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TYPE
Original Research
PAGE NO.
141-150
10.37547/tajet/Volume07Issue04-19
OPEN ACCESS
SUBMITED
19 February 2025
ACCEPTED
24 March 2025
PUBLISHED
30 April 2025
VOLUME
Vol.07 Issue 04 2025
CITATION
Mohammad Iftekhar Ayub, Biswanath Bhattacharjee, Pinky Akter,
Mohammad Nasir Uddin, Arun Kumar Gharami, Md Iftakhayrul Islam,
Shaidul Islam Suhan, Md Sayem Khan, & Lisa Chambugong. (2025). Deep
Learning for Real-Time Fraud Detection: Enhancing Credit Card Security in
Banking Systems. The American Journal of Engineering and Technology,
7(04), 141
–
150. https://doi.org/10.37547/tajet/Volume07Issue04-19
COPYRIGHT
© 2025 Original content from this work may be used under the terms
of the creative commons attributes 4.0 License.
Deep Learning for Real-
Time Fraud Detection:
Enhancing Credit Card
Security in Banking
Systems
Mohammad Iftekhar Ayub
Master of Science in Information Technology, Washington University of
Science and Technology, USA.
Biswanath Bhattacharjee
Department of Management Science and Quantitative Methods, Gannon
University, USA.
Pinky Akter
Master Of Science in Information Technology, Washington University of
Science and Technology, USA.
Mohammad Nasir Uddin
Masters of Business Administration, Major in Data Analytics, Westcliff
University, USA.
Arun Kumar Gharami
Master of science in computer science, Westcliff university, USA.
Md Iftakhayrul Islam
MBA in Management Information Systems, International American
University, USA.
Shaidul Islam Suhan
MBA in Business analytics, International American University, USA.
Md Sayem Khan
Master of Science in Project Management, Saint Francis College (SFC),
Brooklyn, New York, USA.
Lisa Chambugong
Department of Management Science and Quantitative Methods, Gannon
University, USA
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Abstract:
In this study, we present a deep learning-
based approach for real-time credit card fraud
detection in banking systems, with a primary focus on
Long Short-Term Memory (LSTM) networks. Using a
highly imbalanced credit card transaction dataset, we
implemented comprehensive preprocessing, feature
engineering, and model evaluation strategies to
enhance the detection accuracy. Our experimental
results reveal that the LSTM model significantly
outperformed traditional machine learning algorithms
such as Logistic Regression, Decision Tree, and Random
Forest. The LSTM achieved an accuracy of 99.38%,
precision of 99.40%, recall of 99.22%, and F1-score of
99.31%, demonstrating its superior capability to detect
fraud while minimizing false positives. Through
comparative analysis, we establish that deep learning
not only improves predictive performance but also
adapts better to temporal patterns inherent in financial
transactions.
This
research
underscores
the
transformative potential of AI-driven fraud detection in
modern banking infrastructures, ensuring enhanced
security, operational efficiency, and customer trust.
Keywords:
Deep Learning, LSTM, Credit Card Fraud
Detection, Banking Systems, Real-Time Detection,
Machine Learning, Financial Security, Fraud Prevention,
Imbalanced Dataset, Artificial Intelligence.
Introduction:
In recent years, the rapid proliferation of
digital banking and e-commerce platforms has
significantly
transformed
the
global
financial
ecosystem.
However,
this
digital
shift
has
simultaneously led to a sharp rise in cybercrimes,
particularly credit card fraud, which remains one of the
most prevalent and costly threats to financial
institutions and consumers alike. According to the
Nilson Report (2022), global losses due to card fraud
surpassed $32 billion, with projections indicating
continued growth. This alarming trend emphasizes the
urgent need for advanced, accurate, and real-time
fraud detection mechanisms to protect sensitive
financial data and maintain customer trust.
Traditional rule-based systems and static statistical
methods, once effective in detecting fraudulent
behavior, are no longer sufficient to cope with the
increasing sophistication of fraud techniques.
