Real‑Time Credit Card Fraud Detection Using Optimized XGBoost

with Intelligent Pattern Adaptation

P. U. Anitha

, N. Sowmiya

, P. Mathiyalagan

, V. Padmapriya

B. Veera Sekharreddy

and Bala Murugan M.

Department of CSE, Christu Jyothi institute of Technology and Science, Jangaon District, Telangana‑506 167, India

Department of Electronics and Communication Engineering, Surya Engineering College, Erode, Tamil Nadu, India

Department of Mechanical Engineering, J.J. College of Engineering and Technology, Tiruchirappalli, Tamil Nadu, India

Department of CSE, Nandha College of Technology, Erode, Perundurai, Tamil Nadu, India

Department of Information Technology, MLR Institute of Technology, Hyderabad, Telangana, India

Department of MCA, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

Keywords: XGBoost, Fraud Detection, Real‑Time Analytics, Adaptive Model, Credit Card Transactions.

Abstract: A realtime fraud detection system is proposed by applying a tuned XGBoost model in high-frequency credit

card transactions. The model can dynamically adjust to changing fraud activities with the help of dynamic

feature selection and threshold adjusting mechanism. A thorough evaluation on benchmark datasets

demonstrates its better detection accuracy, less false positives, and faster decision-making performance than

classical ensemble and deep learning methods. The system has also interpretability capabilities that can

improve transparency and trust of automated systems for financial decisions, making the system feasible for

deployment at scale in real-world financial infrastructure.

1 INTRODUCTION

The increase in online financial transactions has also

led to a significant rise in the risk of credit card fraud,

so the need for effective and efficient detection

systems becomes more urgent. With the

advancement of fraudulent activities, the traditional

rule-based and static machine learning methods easily

weaken in combating with dynamic fraud trends. To

the threat of the changing landscape, advanced

ensemble models like XGBoost, have become

increasingly popular for managing large, high-

dimensional data easily and effectively. It is worth

noting that the XGBoost (with gradient boosting) not

only improves prediction accuracy, but it also allows

for real-time tuning and adaptation. Based on

effective learning from imbalanced classes and

focusing on feature importance, models built on

XGBoost form an inherently solid structure for

proactive detecting the frauds. This work

investigates the design of an intelligent, on-the-fly,

fraud prevention system capitalizing on the

advantages of XGBoost, but also considering the

operational difficulties of delay, interpretability and

adaptability for financial transaction monitoring.

2 PROBLEM STATEMENT

Traditional fraud detection systems cannot keep pace

with the increasing diversity and prevalence of credit

card fraud. These detection systems commonly

produce high-levels of false positives, have late

responses and possess a lack of adaptability to new

and changing indices of fraudulent activities. The

real-time classification is a challenging issue for the

existing machine learning models and they often do

not make a good trade-off between the detection

accuracy and the processing speed, especially in the

case of imbalanced datasets. There is a demand of a

fast, optimal, and interpretable model to detect and

prevent the fraudulent transactions in online

payment system. This study seeks to bridge these

gaps, constructing a real- time framework for credit

card fraud detection with a state-of-art XGBoost

model to allocate fraud strategies, which considers

608

Anitha, P. U., Sowmiya, N., Mathiyalagan, P., Padmapriya, V., Sekharreddy, B. V. and M., B. M.

Real-Time Credit Card Fraud Detection Using Optimized XGBoost with Intelligent Pattern Adaptation.

DOI: 10.5220/0013870200004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 1, pages

608-614

ISBN: 978-989-758-777-1

new fraud strategies when maintaining

competitiveness in terms of accuracy rate and low

latency in identifying decisions.

3 LITERATURE SURVEY

Machine learning has brought great strides in the

development of the credit card fraud detection

systems. Kandi and García-Dopico (2025) have

Discussed the fact that the combination of LSTM

with XGBoost for fraud detection can be beneficial,

but it is challehing in terms of computation. Tayebi

and El Kafhali, 2025) is an autoencoder based model

using pseudorandom patterns, but they observed that

their proposed models fail to detect mimicry attacks

and provide room for hybrid solutions. Mim et al.

(2024) proposed a soft voting ensemble, which

improved classification but decreased the

interpretability of the decision. Chu et al. (2023) used

ensemble models on the original European cardholder

data and found real-life evidence that limitations of

the dataset impede its validity.

Li et al. (2023) introduced the fraud policy by

reinforcement learning, stressing flexibility yet

mentioning its training cost. Li, Xu, Wu, and Zhang

(2023) examined collaborative schemes which

enhanced learning, but made privacy issues possible.

Zhang et al. (2023) considered interpretable deep

learning approaches to avoid the explainability

problem, echoing the medical needs. Liu et al. (2023)

presented federated learning to enhanced data

privacy across institutes, struggled with varied model

performance.

