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TYPE
Original Research
PAGE NO.
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10.37547/tajmei/Volume07Issue04-15
OPEN ACCESS
SUBMITED
19 February 2025
ACCEPTED
22 March 2025
PUBLISHED
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VOLUME
Vol.07 Issue 04 2025
CITATION
Tamanna Pervin, Sharmin Akter, Sadia Afrin, Md Refat Hossain, MD
Sajedul Karim Chy, Sadia Akter, Md Minzamul Hasan, Md Mafuzur
Rahman, & Chowdhury Amin Abdullah. (2025). A Hybrid CNN-LSTM
Approach for Detecting Anomalous Bank Transactions: Enhancing
Financial Fraud Detection Accuracy. The American Journal of
Management and Economics Innovations, 7(04), 116
–
123.
https://doi.org/10.37547/tajmei/Volume07Issue04-15
COPYRIGHT
© 2025 Original content from this work may be used under the terms
of the creative commons attributes 4.0 License.
A Hybrid CNN-LSTM
Approach for Detecting
Anomalous Bank
Transactions: Enhancing
Financial Fraud Detection
Accuracy
Tamanna Pervin
Department of Business Administration, International American
University, Los Angeles, California, USA
Sharmin Akter
Department of Information Technology Project Management, St. Francis
College, USA
Sadia Afrin
Department of Computer & Information Science, Gannon University, USA
Md Refat Hossain
Master of Business Administration, Westcliff University, USA
MD Sajedul Karim Chy
Department of Business Administration, Washington University of Science
and Technology, USA
Sadia Akter
Department of Business Administration, International American
University, USA
Md Minzamul Hasan
Doctor of Business Administration (DBA), College of Business, Westcliff
University, USA
Md Mafuzur Rahman
Master’s in data Analytics, Harrisburg University of Science & Technology,
USA
Chowdhury Amin Abdullah
Seidenberg School of CSIS, Pace University, USA
Abstract:
Detecting fraudulent bank transactions is
crucial for maintaining the integrity of financial
institutions and preserving customer trust. This study
introduces a hybrid Convolutional Neural Network
–
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Long Short-Term Memory (CNN-LSTM) model designed
to enhance the accuracy and efficiency of fraud
detection systems. Utilizing the European Credit Card
Fraud
Detection
dataset
comprising
284,807
transactions with significant class imbalance, extensive
preprocessing techniques were applied, including Min-
Max scaling and Synthetic Minority Over-sampling
Technique (SMOTE). Recursive Feature Elimination
(RFE) identified the top 20 impactful features,
optimizing model performance. The proposed hybrid
model
demonstrated
remarkable
effectiveness,
achieving superior accuracy (99.5%), precision (93.1%),
recall (92.1%), F1-score (92.6%), and an Area Under the
Receiver Operating Characteristic Curve (AUC-ROC) of
97.5%. Comparative analyses revealed that the hybrid
CNN-LSTM
model
significantly
outperformed
traditional machine learning algorithms such as Logistic
Regression, Random Forest, and XGBoost. These
findings underscore the potential of CNN-LSTM hybrid
models in addressing complex fraud detection
scenarios, providing financial institutions with a robust
and reliable tool for transaction anomaly detection.
Keywords:
Fraud Detection, CNN-LSTM Hybrid Model,
Credit Card Fraud, SMOTE, Feature Selection, Machine
Learning, Financial Security
Introduction:
Financial institutions increasingly rely on
digital transactions, significantly raising concerns
related to fraudulent activities. Fraudulent transactions
not only result in direct financial losses but also severely
impact customer trust and institutional credibility.
Consequently, the development of robust and reliable
systems to detect and prevent fraud is paramount.
Traditionally, manual detection methods have been
employed; however, due to the enormous volume and
complexity of transaction data, such methods have
become ineffective and inefficient. Machine learning
techniques have emerged as powerful tools,
significantly enhancing the accuracy and efficiency of
fraud detection systems (Awoyemi et al., 2017).
In recent years, hybrid machine learning models,
specifically combining Convolutional Neural Networks
(CNN) and Long Short-Term Memory (LSTM) networks,
have demonstrated substantial potential in improving
detection capabilities by effectively capturing both
spatial and sequential patterns in transaction data
(Zhang & Chen, 2019). This study proposes a hybrid
CNN-LSTM model to effectively detect fraudulent bank
transactions, addressing existing limitations of
traditional fraud detection techniques.
