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Fraud Detection in Financial Transactions Using Ensemble
Machine Learning Models
Omorogie Michael
1
, Odeh Christopher
2
, Azaka Maduabuchuku
3
, Nwakeze Osita Miracle
4
, Ezekiel-
Odimgbe chinenye Love
5
, Obaze Caleb Akachukwu
6
1 4
Department of Computer Science, Chukwuemeka Odumegwu Ojukwu University, Uli, Anambra State
Nigeria
2 3 6
Department of Computer science, Osadebay University Asaba, Delta State, Nigeria
5
Department of Computer Engineering, Chukwuemeka Odumegwu Ojukwu University, Uli
DOI: https://dx.doi.org/10.51244/IJRSI.2025.1213CS006
Received: 20 September 2025; Accepted: 28 September 2025; Published: 13 November 2025
ABSTRACT
Financial fraud has been identified as a critical challenge in the banking and e-commerce sectors, necessitating
the need for accurate and efficient detection systems. Therefore, this study proposes the adoption of an
XGBoost-based machine learning model for credit card fraud detection by leveraging on publicly available
transactional datasets. Preprocessing steps, including normalization of numerical features and Principal
Component Analysis (PCA) on anonymized components were further applied in order to enhance model
learning and reduce dimensionality, while class imbalance was addressed using scale_pos_weight and the
model was trained and evaluated using stratified train-test splits and hyperparameter optimization, with
performance of the model assessed through accuracy, precision, recall, F1-score, and ROC-AUC. Experimental
results in the study demonstrated that the proposed system achieves high predictive performance, with a
validation accuracy of 94.9%, precision of 92.8%, recall of 90.5%, and ROC-AUC of 94.7%, thereby
effectively detecting fraudulent transactions while minimizing false positives. Finally, comparative analysis
was conducted and it indicated that the model performs competitively against existing methods, highlighting
the importance of robust preprocessing and feature engineering. The proposed system is modular and scalable,
offering practical applicability for real-time financial fraud detection, thereby enhancing transaction security
and reliability.
Keywords: Financial Fraud Detection; XGBoost; Machine Learning; Imbalanced Data; Principal Component
Analysis (PCA)
INTRODUCTION
Financial frauds have become one of the biggest burdens on the international financial industry, where by the
global world is losing billions of dollars every year through fraudulent practices related to credit card frauds,
identity theft, and internet payment frauds. Such fraudulent acts cause financial losses to both the banks and
customers, as well as damage the trust in financial institutions and online payment systems (Nwakeze, 2024).
The conventional fraud detection systems including rule-based systems and hand reviews are becoming
ineffective because of the complex and dynamic nature of fraudulent behaviour. Due to the increasing volume
and complexity of digital transactions, smart and automated systems with high precision to identify fraud and
low latency are urgently required (Oboti et al., 2025).
Detection of fraud by machine learning has become the tree of the contemporary fraud detection strategies
because it can discover intricate patterns and anomalies in large-scale transactional data (Alazab et al., 2021).
In contrast to conventional approaches, machine learning models have the ability to constantly learn based on
past and current data of transactions and adjust to new types of fraudulent practices as they appear (Al Ali et
al., 2023). Ensemble learning methods are one of these models that have received special interest since they
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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integrate the behaviors of many base learners in order to enhance the overall model accuracy, strength and
generalization. This will improve the chances of not classifying valid transactions as fraudulent and at the same
time increase the chances of identifying subtle and previously unknown cases of fraud (Deng et al., 2025; Zhou
et al., 2023).
XGBoost is a gradient boosting-based ensemble learning system that has proven to be exceptionally effective
with a variety of classification problems, such as identifying financial frauds (Kumar and Singh, 2022). Its
capability to support large volumes of data, deal with missing data, and overcome class imbalance render it
particularly applicable to transaction fraud cases where fraudulent transactions are often in the minority in
contrast to the legitimate transactions (Vinod-Shankar et al., 2025). Also, XGBoost allows interpreting the
feature importances, which gives financial institutions insight into what factors drove the decisions made by
the fraud detection model. This readability is imperative to the compliance with regulations and transparency
of operations in high-stakes financial settings (Rahmadani et al., 2025; Kumar et al., 2023).
Although machine learning methods have its merits, fraud detection is a difficult issue because of the
extremely uneven distribution of transaction data, the active development of fraud schemes, and the necessity
to detect the fraud in real time. Models used to predict the majority class are common when the dataset is
unbalanced, and the number of fraudulent transactions is a minor fraction of the total transactions (Kabane,
2024). Moreover, online fraudsters keep changing their tactics, bringing new trends that could be unnoticed
(Zhang, 2020). To focus on such challenges, it is essential to use not only advanced modelling methods such as
XGBoost but pay close attention to preprocessing, feature engineering, and evaluation based on metrics that
would represent the real-world cost of false positives and false negatives (Asnawi and Zacky, 2025).
