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

Analysis (PCA)

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).

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).

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.

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

The results used to conduct this study will be gathered in the publicly available data sets of financial transactions,

Data privacy and ethics will be ensured since neither the dataset nor the study has any Personally Identifiable

Information (PII) of customers.

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

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

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.

The stage of model development included the design, training, and testing of a machine learning algorithm that

Extreme Gradient Boosting or XGBoost is an effective ensemble learning algorithm that is a gradient-boosted

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

10. labels <- data$Class

16. learning_rate: 0.1,

18. objective: "binary:logistic",

20. }

24. # Predict on test set

Table 3: Performance Results of the XGBoost Model

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

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 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-

implementation. Table 4 reports the results from the comparative analysis