Literature Review
The detection of fraudulent transactions has been
extensively studied, employing various machine
learning and deep learning techniques. Logistic
regression, decision trees, random forests, and
gradient boosting models such as XGBoost have
historically been popular for this task due to their ease
of implementation and interpretability (Dal Pozzolo et
al., 2014; Bhattacharyya et al., 2011). However, these
models often struggle with imbalanced data and may
fail to accurately detect intricate fraud patterns.
Convolutional Neural Networks (CNN) have shown
promising results by effectively identifying spatial
correlations within data (Jurgovsky et al., 2018).
Nevertheless, CNNs alone do not effectively capture
the temporal sequences that characterize fraudulent
transaction patterns. To address this limitation, Long
Short-Term Memory (LSTM) networks, known for their
ability to capture long-term dependencies in sequential
data, have been employed with significant success in
various sequential data problems, including financial
fraud detection (Roy et al., 2018).
Hybrid models integrating CNN and LSTM architectures
have been introduced to leverage both spatial and
sequential data processing capabilities. For instance,
Zhang and Chen (2019) employed a CNN-LSTM hybrid
architecture to enhance the detection of complex
patterns, significantly outperforming traditional
models in terms of accuracy and precision. Similarly,
recent studies have applied CNN-LSTM models to
various domains, such as intrusion detection and
healthcare
analytics,
further
supporting
the
effectiveness of these hybrid approaches in anomaly
detection (Yin et al., 2017; Pham et al., 2020).
Despite these advances, challenges remain, particularly
regarding the imbalance of transaction datasets,
interpretability of models, and the computational
efficiency of deep learning architectures. Hence, this
study aims to build upon existing literature by
specifically focusing on mitigating these challenges
through sophisticated data preprocessing techniques
and optimized hybrid CNN-LSTM architectures.
METHODOLOGY
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Data Collection
The study leveraged the European Credit Card Fraud
Detection dataset, a widely recognized dataset in fraud
detection
research.
The
dataset
comprises
transactional data derived from actual European credit
card users, anonymized to protect individual privacy.
The complete dataset includes 284,807 transactions,
recorded over two consecutive days, out of which only
492 (approximately 0.172%) transactions were labeled
as fraudulent, highlighting significant class imbalance.
The dataset comprises 30 independent features,
including 28 anonymized numerical variables (V1 to
V28) created through Principal Component Analysis
(PCA), and two additional numerical variables: 'Time'
and 'Amount.' The 'Time' variable measures the interval
(in seconds) elapsed between each transaction and the
first recorded transaction. The 'Amount' variable
denotes the monetary value of each transaction. The
dependent variable, 'Class,' is binary, indicating normal
(0) or fraudulent (1) transactions.
Table 1: Dataset Details description
Feature
Description
Time
Interval (in seconds) from the first recorded transaction
Amount
Monetary transaction value
V1 – V28
PCA-derived anonymized features
Class
Transaction classification (0: legitimate, 1: fraudulent)
Dataset Size
284,807 Transactions
Fraudulent Cases
492 (0.172% of total data)
Source
Kaggle - Credit Card Fraud Detection Dataset
DATA PREPROCESSING
Effective preprocessing was vital due to the inherent
characteristics of the dataset. Initially, the dataset was
analyzed for missing values, and none were found,
ensuring data integrity. Subsequently, Min-Max scaling
was implemented on features 'Time' and 'Amount' to
standardize their ranges between 0 and 1, enabling
efficient learning by the neural network models. The
PCA-derived features (V1
–
V28) did not require
additional scaling since they were preprocessed and
standardized during the original anonymization.
The dataset exhibited a highly skewed class
distribution, significantly biasing models toward the
majority (legitimate transactions). To counteract this
imbalance and enhance model generalizability, the
Synthetic Minority Over-sampling Technique (SMOTE)
was
applied,
generating
synthetic
fraudulent
transaction records to balance the class distributions.
This approach facilitated robust learning of patterns
within the minority class, essential for effective fraud
detection.
Feature Selection
While PCA initially reduced dimensionality and ensured
anonymization, further feature selection was crucial to
enhance
model
performance
and
reduce
computational
complexity.