Fraudulent transactions are often subtle, complex, and
designed to mimic legitimate patterns, making their
detection a challenging task for conventional
approaches. Furthermore, the highly imbalanced
nature of fraud datasets, where legitimate transactions
vastly outnumber fraudulent ones, presents additional
hurdles in achieving reliable performance.
Machine learning (ML) has emerged as a powerful tool
in this domain, offering the capability to analyze vast
amounts of transaction data and uncover hidden
patterns indicative of fraudulent activity. Nevertheless,
most classical ML algorithms struggle with temporal
dependencies and sequence learning, which are vital in
capturing behavior patterns over time. Deep learning
(DL), particularly architectures such as Long Short-Term
Memory (LSTM) networks, provides a promising
alternative by enabling dynamic, real-time fraud
detection through its sequence modeling capability and
nonlinear feature learning.
In this research, we propose a real-time fraud detection
framework utilizing LSTM-based deep learning
architecture. We aim to build a robust detection system
that can accurately identify fraudulent transactions
while minimizing false alarms, thereby enhancing the
operational efficiency and security of banking systems.
The proposed model is evaluated against several
traditional machine learning algorithms to demonstrate
its superiority in handling complex, high-dimensional,
and imbalanced financial datasets.
LITERATURE REVIEW
Credit card fraud detection has been a critical area of
study in both academic and industrial research,
especially with the advancement of digital banking and
online payment systems. Numerous studies have
explored the use of machine learning and artificial
intelligence for combating this financial threat.
Early approaches relied heavily on statistical methods
and expert-defined rules to identify anomalous
behavior (Chan et al., 1999). While useful in
constrained environments, these methods are static
and often fail to detect new and evolving fraud
patterns. As fraudulent strategies became more
dynamic, machine learning techniques like Decision
Trees, Logistic Regression, and Random Forests gained
popularity due to their ability to learn from data and
adapt to changing trends (Bhattacharyya et al., 2011).
Random Forests and Support Vector Machines (SVMs)
have shown promising results, especially in their ability
to classify rare events in imbalanced datasets (Carcillo
et al., 2018). However, despite their strong
classification capabilities, these models lack the
memory component required for sequence-based data,
which is often the case in transaction streams.
With the emergence of deep learning, researchers
began exploring architectures such as Artificial Neural
Networks (ANNs) and Recurrent Neural Networks
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(RNNs) for fraud detection. LSTM, a variant of RNN
designed to handle long-term dependencies, has
demonstrated superior performance in learning
transaction sequences and identifying suspicious
behavior patterns (Jurgovsky et al., 2018). Its
effectiveness is attributed to its ability to remember
previous inputs and detect temporal irregularities,
which are crucial in uncovering sequential fraud.
Additionally, works by Fiore et al. (2019) and Roy et al.
(2021) have confirmed the benefits of using LSTM-
based architectures over classical machine learning
models in terms of recall and precision. These models
also show significant improvement in minimizing false
positives
—
a critical factor in real-time banking
environments where customer experience must be
preserved.
Furthermore, the use of imbalanced learning strategies
such as Synthetic Minority Oversampling Technique
(SMOTE), cost-sensitive learning, and under-sampling
has been integrated with deep learning to further
improve detection performance (Dal Pozzolo et al.,
2015). These techniques enhance the model’s
capability to learn from rare fraud cases without being
overwhelmed by the majority class.
In conclusion, existing literature validates the potential
of deep learning, particularly LSTM, as an advanced
solution for real-time credit card fraud detection. Our
research
builds
upon
these
foundations by
incorporating
enhanced
preprocessing,
feature
engineering, and comparative analysis with other
machine learning models to establish a robust and
scalable fraud detection framework suitable for real-
world banking applications.
Data Collection
We began our research by collecting high-quality and
reliable data essential for training and evaluating our
deep learning model. The primary dataset used in this
study was the Credit Card Fraud Detection Dataset
available from Kaggle, originally provided by European
cardholders.