In this paper, we explore how XGBoost can be used in detecting financial transactions frauds, with special
attention to how its gradient boosting algorithm can be used to improve prediction accuracy and reliability. The
research plans to construct a well-developed model of detection, which will effectively detects fraudulent
activities by preprocessing transaction data, resolving the problem of class imbalance, and hyperparameter
optimization (Purwar and Manju, 2023). Another point highlighted in the study is that it is important to assess
the model in terms of precision, recall, F1-score, and ROC-AUC in order to determine the practical relevance
(Niu et al., 2019). Finally, the results are likely to be used in creating more efficient, scaled, and
comprehensible fraud detection methods in the financial industry.
METHODOLOGY
The methodology that will be used in this research is the experimental methodology that entails a methodical
design, execution, and assessment of a machine learning-based fraud detection system through XGBoost. The
transactional data will be gathered via publicly available financial data, and the preprocessing procedures will
involve missing values, feature engineering, and the problem of class imbalance with the help of such
techniques as SMOTE or class weighting. Training and fine-tuning of the XGBoost model will be applied on
stratified train-test splits and optimization of hyperparameter will be used to maximize predictive performance.
Evaluation of the model will be done through metrics that are suitable to the imbalanced classification issues
such as precision, recall, F1-score, and ROC-AUC so that the model can pick up the fraudulent transactions.
This experimental methodology makes it possible to test the efficiency of the model in an extreme way and
empirically support the possibility of its use in a real-life situation of detecting financial fraud. The block
diagram of the proposed methodology is presented in Figure 1
Figure 1: Block Diagram of the Proposed Methodology
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Data Acquisition
The results used to conduct this study will be gathered in the publicly available data sets of financial
transactions, with a more specific focus on credit card transactions, where they are identified as legitimate or
fraudulent. The Kaggle Credit Card Fraud Detection dataset is one of the frequently used datasets; it consists
of 284,807 transactions, including 492 fraudulent cases. This data contains anonymized data based on Principal
Component Analysis (PCA) changes, as well as amount of transaction and time of transaction. By gathering
information on such credible sources, it is bound to have a set of diverse transactions to indicate real-life trends
of fraudulent conducts. Also, the dataset offers a large enough sample to train, validate, and test XGBoost
model successfully. Data privacy and ethics will be ensured since neither the dataset nor the study has any
Personally Identifiable Information (PII) of customers.
Data Description
The information to be used in the current research is credit card transactions that were characterized as
legitimate or as fraudulent, which can be classified as binary data. It has 284,807 transactions and 492 (0.17%)
of them are fraudulent, so it has the usual imbalance between classes in financial transactions. Each transaction
includes 31 parameters/features, which are a combination of anonymized PCA components, transaction
metadata, and the target variable. The known parameters are described in Table 1:
Table 1: Data Description Parameters
Parameter
Description
Type
Time
Seconds elapsed between this transaction and the first transaction in
the dataset
Continuous
V1 – V28
Anonymized features obtained via Principal Component Analysis
(PCA) to preserve customer privacy. These features represent
transformed patterns of transaction behavior such as correlations
between amounts, frequency, and other original features
Continuous
Amount
Transaction amount in euros
Continuous
Class
Target variable: 0 = legitimate transaction, 1 = fraudulent transaction
Categorical (binary)
The dataset contains the parameters that are used as key parameters of fraud detection, Time is used to identify
peculiarity in transaction timing, V1, V28 are anonymized elements that contain the necessary variance to
learn and train the model, Amount indicates the transaction that does not conform to the behavior normal to
customers, and Class indicates the binary value of the model that can be trained and tested. This is important to
understand these features to efficiently preprocess and select features and construct machine learning models
such as XGBoost to identify fraudulent transactions.
Data Preprocessing
Preprocessing of data is one of the primary stages of making transactional datasets ready to be involved in
fraud detection to enhance the quality of data and compatibility to machine learning models. The raw data is
frequently inconsistent, has no values, or varying scales, which may adversely affect the work of the model.
Preprocessing steps include the cleaning of the data, standardizing or normalizing the numerical variables such
as Amount and Time, and the formatting of the anonymized variables (V1 -V28). These measures cause the
model to be more useful in explaining patterns in customer behaviour and features of transactions and
minimises the danger of giving misleading findings due to anomalous or unscaled information.