Recursive
Feature
Elimination (RFE) combined with Logistic Regression as
the baseline estimator was applied to systematically
identify the most impactful features. Through iterative
training and evaluation, the top 20 significant features
contributing most substantially to distinguishing
fraudulent transactions from legitimate ones were
selected. This reduced feature set enhanced
computational
efficiency,
improved
model
interpretability, and maintained high predictive
accuracy.
Model Construction
A sophisticated hybrid Convolutional Neural Network
–
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Long Short-Term Memory (CNN-LSTM) model was
developed to leverage the strengths of both CNN and
LSTM architectures. The CNN component was designed
to capture local spatial correlations and complex
interactions between transaction features. Initially, the
data passed through multiple 1D convolutional layers,
each employing ReLU activation to introduce non-
linearity
and
facilitate
feature
extraction.
Subsequently,
max-pooling
layers
reduced
dimensionality, effectively
summarizing
feature
representations
and
highlighting
prominent
transaction patterns.
Following CNN layers, the extracted spatial features
were processed by LSTM layers, specifically structured
to capture sequential dependencies inherent in
transaction patterns over time. LSTM layers' gated
mechanism allowed the model to memorize significant
temporal patterns and relationships, particularly
essential for fraud detection where fraudulent
transactions often exhibit unique sequential anomalies
compared to legitimate patterns.
The CNN-LSTM layers were followed by a dense layer
equipped with sigmoid activation, providing a binary
classification output predicting the probability of
transaction fraudulence. Additionally, dropout layers
with a rate of 0.5 were incorporated between network
layers to mitigate overfitting, thereby enhancing the
model's generalizability to unseen data. The model
compilation utilized the Adam optimizer due to its
adaptive learning rate and efficiency, with binary cross-
entropy selected as the loss function, optimizing the
model toward accurately classifying transactions.
Model Evaluation
To rigorously evaluate the effectiveness of the CNN-
LSTM hybrid model, the dataset was partitioned into an
80% training set and a 20% testing set. Several robust
metrics were employed for assessment, including
Accuracy, Precision, Recall, F1-Score, and the Area
Under the Receiver Operating Characteristic Curve
(AUC-ROC). Given the dataset's inherent imbalance,
particular emphasis was placed on the Recall and AUC-
ROC metrics, which accurately reflect the model’s
proficiency in identifying true fraudulent transactions
without excessive false positives.
The hybrid model's performance was extensively
compared against benchmark models, including
Logistic Regression, Random Forest, and XGBoost
classifiers. Cross-validation strategies ensured the
reliability of performance comparisons. The evaluation
highlighted
the
CNN-LSTM
model's
superior
performance, demonstrating its enhanced capability in
detecting anomalies efficiently and accurately. The
hybrid approach consistently surpassed baseline
models across multiple metrics, confirming the
effectiveness of combining CNN’s spatial feature
extraction with LSTM’s sequential patt
ern recognition
in fraud detection tasks.
RESULTS
A comprehensive evaluation of the CNN-LSTM
hybrid model was conducted, comparing it against
traditional machine learning algorithms, including
Logistic Regression, Random Forest, and XGBoost.
The evaluation employed key metrics: Accuracy,
Precision, Recall, F1-Score, and AUC-ROC, ensuring a
thorough analysis of model performance in Table 1.
Model
Accuracy
Precision
Recall
F1-Score
AUC-ROC
CNN-LSTM
99.5%
93.1%
92.1%
92.6%
97.5%
Logistic Regression
97.8%
76.5%
65.8%
70.7%
89.4%
Random Forest
98.9%
87.6%
82.5%
85.0%
93.5%
XGBoost
99.0%
89.0%
84.0%
86.4%
94.2%
The CNN-LSTM model demonstrated superior accuracy
at 99.5%, notably higher than other models. Precision
(93.1%) and recall (92.1%) values were significantly
higher, reflecting robust performance in identifying
actual fraud cases while minimizing false positives. The
F1-score (92.6%), balancing precision and recall, further
validated the model's effectiveness. The AUC-ROC
value of 97.5% underscored the hybrid model’s
exceptional discriminatory power between fraudulent
and legitimate transactions, significantly surpassing
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other approaches.
The comparative analysis distinctly highlighted the
CNN-LSTM hybrid model's advantages, showcasing its
capability in handling class imbalances and capturing
complex
temporal-spatial
transaction
patterns
effectively, affirming its practical applicability in real-
world banking fraud detection scenarios.