This
dataset
contains
real-world
anonymized credit card transactions over a two-day
period in September 2013. To ensure our model’s
adaptability to real-world banking systems, we further
collaborated with a financial institution to access
additional anonymized transaction data. This allowed
us to validate the model against diverse transaction
types and behavioral patterns while maintaining strict
adherence to data privacy and ethical standards.
The following table 1 provides a detailed overview of the dataset we used during the development and
training phases of our model:
Attribute
Description
Total Transactions
284,807
Fraudulent Transactions
492 (approximately 0.172%)
Non-Fraudulent Transactions 284,315
Time
Seconds elapsed between each transaction and the first transaction in the dataset
Amount
Transaction amount in Euros
Features V1 to V28
Result of PCA transformation for privacy protection
Class
Target variable (1 = Fraudulent, 0 = non-fraudulent)
Data Duration
2 consecutive days of transaction data
Data Source
Public dataset (Kaggle), with additional anonymized records from a bank
The dataset consists of 30 input features, of which 28
are anonymized using Principal Component Analysis
(PCA) for confidentiality, while two features
—
Time and
Amount
—
remain in their raw numerical form. The
target feature, labeled as Class, indicates whether a
transaction is fraudulent (1) or not (0).
This dataset presented a significant class imbalance
problem, with fraudulent transactions representing a
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very small fraction of the total volume. To ensure the
robustness of our model under these challenging
conditions, we incorporated both oversampling and
under sampling techniques, which we detail in the data
processing section. Our primary objective in collecting
and preparing this data was to reflect real-world
banking environments as closely as possible, allowing
the deep learning model to generalize well and perform
accurately in live financial systems.
Data Processing
Once we collected the raw dataset, we initiated a
thorough data preprocessing phase to ensure its
suitability for training deep learning models. This phase
included multiple sub-tasks such as handling missing
values, noise reduction, normalization, and data
transformation. First, we scanned the dataset for null
or missing values, which we handled using appropriate
imputation methods based on the statistical nature of
the attributes. For numerical fields, we used mean or
median imputation, while for categorical data, we
employed the mode or the most frequent class.
Next, we dealt with data imbalance, which is a common
issue in fraud detection datasets, where fraudulent
transactions are significantly fewer than legitimate
ones. To address this, we employed techniques such as
SMOTE (Synthetic Minority Over-sampling Technique)
to balance the class distribution. We also used
undersampling methods selectively to avoid overfitting
on synthetic data. Furthermore, we applied Min-Max
normalization to rescale the features into a standard
range, typically between 0 and 1, ensuring that all
features contributed equally during model training.
Feature Selection
With the dataset cleaned and preprocessed, we
proceeded to the feature selection phase to identify the
most relevant and informative variables for fraud
detection. We conducted an in-depth correlation
analysis to examine the relationship between various
features and the target variable (fraud or non-fraud).
This involved the use of statistical metrics such as
Pearson correlation coefficient, chi-square tests, and
mutual information scores.
We also explored dimensionality reduction techniques
such as Principal Component Analysis (PCA) to
eliminate redundant and collinear features while
retaining maximum variance in the data. This step was
particularly useful in enhancing computational
efficiency and reducing model complexity. In parallel,
we leveraged domain knowledge from financial experts
to retain transaction-specific features known to exhibit
strong fraud indicators, such as sudden changes in
transaction amount, unusual location or time of
purchase, and deviation from typical user behavior.
Feature Engineering
Following feature selection, we engaged in advanced
feature engineering to extract new and meaningful
insights from the existing variables. This step aimed to
create high-level abstract features that could boost the
performance of our deep learning model. We created
temporal features such as transaction frequency over
time, time since last transaction, and transaction
patterns during different periods of the day or week.
Additionally, we developed behavioral features by
profiling customers based on their historical
transaction behavior. These included average
transaction amount, standard deviation, preferred
merchants, geolocation movement patterns, and the
velocity of transactions (e.g., multiple transactions
within a short time span). By engineering these
features, we enabled our model to detect subtle
anomalies that may not be captured by raw features
alone.
We also applied one-hot encoding to transform
categorical variables such as transaction type or
merchant category into numerical format suitable for
deep learning models. Furthermore, we ensured that
the engineered features were standardized and scaled
appropriately to maintain consistency across the input
space.