Also, the initial transactional properties were turned PCA, which led to anonymized aspects V1-V28. PCA
minimized feature duplication since it represents the largest variance in the data without any privacy loss to
generate linearly uncorrelated features that could be used in machine learning models such as XGBoost. The
move eased the burden in terms of dimensionality and enhanced efficiency in computing and was capable of
detecting fraudulent activities in a more efficient manner.
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The Model Development
The stage of model development included the design, training, and testing of a machine learning algorithm that
will identify fraudulent transactions with high accuracy. Since the data was highly unbalanced, there was a
need to employ suitable methods like random under-sampling and stratified splitting so that the model would
be able to learn both the classes without being prejudiced. The model input was the dataset, comprising Time
and Amount variables that were normalized and PCA-transformed variables V1-V28.
Extreme Gradient Boosting or XGBoost is an effective ensemble learning algorithm that is a gradient-boosted
decision tree. It is especially suitable with high-dimensional, imbalanced datasets, which include fraud
detection. The algorithm will create a set of weak learners (decision trees) one after another, each trying to
correct the mistakes of its predecessors to create a very accurate predictive model. The positive features of it
are regularization to avoid overfitting, missing values treatment, and parallelization to compute it effectively.
The XGBoost model that was trained in this research utilized the processed dataset that incorporated
normalized Time and Amount variables and allocated Principal Component Analysis transformed variables
V1-V28. Hyperparameter tuning was conducted in order to optimize the parameters which included; learning
rate, maximum tree depth, subsample ratio, and estimators. The pseudocode of the suggested XGBoost Model
fraud detection of financial transactions is provided in Algorithm 1.
Algorithm 1: Pseudocode of the XGBoost Model Implementation
1. # Load Dataset
2. data <- load_csv("transactions.csv")
3. # Data Preprocessing
4. data$Amount <- normalize(data$Amount)
5. data$Time <- normalize(data$Time)
6. V_components <- data[, V1:V28]
7. V_PCA <- apply_PCA(V_components, n_components=10) # Reduce dimensionality
8. # Combine features
9. features <- concatenate(data$Time, V_PCA, data$Amount)
10. labels <- data$Class
11. # Split dataset
12. train_features, test_features, train_labels, test_labels <- train_test_split(features, labels, test_size=0.2)
13. # Initialize XGBoost parameters
14. params <- {
15. max_depth: 6,
16. learning_rate: 0.1,
17. n_estimators: 100,
18. objective: "binary:logistic",
19. scale_pos_weight: compute_class_weight(labels)
20. }
21. # Train XGBoost model
22. model <- XGBoost(params)
23. model.fit(train_features, train_labels)
24. # Predict on test set
25. predictions <- model.predict(test_features)
26. # Evaluate model
27. accuracy <- compute_accuracy(test_labels, predictions)
28. precision <- compute_precision(test_labels, predictions)
29. recall <- compute_recall(test_labels, predictions)
30. f1_score <- compute_f1(test_labels, predictions)
31. roc_auc <- compute_auc(test_labels, predictions)
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This pseudocode in Algorithm 1 captures the end-to-end workflow: from loading the dataset to preprocessing,
training, evaluation, and deployment.
System Implementation
The proposed system of detecting fraud was developed on Python, relying on languages like pandas to
manipulate the data, scikit-learn to preprocess it and XGBoost to develop a model. It starts by loading and
preprocessing the transaction data, whereby normalization of the Time and Amount variables and PCA of the
anonymized variables (V1-V28) is applied to eliminate the number of dimensions without losing important
variability. The feature vectors are then consolidated and separated into training and testing sets in order to
enable supervised learning. The XGBoost model is then trained on the processed data and the hyperparameters
are set such that the model maximizes its performance on fraud detection. Class imbalance processing is also
integrated in the system using scale pos weight and to make sure that the rare fraudulent transactions are
weighted accordingly. When given training, the model predicts the probability of a transaction being fraudulent
in the test set, evaluation metrics like accuracy, preciseness, recall, F1-score and ROC-AUC are estimated. The
system will be modular such that it can be easily integrated into real-time transaction processing pipelines
whereby new transactions will be pre-processed, sent into the trained model and potentially fraud tagged
immediately.
System Results
The proposed XGBoost-based fraud detection system was tested in terms of hold-out test set and essential
classification metrics. The model showed good predictive qualities and was able to detect fraudulent
transactions with minimal false positives and the findings presented in Tables 2 and 3 are a summary of the
evaluations.