Wu, Li and Zhou (2022) used VAE for anomaly

detection, which suffered from the overfitting on the

unbalanced data. Li et al. (2022) applied GNNs for

transaction relationship mapping, but encountered

processing inefficiency. Zhang et al. (2022) added

blockchain to better secure and transact the

dissemination, however, its real-time applicability

was unclear. Xu et al. (2022) also introduced human

expert prior into machine learning for providing

baseline knowledge, but manual interventions made

it very difficult to adapt according to the case.

Zhang et al. (2021) studied complex deep

reinforcement learning models that were found to be

promising yet computationally expensive. Chen et al.

(2021) proposed transfer learning approaches that

helped generalize more expressions but with the

necessity of source-target data alignment. Xu et al.

(2021) proposed an explainable AI framework

specific to finance, and Wang et al. (2021) focused on

dynamic model updates and suffered from

distributed synchronization challenges.

Chen et al. (2020) applied hybrid evolutionary

algorithms to the selection process, which provides

optimization of hyperparameter with the price of

convergence rate for training. Bhattacharya et al.

(2020): balanced the classes with synthesized

sampling, however the introduction of noise proved

to be difficult. Zhang et al. (2019) developed a hybrid

rule and machine learning-based system, but its

precision was high and adaptation was low. Lastly,

Smith et al. (2010) described legacy knowledge-

based systems that provided fundamental

benchmarking but seemingly could not adapt to

current fraud threats.

This aggregate research indicates the need for a

coherent model that considers accuracy,

interpretability, real-time response and adaptability

variables which the proposed XGBoost-based system

looks to incorporate and improve.

4 METHODOLOGY

An Intelligent On-Line Fraud Detection Based on the

XGBoost Algorithm for Dynamic Credit Card

Transactions Proposed approach is an intelligent

real-time fraud detection concept using the XGBoost

algorithm for dynamic credit card transactions. This

approach is organized such that prompt

identification, precision and progressive learning are

its integral elements to keep the pace with ever-

changing fraudulent tactics. In general, the entire

system consists of a set of housed phases including:

Data-preprocessing, Feature-engineering, Imbalance-

handling, Model-training, Hyperparameter-

optimization, Real-time-deployment with continuous

learning.

First, transactional datasets are obtained from

trusted, actual sources of the real world, which

include real and fake records. These data are

frequently characterized by a very heavy class

imbalance, counterfeit transactions are, in fact, a

small percentage. Preprocessing: The data is

subjected to preprocessing and unnecessary fields are

removed, null values are being managed, categorical

variables are being encoded and continuous

variables are being normalized. Features in the time

dimension also engineered, such as transaction

volume per user, merchant risk scores, and

transaction velocity are used as contextually relevant

inputs to the model.

Figure 1 shows the Real-Time

Credit Card Fraud Detection Workflow using

XGBoost.

Real-Time Credit Card Fraud Detection Using Optimized XGBoost with Intelligent Pattern Adaptation

609

Once the data are balanced and the features are

chosen, the XGBoost model is fitted with the gradient

boosting decision tree model. The model is adjusted

with an objective function to minimize the log loss,

which is suitable for binary classification problem

such as fraud detection. We proceed with 5-fold

cross-validation in order to avoid model instability

and overfitting. The number of early stopping

rounds is used to stop the training if the performance

on the validation data gets saturated. Throughout the

training, the server continuously updates its internal

decision trees by obtaining 1st and 2nd gradient

information, and can learn complex patterns quickly.

Figure 1: Real-time credit card fraud detection workflow

using XGBoost.

The feature selection is an important stage in the

methodology as non-informative or redundant

features may reduce the model performance.

Leveraging XGBoost’s built-in scalability to measure

feature importance with gain, coverage, and

frequency metrics, we partition the highly influential

variables. This process helps in model interpretability

while decreasing training time and risk of overfitting.

Furthermore, the domain knowledge is incorporated

to strengthen the model with better discriminability in

modeling the normal and fraudulent patterns. The

features are then selected and ranked iteratively

using their contribution to improve the performance

in the cross-validation.

Table 1 shows the Cross-

Validation Performance Summary (5-Fold).

Since the data usually is in imbalanced

distribution, the common learning algorithms often

bias towards the majority class, thus causing the low

detecting rate on the minority (fraud) class. To

address this problem, we adopt the technique of

Synthetic Minority Over-sampling Technique

(SMOTE) followed by Tomek links to form a

balanced and clean training dataset. Furthermore,

cost-sensitive learning is integrated in the XGBoost

setting, such that false negatives are punished more

than the false positives, which will help detect the

fraudulent transitions in a better way.