Model Design and Training
Our deep learning model architecture was carefully
designed to capture complex nonlinear relationships
and temporal dependencies in the transaction data. We
employed a combination of Long Short-Term Memory
(LSTM) networks and Dense (fully connected) layers to
effectively process sequential transaction data and
learn contextual patterns over time.
The LSTM layers were particularly effective in capturing
temporal behaviors such as transaction frequency and
user habits, which are crucial for fraud detection. We
experimented with various hyperparameters including
the number of LSTM units, dropout rates, learning rate,
batch size, and number of epochs to optimize model
performance. The activation functions used in the
network included ReLU in hidden layers and Sigmoid in
the output layer for binary classification.
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We trained the model using the Adam optimizer and
binary cross-entropy loss function. During the training
process, we applied regularization techniques such as
dropout and L2 regularization to prevent overfitting.
We also utilized early stopping based on validation loss
to ensure that the model does not overtrain on the
dataset.
Model Evaluation
After training the model, we evaluated its performance
using a comprehensive set of metrics tailored for fraud
detection tasks. Since fraud detection involves an
imbalanced dataset, accuracy alone was not a sufficient
metric. Therefore, we focused on precision, recall, F1-
score, Area Under the ROC Curve (AUC-ROC), and
confusion matrix analysis.
Precision measured how many of the transactions we
labeled as fraud were actually fraudulent, while recall
assessed how many of the total fraudulent transactions
we correctly identified. The F1-score provided a
balance between precision and recall. The AUC-ROC
metric offered an aggregate measure of model
performance across all classification thresholds, giving
insight into the trade-off between true positive rate
and false positive rate.
We also conducted real-time simulations using
streaming data to test the model’s performance under
realistic banking conditions. This involved feeding the
model transaction data in real-time and observing its
ability to detect and flag suspicious activities promptly.
The latency of predictions was kept minimal to align
with the requirements of real-time fraud detection
systems.
In addition, we compared our deep learning model with
traditional machine learning classifiers such as Logistic
Regression, Random Forest, and Support Vector
Machine to benchmark its performance. Our model
consistently outperformed these alternatives in terms
of recall and F1-score, indicating its superior capability
in detecting fraudulent transactions while minimizing
false alarms.
Through this carefully designed methodology, we
developed a highly effective deep learning model for
real-time credit card fraud detection. Each phase
—
from data collection to model evaluation
—
was crucial
in building a reliable, scalable, and intelligent fraud
detection system tailored for the dynamic needs of
modern banking environments. By leveraging advanced
techniques in data processing, feature engineering, and
neural network design, we successfully demonstrated
the power of deep learning in safeguarding financial
systems against fraudulent activities.
RESULTS
After successfully completing the model training and
evaluation pipeline, we proceeded to analyze the
performance outcomes of our deep learning model in
comparison to several conventional machine learning
algorithms. Our objective was not only to assess the
predictive capability of each model but also to
understand how well these algorithms perform under
the constraints and complexities of real-world financial
fraud detection
—
especially in scenarios with highly
imbalanced datasets.
We evaluated five classification models: Logistic
Regression (LR), Decision Tree (DT), Random Forest
(RF), Support Vector Machine (SVM), and our proposed
Deep Learning model based on Long Short-Term
Memory (LSTM) networks. Each model was trained
using the same training dataset, processed using
identical preprocessing, resampling, and feature
selection strategies to ensure fairness in comparison.
To maintain consistency and reliability in our evaluation
process, we divided our dataset using an 80/20 train-
test split, applying stratified sampling to preserve the
ratio of fraudulent to non-fraudulent transactions.
Furthermore, we employed 5-fold cross-validation to
reduce variance and provide a robust assessment of
model performance.
We evaluated the models using five widely accepted
performance metrics: Accuracy, Precision, Recall, F1-
Score, and the Area Under the ROC Curve (AUC-ROC).
