML to
identify behaviour patterns that may indicate fraudulent activity, while
human intelligence can provide context and insights that an automated
system may miss. By combining these two approaches, future research can
significantly improve the fight against fraud.

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