To improve the model performance even more,

Bayesian Optimization is used for hyperparameter

optimization. Hyperparameters such as learning rate,

tree depth, subsample, and minimum child weight

are sequentially tuned for the optimal trade-off

between model complexity and generalisation. This

warm-up stage is crucial to maintain the model

lightweight and efficient for real-time usage, while

not giving away predictive ability.

Table 1: Cross-validation performance summary (5-Fold).

Fold Accuracy (%) Precision (%) Recall (%) F1-Score (%)

1 98.3 90.2 93.5 91.8

2 98.6 91.1 94.0 92.5

3 98.7 91.7 94.3 92.9

4 98.9 92.2 94.7 93.4

5 98.8 91.9 94.2 93.0

Average 98.7 91.4 94.1 92.7

In the production environment, the model is

implemented within a transaction monitoring system

using a scalable API layer. For each incoming

transaction, real-time preprocessing is applied and the

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COMMUNICATION, AND COMPUTING TECHNOLOGIES

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selected features are input to the pre-trained XGBoost

model for fraud prediction. A threshold is used to

decide whether to classify the transaction based on

the model's output probability. If the risk exceeds a

predetermined threshold, the transaction is

temporarily appended, either suspended for review or

undergoes further multi-factor authentication,

according to institutional guidelines.

To keep the efficiency, the system has an

adaptive learning. Iterative retraining is planned with

new transaction data trained with labels based on user

feedback and investigation result. This ongoing

learning loop ensures that the model stays current

with recent fraud while that doesn’t change. There is

also an in-eye feedback engine to track the model

predictions, and system alarms that can be used for

feature drift, model degradation and adapt the

threshold in real-time if necessary.

In addition, SHAP (SHapley Additive

explanations) values are parsed to allow explain

ability of individual prediction decisions. This

interpretability layer is very important in finance

systems, where open books and auditablility is

paramount. For each transaction classified as

fraudulent, the system can produce an understandable

report explaining which factors most influenced the

decision, in support of trust and compliance needs.

Figure 2: Class ratio of before and after SMOTE-Tomek.

On the whole, this approach makes use of XGBoost

for the merits of high-dimensional data process,

feature importance interpretation and real-time

inference, in addition, it also involves method such as

data imbalance, model tuning and real-time

deployment. We have built a system that is able to

detect financial fraud in a robust, scalable and

adaptive manner, one which scales both up and down,

with the ability to adapt to new threats and system

operational requirements that are characteristic of

modern financial systems.

Figure 2 shows the Class

Ratio of Before and After SMOTE-Tomek.

5 RESULT AND DISCUSSION

We tested the XGBoost-based intelligent fraud

detection model in a real credit card transaction data

set, which contains millions of anonymized instances,

quite a few of which are fraud instances. The dataset

was balanced after preprocessing and the resampled

by SMOTE-Tomek to make the class distribution

nearly equal, thus ensuring the model could learn the

fine-grained patterns effectively. The experimental

results confirmed that XGBoost can achieve a

remarkable improvement in accuracy and efficiency

compared to traditional and even some DL-based

models.

Table 2 shows the Model Evaluation Metrics.

Table 2: Model evaluation metrics.

Metric Value (%)

Accuracy 98.7

Precision 91.4

Recall 94.1

F1-Score 92.7

AUC-ROC 99.3

The model attained an average accuracy of 98.7%

along with precision, recall and F1-score of 91.4%,

94.1% and 92.7% respectively. To pick the best

classifier, apart from comparing the accuracy of

several classifiers, we compared various metrics,

among which the AUC-ROC for the two classes

provided for an optimal threshold decision. These

numbers show the excellent performance of the

model in detecting fraudulent activities and reducing

the chance of false positives, a major issue

encountered in fraud detection systems that overflag

legit users and may lead to frustration and harm to

your business.

Figure 3 shows the Performance

Comparison of Models.

Figure 3: Performance comparison of models.

Real-Time Credit Card Fraud Detection Using Optimized XGBoost with Intelligent Pattern Adaptation

611

This including the fact that XGBoost is able to

explain more complex relationships and interactions

of variables than the linear models, helping it to have

better performance. Advanced feature engineering

such as making use of transaction velocity, merchant

category profiling and user spending habits gave rich

contextual signals to the model and made it easy for

the model to pick out the fraudulent patterns. Features

such as fast multiple purchases from various

geographic locations in a short period of time

consistently ranked among the top predictors based

on the model. These observations from SHAP values

improved the model interpretability and trust.

Table

3 shows the Comparison with Other Models.

Table 3: Comparison with other models.