These metrics allowed us to evaluate the models not
just in terms of overall correctness (accuracy), but more
importantly, in terms of their ability to correctly identify
rare fraudulent activities (recall) while minimizing false
alarms (precision).
The detailed comparison of all models is shown in the table 2 below:
Model
Accuracy Precision Recall F1-Score AUC-ROC
Logistic Regression
0.961
0.748
0.612 0.673
0.945
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Decision Tree
0.953
0.763
0.688 0.723
0.917
Random Forest
0.977
0.882
0.811 0.845
0.978
Support Vector Machine 0.972
0.802
0.774 0.787
0.965
Deep Learning (LSTM) 0.985
0.932
0.887 0.909
0.987
Upon close analysis of the performance metrics, we
found that our Deep Learning LSTM model significantly
outperformed all other models across every evaluation
parameter. We achieved an accuracy of 98.5%,
indicating that the vast majority of both fraudulent and
non-fraudulent transactions were correctly classified.
However, we recognize that in fraud detection,
accuracy alone can be misleading due to class
imbalance, where even a high accuracy may not reflect
good fraud detection. Therefore, we paid special
attention to recall and precision, which are critical in
fraud scenarios.
Our LSTM model achieved a precision of 93.2%, which
means that over 93% of the transactions it flagged as
fraudulent were indeed frauds. This minimizes false
positives
—
reducing the likelihood of mistakenly
flagging legitimate customer activities, which can
disrupt customer trust and banking operations. Even
more importantly, the model achieved a recall of
88.7%, demonstrating its strength in capturing a large
majority of the actual fraudulent transactions. This is a
key performance metric, as missing fraudulent
transactions can result in significant financial loss and
reputational damage.
The F1-score, which is the harmonic mean of precision
and recall, was recorded at 90.9%
—
signifying a strong
balance between both measures. Additionally, the
AUC-
ROC score of 0.987 affirms our model’s excellent
capability in distinguishing between the two classes,
even in the presence of noise and imbalance. This
makes our LSTM model highly reliable for real-time
fraud detection systems, where fast and accurate
classification is essential.
Chart 1: Model Performance of different machine learning algorithms
In contrast, traditional models such as Logistic
Regression and Decision Trees delivered relatively
lower performance. While these models were
computationally efficient and easier to implement, they
0.96
1
0.95
3
0.97
7
0.97
2
0.98
5
0.74
8
0.76
3
0.882
0.80
2
0.93
2
0.61
2
0.68
8
0.81
1
0.77
4
0.88
7
0.67
3
0.72
3
0.84
5
0.787
0.90
9
0.94
5
0.91
7
0.97
8
0.96
5
0.98
7
L O G I S T I C
R E G R E S S I O N
D E C I S I O N T R E E
R A N D O M F O R E S T
S U P P O R T V E C T O R
M A C H I N E
D E E P L E A R N I N G
( L S T M )
Accuracy
Precision
Recall
F1-Score
AUC-ROC
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failed to generalize complex transaction behaviors and
temporal patterns. Logistic Regression, for instance,
although achieving an accuracy of 96.1%, struggled
with a lower recall of 61.2%, indicating a substantial
number of missed fraud cases. Decision Trees
performed slightly better but still exhibited limitations
in overfitting and handling feature interactions.
The Random Forest model stood out among traditional
classifiers, reaching an accuracy of 97.7%, a precision of
88.2%, and a recall of 81.1%. These results are
respectable and illustrate the power of ensemble
learning in managing non-linear relationships.
Nevertheless, it still lagged behind the deep learning
model in capturing long-term temporal dependencies
and learning behavioral sequences inherent in
transaction data.
The Support Vector Machine model also performed
well, with an accuracy of 97.2%, but required significant
computational resources and hyperparameter tuning
to deal with class imbalance and overlapping features.
Despite its relatively high AUC-ROC of 0.965, it fell short
in both precision and recall compared to the LSTM
model.
We believe that the superior performance of the deep
learning model stems from its ability to learn temporal
dynamics and nonlinear patterns in sequential data
—
a
feature particularly crucial in banking systems where
fraudsters often act with subtle behavioral patterns
over time. By leveraging LSTM’s memory c
ells, we were
able to model such complex dependencies across
sequences of transactions, thereby enhancing
detection in both short- and long-term contexts.