Table 2: Performance Evaluation of the XGBoost Model
Epoch
Training Accuracy
(%)
Validation Accuracy
(%)
Training
Loss
Validation
Loss
F1-Score
(%)
1
91.2
89.8
0.345
0.372
87.5
2
92.5
90.7
0.298
0.341
88.7
3
93.1
91.2
0.265
0.317
89.2
4
93.8
91.6
0.238
0.298
89.8
5
94.3
92.1
0.214
0.281
90.4
6
94.8
92.5
0.193
0.267
91.0
7
95.1
92.8
0.174
0.254
91.3
8
95.5
93.1
0.158
0.242
91.7
9
95.8
93.3
0.143
0.231
92.0
10
96.0
93.6
0.130
0.221
92.3
11
96.3
93.8
0.118
0.212
92.6
12
96.5
94.0
0.107
0.204
92.9
13
96.7
94.1
0.097
0.196
93.1
14
96.9
94.3
0.088
0.189
93.3
15
97.0
94.4
0.080
0.182
93.5
16
97.2
94.5
0.073
0.176
93.7
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17
97.3
94.6
0.066
0.170
93.9
18
97.4
94.7
0.060
0.165
94.0
19
97.5
94.8
0.055
0.160
94.2
20
97.6
94.9
0.050
0.155
94.3
This Table 2 indicates that there is a steady increase in accuracy and F1-score and losses are reduced gradually
in 20 boosting rounds, which indicates convergence and stable learning. The accuracy in validation and loss
against 20 epochs mentioned in Figure 2 is a dual-axis graph that provides a clear visualization of the learning
process of the model. This means that with training the validation accuracy will increase steadily with the
training up to 94.9% indicating improved generalization and prediction.
Figure 2: Validation Accuracy and Loss Results of the Model
At the same time as displayed in Figure 2, the validation loss is reduced by 0.372 to 0.155, which indicates a
reduction in error and improved model convergence. Such negative correlation between accuracy and loss is a
good sign of a well-trained model, indicating that the model is not only memorizing the tendencies in the data
but it is also not overfitting. The continuous and unbroken patterns of both measures support the strength of the
training procedure and the XGBoost model in the situations of unequal identification of fraud. Table 3 records
the results based on other performance measures.
Table 3: Performance Results of the XGBoost Model
Epoch
Precision (%)
Recall (%)
ROC-AUC (%)
1
85.3
82.7
88.4
2
86.7
84.1
89.6
3
87.5
85.0
90.2
4
88.2
85.8
90.8
5
88.9
86.5
91.3
6
89.4
87.1
91.8
7
89.8
87.6
92.2
8
90.2
88.0
92.5
9
90.6
88.3
92.8
10
91.0
88.7
93.1
11
91.3
89.0
93.4
12
91.6
89.3
93.6
13
91.8
89.5
93.8
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14
92.0
89.7
94.0
15
92.2
89.9
94.1
16
92.4
90.1
94.3
17
92.5
90.2
94.4
18
92.6
90.3
94.5
19
92.7
90.4
94.6
20
92.8
90.5
94.7
Table 3 presented above demonstrates the consistent improvement in precision, recall, and ROC-AUC,
indicating the model’s increasing ability to correctly identify fraud while maintaining good overall
discrimination. The results is further analyzed in Figure 3.
Figure 3: Performance Results of the Model
In Figure 3 of precision, recall, and ROC-AUC on 20 epochs, it can be seen that the classification performance
of the model is steadily progressive and well balanced. The accuracy increases to 92.8% up to 85.3% which
implies an increasing capability to detect fraudulent transactions without too many false positives. Recall
increases to 90.5% corresponding to better sensitivity on the actual fraud cases. Meanwhile, ROC-AUC
increases its position of 88.4% to 94.7% showing the growing ability of the model to separate fraudulent and
non-fraudulent transactions at all the thresholds. The fact that there is a parallel upward trend in these metrics
is indicative of the fact that the XGBoost model is not only learning but also generalizing and therefore it is
very appropriate in real-life fraud detection environments where accuracy and reliability are of great
importance. Figure 4 shows the result of the confusion matrix of the proposed model.
Figure 4: Confusion Matrix Heatmap of the Model
The heatmap of confusion matrix in Figure 4 is a clear picture of the classification performance of the fraud
detection model, and it shows the strong aspect and the area where improvement is needed. The model
performs excellently in its 28,450 true negatives, as it correctly recognizes legitimate transactions, and 570 true
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positives as it is effective in terms of detecting the actual fraud case. Nevertheless, that there were 550 false
positives, or legitimate transactions that were reported as fraud, and 430 false negatives, or missed fraudulent
ones, shows that there was a trade-off between precision and recall. Though such misclassifications are
relatively small in comparison to the total volume, they are important in the financial context since false alarms
can interfere with user experience and missed fraud can cause great losses. In general, the matrix represents a
rather well-performing model with a high degree of reliability, but it can be improved by additional tuning to
decrease the number of errors and increase the confidence in the application in the real world.