Model Accuracy (%) Recall (%) F1-Score (%) Avg Latency (ms)

Logistic Regression 89.5 72.3 80.1 18

Random Forest 94.2 85.7 89.3 42

LSTM 96.1 90.5 92.2 120

Proposed XGBoost 98.7 94.1 92.7 35

The second important aspect in which the system

has been assessed is the real-time performance.

Average latency for the predictions of the model was

less than 35ms per transaction; thus, making it quite

well-suited for real-time live transaction processing.

After comparing with deep learning models (e.g.,

LSTM or CNN) that usually have much longer

running time and computational cost, XGBoot was a

lightweight model but also competitive in

performance. Considering that decisions need to be

made instantly to avoid losing in high- frequency

transaction atmosphere, rapid response of system is

extremely crucial for the system.

Another aspect of the results was to study how the

system is able to generalize to data drift and new

fraud trends. In a simulation over a three-month long

transaction stream, a stable performance curve

remained stable with only slight degradations in

accuracies and these slight variations were

automatically compensated by periodically

retraining. Such an adaptive feedback loop within the

system brought that false positives and false

negatives were never discarded at each iteration, but

were over the time carried as feedback for the future

training cycle. This active learning way enabled the

model to adapt to new ways of cheating and keep up

with evolving transactional ecosystems.

Table 4

shows the Adaptive Retraining Impact Over Time.

Comparison with other models had shown that the

proposed method was superior. Simple logistic

regression models, although interpretable, did not

have depth to accommodate complicated patterns and

had much lower recall (72.3%). The decision trees

were faster but highly susceptible to overfitting and

gave irregular results for different test samples.

Ensemble techniques, including Random Forest and

Gradient Boosting Machine (GBM), showed better

results, but XGBoost surpassed all, thanks to its

ability for regularization and parallel calculation.

Deep learning techniques, including LSTM, could

learn sequence dependencies but were difficult to

tune and required high computational resources,

making them impractical for real-time field

deployment.

Table 4: Adaptive retraining impact over time.

Retraining

Round

New

Fraud

Detected

Accuracy

(%)

Recall

(%)

Precis

ion

(%)

Initial

Model

492 98.7 94.1 91.4

After 1st

Month

527 98.8 95.0 92.2

After 2nd

Month

560 99.0 95.6 92.7

After 3rd

Month

589 99.1 96.1 93.3

Another point to discuss is the interpretability of

the model. This way, by the help of SHAP

explanations, they could make the system show why

it predicted a transaction as a fraud. This enabled

banks and credit card companies to create

transparent audit logs to help them comply with

regulations like GDPR and PCI-DSS. It also helped

customer support teams troubleshoot false alerts more

effectively by understanding the feature reasons for

model decisions.

Figure 4 shows the Latency

Comparison of Fraud Detection Models.

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COMMUNICATION, AND COMPUTING TECHNOLOGIES

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Figure 4: Latency comparison of fraud detection models.

From a deployment standpoint, we incorporated

the model into a simulated banking transaction

system as RESTful APIs. The transaction records

were sent to the model’s inference engine as they

happen, ran them through the ‘train’d pipeline and

returned a score of how much fraud risk is there. This

risk score was then matched to a series of decision

rules accepting it, passing it to manual review, or

invoking step-up challenge. The system supported

more than 1000 transactions/sec without any

performance bottlenecks which, attests to its

scalability and production-readiness.

Figure 5: Model performance over adaptive retraining.

Finally, the XGBoost fraud detection system

offers a very promising set of features combining

high accuracy, real-time decision-making, model

interpretability, and ability to adapt to new

transactions. It is also made more robust by being

domain-driven as well as its combination of

intelligent resampling and hyperparameter

optimization. Our experiment results demonstrate

that the proposed algorithm surpasses other methods

and is a practical way for us to take care of digital

financial transaction fraud detection in practice.

Figure 5 shows the Model Performance Over

Adaptive Retraining.

6 CONCLUSIONS

This research experimentally proves the effectiveness

of the XGBoost-based intelligent model for online

instantaneous credit card fraud detection in practice.

By utilizing sophisticated feature engineering, data

imbalance, and adaptive learning, the proposed

methodology achieves state-of-the-art accuracy and

efficiency as well as scalability and interpretability.

Moreover, the model is demonstrably superior to

traditional as well as deep learning-based

alternatives in predicting default and thrives in

environments requiring speed and accuracy when

processing transactions in real-time. With the being

able to dynamically adjust to changing fraud

patterns, you can rely on it long-term. The

implementation of SHAP-based interpretability

further enhances its application in regulatory and

operational settings. In summary, the work provides

a holistic and applicable solution to the central

problems witnessed in the recent approaches for the

fraudulent detection systems, and this forms a solid

basis for further improvements on the secure

financial technologies.

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