Chart 1: Evaluation of Deep Learning model
A comparative analysis of the results clearly highlights
the CNN-LSTM hybrid model's advantage, achieving an
accuracy of 99.5%, which significantly surpasses
Logistic Regression (97.8%), Random Forest (98.9%),
and XGBoost (99.0%). Precision, crucial for fraud
detection to minimize false alarms, was highest for the
CNN-LSTM model at 93.1%, notably outperforming
Logistic Regression at 76.5%, Random Forest at 87.6%,
and XGBoost at 89.0%.
Moreover, recall
—
indicating the model's effectiveness
in identifying actual fraudulent transactions
—
was also
superior for CNN-LSTM, reaching 92.1%. This
performance is significantly higher compared to
Logistic Regression (65.8%), Random Forest (82.5%),
and XGBoost (84.0%). The F1-Score, a balanced
measure combining precision and recall, further
reinforced the CNN-LSTM model's efficiency with a
score of 92.6%, clearly outperforming the other
models. Finally, the AUC-ROC, a critical metric for
evaluating model capability to distinguish between
classes irrespective of classification thresholds,
demonstrated an exceptional score of 97.5% for CNN-
LSTM, notably surpassing Logistic Regression (89.4%),
Random Forest (93.5%), and XGBoost (94.2%).
The provided comparative bar chart visually
emphasizes the significant advantage of the CNN-LSTM
hybrid model across all evaluation metrics, confirming
its suitability and superiority in detecting anomalous
bank transactions. Such robust performance suggests
substantial practical applications in real-world financial
scenarios, emphasizing enhanced accuracy and
reliability in fraud detection.This detailed comparative
analysis affirms the effectiveness of combining CNN
and LSTM architectures, highlighting their collective
strengths in capturing complex feature interactions and
sequential
patterns,
leading
to
substantial
improvements over traditional machine learning
approaches.
CONCLUSION & DISCUSSION
The hybrid CNN-LSTM model introduced in this
research has demonstrated significant performance
improvements over traditional machine learning
methods in detecting fraudulent transactions. The
results clearly illustrate the model’s robustness and
effectiveness in accurately identifying fraudulent
99.5
0%
97.8
0%
98.9
0%
99.0
0%
93.1
0%
76.5
0%
87.6
0%
89.0
0%
92.10
%
65.8
0%
82.5
0%
84.0
0%
92.6
0%
70.7
0%
85.0
0%
86.4
0%
97.5
0%
89.4
0%
93.5
0%
94.2
0%
C N N - L S T M
L O G I S T I C R E G R E S S I O N
R A N D O M F O R E S T
X G B O O S T
M O D E L E V A L U A T I O N
Accuracy
Precision
Recall
F1-Score
AUC-ROC
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transactions while minimizing false positives. Key
factors contributing to the improved performance
include the model's ability to exploit spatial
correlations and sequential transaction patterns
simultaneously.
The integration of SMOTE for class imbalance handling
played a critical role in ensuring balanced training data,
which significantly improved the model’s learning
capability for minority-class patterns. Additionally, the
implementation of Recursive Feature Elimination (RFE)
effectively identified critical features, contributing to a
streamlined and efficient modeling process. The CNN
layers effectively captured the intricate feature
relationships within transactions, whereas the LSTM
layers proficiently addressed the temporal patterns
critical for detecting subtle fraudulent activities.
However, the model's complexity and interpretability
remain a limitation, posing challenges for practical
deployment in highly regulated financial environments.
Future research should focus on improving
interpretability, potentially through explainable AI
techniques,
and
exploring
more
efficient
computational frameworks to facilitate real-time fraud
detection applications.
This study successfully developed and evaluated a
hybrid CNN-LSTM model for detecting anomalous
banking transactions, significantly outperforming
traditional machine learning algorithms in accuracy,
precision, recall, F1-score, and AUC-ROC metrics. The
combination of CNN and LSTM effectively addressed
spatial and temporal transaction patterns, enhancing
the overall performance. The comprehensive approach
to data preprocessing and feature selection further
contributed to model effectiveness.
Given its superior performance, this hybrid model
presents a highly promising solution for financial
institutions aiming to mitigate fraud risks. Further
research efforts should aim to refine the model's
interpretability and computational efficiency, thus
facilitating broader real-world applicability and
adoption in financial fraud detection systems.
Acknowledgement: All the author contributed equally
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