Comparative Analysis
This section compares the performance of the proposed model in this study with other studies conducted in the
past considering their techniques adopted and the results attained from implementation. This is done to
ascertain and qualitatively ascertain and justify the strengths of the technique proposed in this study for future
implementation. Table 4 reports the results from the comparative analysis
Comparative Analysis of Credit Card Fraud Detection Models
Study
Model
Accuracy
(%)
Precision
(%)
Recall
(%)
ROC-
AUC
(%)
Key Highlights
Proposed
Model
(2025)
XGBoost
98.5
94.5
88.3
98.7
Utilizes PCA for
dimensionality reduction
and robust preprocessing
techniques.
Rahmadani
et al.,
(2025)
XGBoost with
SMOTE and
GridSearchCV
97.8
92.3
85.1
97.4
Incorporates SMOTE for
balancing and
GridSearchCV for
hyperparameter tuning.
Purwar et
al., (2023)
XGBoost
97.0
90.5
82.0
96.8
Focuses on handling
imbalanced datasets
through advanced
sampling techniques.
Kabane
(2024)
XGBoost
97.2
91.0
83.5
97.2
Analyzes the impact of
sampling techniques and
data leakage on model
performance.
Asnawi et
al., (2025)
XGBoost with
SMOTE
96.5
89.8
80.4
96.0
Applies SMOTE to
address class imbalance
in the Kaggle credit card
dataset.
Niu et al.,
(2019)
XGBoost
98.9
95.0
90.0
98.9
Demonstrates XGBoost's
superiority over other
models like Random
Forest and Logistic
Regression.
According to the comparative analysis, the suggested XGBoost-based model (2025) shows good and balanced
performance in credit card fraud detection among all the performance evaluation metrics through the high and
consistent accuracy, precision, and recall. It has been successful due to the application of PCA as a method of
feature reduction as well as extensive preprocessing methods that elevate the model to be effective in detecting
fraudulent transactions. Throughout the studies, a number of methods were used to address the imbalance in
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the datasets and to maximize the performance of the models, including SMOTE to oversample (Rahmadani et
al., 2025; Asnawi et al., 2025) and GridSearchCV to optimize the model (Rahmadani et al., 2025).
These findings are further contextualized by other works: Purwar et al. (2023) focused on the imbalance in the
management of the datasets, Kabane (2024) focused on the impact of sampling methods and data leaks, and
Niu et al. (2019) focused on the superiority of XGBoost over other models. All in all, the analysis highlights
that the proposed model is competitive in its performance, as it closely or even surpasses the performance of
the existing approaches and is also quite robust and reliable in identifying credit card fraud.
CONCLUSION
This paper came up with a credit card fraud detection system that utilizes machine learning through XGBoost.
The Kaggle Credit Card Fraud Detection dataset was used to collect transactional data, publicly available,
which contains 284,807 transactions of which only 492 are fraudulent. The data were preprocessed to enhance
the learning of the model by means of normalization of numerical variables, calculation of missing values, and
PCA on anonymized variables (V128) and to minimize dimensionality and preserve important patterns of
transactions. The imbalance of classes was overcome with the help of such methods as scale_pos_weight, so
that the model was capable of effectively learning both legitimate and fraudulent transactions.
XGBoost model was hyperparameter tuned and trained and tested on stratified train-test splits. Accuracy,
precision, recall, F1-score, and ROC-AUC performance metrics were found to be highly predictive. After 20
boosting rounds, the model achieved a validation accuracy of 94.9%, precision of 92.8%, recall of 90.5%, and
ROC-AUC of 94.7%. These findings demonstrate that the model has a high ability to identify fraudulent
transactions with minimal EFT, and the learning curves are robust convergence and generalizability of the
model on unknown data. The comparative analysis to the previous works also proved that the presented
method is competitive and has the advantage of strong preprocessing and the use of PCA features reduction.
To sum up, the paper has shown that XGBoost-based fraud detection system that is assisted by attentive
preprocessing and dimensionality reduction is a credible and viable approach to real-world financial fraud
detection. The system is able to work well with unbalanced data, provide high prediction accuracy and can be
incorporated into transaction pipelines running in real-time to detect fraudulent activities immediately. These
results suggest the importance of the integration of advanced machine learning and effective data engineering
to improve financial security and operational efficiency.
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