Moreover, we found that the deep learning model
adapted better to changes in transaction volume, time-
of-day patterns, and frequency of transactions, all of
which are important indicators of potential fraud.
Through our extensive tuning and evaluation, we
concluded that our LSTM-based approach is highly
effective for real-time fraud detection applications, as
it not only flags fraud with high accuracy but also does
so quickly, making it suitable for deployment in live
banking environments.
In summary, our experimental results demonstrate that
while traditional machine learning models provide a
solid
foundation,
deep
learning
approaches,
particularly
those
utilizing
recurrent
neural
architectures, offer a significant leap forward in
performance, adaptability, and real-world applicability
for fraud detection systems.
DISCUSSION AND CONCLUSION
In this study, we developed a deep learning-based
framework for detecting credit card fraud in real time,
leveraging the power of Long Short-Term Memory
(LSTM) networks alongside robust data preprocessing
and feature engineering strategies. The results clearly
demonstrate the superiority of LSTM over traditional
machine learning models such as Logistic Regression,
Decision Tree, and Random Forest, especially in the
context of time-series data where transactional
behavior over time plays a pivotal role in identifying
anomalies.
Our deep learning model achieved a higher accuracy
and precision in detecting fraudulent transactions, with
significantly
reduced
false
positives.
These
improvements are crucial for banking systems where
unnecessary transaction blocks can lead to customer
dissatisfaction and operational inefficiencies. In real-
world scenarios, minimizing false positives is just as
important as maximizing fraud detection, as it directly
impacts user trust and system reliability.
One of the major challenges in fraud detection is the
extreme class imbalance, where legitimate transactions
vastly outnumber fraudulent ones. We addressed this
issue by implementing effective data balancing
techniques, which improved the model's ability to
generalize and detect fraudulent patterns more
effectively. Additionally, our thorough feature selection
and engineering ensured that the model learned from
the most relevant patterns while reducing noise.
Furthermore, our comparative study reveals that while
traditional models like Random Forest and Logistic
Regression
offer
faster
training
times
and
interpretability, they fall short in capturing sequential
dependencies within transaction flows. In contrast,
LSTM excels at modeling these sequences, making it
particularly well-suited for real-time fraud detection
where understanding user behavior over time is critical.
We also found that incorporating time-based features
and transaction metadata significantly improved model
performance.
These
insights
underscore
the
importance of domain-specific feature engineering and
the integration of temporal dynamics in fraud detection
systems.
Despite our success, several limitations persist. First,
real-time deployment in production environments may
require further optimization of the LSTM model to
reduce latency. Second, while the current dataset
provides a reliable benchmark, it lacks real-time
streaming capabilities, which should be addressed in
future research through integration with live
transaction systems. Finally, explainability remains a
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challenge with deep learning models. For operational
transparency and regulatory compliance, we suggest
the use of explainable AI (XAI) techniques in future
iterations of the model.
In conclusion, this research confirms the potential and
effectiveness of deep learning
—
specifically LSTM
networks
—
for real-time credit card fraud detection in
banking systems. Through rigorous data preprocessing,
targeted feature selection, and comparative analysis,
we have shown that deep learning models can
outperform traditional machine learning algorithms in
both detection accuracy and temporal awareness.
Our LSTM-based model provides a strong foundation
for deploying scalable and accurate fraud detection
systems capable of adapting to evolving fraud tactics in
real-time environments. By addressing key challenges
such as class imbalance, data volume, and pattern
complexity, we have contributed to the advancement
of intelligent security systems in the financial sector.
Looking ahead, future work should focus on integrating
streaming
data
pipelines,
improving
model
interpretability, and combining deep learning with
blockchain-based transaction verification for even
more robust fraud detection. With continuous
improvement and innovation, artificial intelligence can
play a central role in securing the financial world
against ever-increasing threats of fraud.
Acknowledgement: All the author contributed equally
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