The American Journal of Applied Sciences
128
https://www.theamericanjournals.com/index.php/tajas
TYPE
Original Research
PAGE NO.
128-132
10.37547/tajas/Volume07Issue07-13
OPEN ACCESS
SUBMITED
18 June 2025
ACCEPTED
24 June 2025
PUBLISHED
27 July 2025
VOLUME
Vol.07 Issue 07 2025
CITATION
Dip Bharatbhai Patel. (2025). Comparing Neural Networks and
Traditional Algorithms in Fraud Detection. The American Journal of
Applied Sciences, 7(07), 128
–
132.
https://doi.org/10.37547/tajas/Volume07Issue07-13
COPYRIGHT
© 2025 Original content from this work may be used under the terms
of the creative commons attributes 4.0 License.
Comparing Neural
Networks and Traditional
Algorithms in Fraud
Detection
Dip Bharatbhai Patel
University of North America, Virginia, USA
Abstract:
Fraud detection has become an essential
component of financial security systems. Traditional
algorithms have long served as the backbone of these
systems. The rise of neural networks is revolutionizing
the process as it offers new approaches to identifying
complex fraud patterns. The paper presents a
comparative analysis of neural networks and traditional
algorithms. These include decision trees, rule-based
systems, and logistic regression in fraud detection. The
comparison is based on scalability, accuracy,
interpretability,
computational
efficiency,
and
adaptability. The findings reveal that neural networks
outperform traditional methods in subtle, non-linear
fraud patterns but suffer from interpretability and data
requirements. A hybrid detection framework that
combines neural intelligence with rule-based logic is
proposed for real-time, robust fraud management. For
instance, a neural ensemble model achieved over 97%
accuracy while traditional systems achieved 89-91%.
The paper highlights that the hybrid approach offers
optimal results in real-world scenarios.
Keywords:
Fraud detection, neural networks, machine
learning, traditional algorithms, anomaly detection.
I.
INTRODUCTION
Fraud poses a significant threat to global financial
systems. It is costing many companies billions annually.
As digital transactions increase, the complexity of
fraudulent behaviors also increases. Therefore, fraud
detection systems must evolve to detect and identify
fraudsters' ever-changing tactics. Historically, traditional
algorithms have served as the key detection mechanism.
However, neural networks and deep learning have
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emerged as the most effective tools in predictive
analytics and anomaly detection. Traditional machine
learning techniques like decision trees, support vector
machines, and logistic regression are widely used due to
their transparency, and they are easy to deploy. Neural
networks have revolutionized the field of pattern
recognition and anomaly detection. It is capable of
learning complex temporal, behavioral, and spatial
patterns. This strategy is effective in adapting to fraud
tactics that escape traditional logic.
Despite these strengths, neural models are often
criticized for thei
r “black box” nature and computational
complexity. The paper compares traditional algorithms
and neural networks to determine their strengths and
weaknesses in fraud detection. It will explore how each
method works, analyze performance metrics, and assess
their practicality in real-world applications.
II.
Literature Review
Fraud detection involves identifying suspicious or
unauthorized transactions within massive datasets.
Techniques used can be categorized into classifications,
which are supervised. We have unsupervised, which is
anomaly detection. Traditional methods like decision
trees, support vector machines, and logistic regression
rely on predefined rules and feature engineering. Neural
networks, such as deep learning models, leverage
hierarchical
layers
to
learn
complex
data
representations.
Many issues are encountered by fraud detection
systems, including evolving fraud patterns, class
imbalance, and the need for real-time detection.
Therefore, it is essential to select the correct algorithm
to handle these challenges effectively.
A.
Traditional Fraud Detection Algorithms
These algorithms have long been employed in fraud
detection due to their interpretability, ease of
implementation, and simplicity. Logistic regression is
one of the most commonly used techniques. It is
effective due to its speed and ability to handle binary
classification. This approach assumes a linear
relationship and struggles with complex patterns.
Another method is decision trees and random forests.
The models are intuitive and can handle non-linear data
better than regression. Random forest, for instance, can
improve
performance
via
ensemble
learning
(Murorunkwere et al., 2022). The model might be
computationally expensive on large datasets. We have
Rule-based systems that heavily rely on domain
expertise. Rule-based systems are critical to providing
transparent decisions. However, these models are
brittle and demand frequent updates to match the ever-
changing fraud patterns.
Okur et al. (2021) believe the Support Vector Machine
(SVM) model is effective in high-dimensional spaces.
They are used in linear and non-linear classification.
However, they have limitations, such as handling large-
scale datasets. These authors discovered that rule-based
and statistical models like decision SVM and decision
trees are still being used extensively. Their simplicity
allows for rapid deployment and transparency. They,
however, limit adaptability to changing fraud trends.
B.
Neural Networks for Fraud Detection
Neural networks, intense learning models such as RNNs
and CNNs, improve fraud detection accuracy. They are
capable of automatically learning hierarchical data
patterns. Osegi and Jumbo (2021) propose a simulated
annealing-trained
neural
model
outperforming
traditional classifiers.
Artificial Neural Networks (ANNs)
These networks mimic brain-like structures, and they
can handle complex non-linear relationships. These
models are effective when they are well-trained on large
datasets. These datasets must be well-labeled to make
it easy to operate.
Convolutional Neural Networks (CNNs)
These models are standard in image processing. These
models can also be used for feature extraction in
traditional patterns and are effective in user behaviors
(Okur et al., 2021).
Recurrent Neural Networks (RNNs) and LSTMs
These models are well-suited for sequential data like
transaction time series. The models offer various
advantages, like tracking behavioral anomalies over
time, which is important.
Autoencoders and Anomaly Detection
Autoencoders are effective in unsupervised settings.
They are considered adequate for detecting outliers in
high-dimensional datasets with scarce labeled data.
C.
Advances in Deep Learning
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Esenogho et al. (2022) highlight the importance of
embedding a neural ensemble model with feature
engineering, as this will improve recall and precision.
Karthika and Senthilselvi (2023) highlight that dilated
convolutional neural networks integrated with sampling
techniques are critical in overcoming class imbalance.
D.
Challenges and Limitations
Data availability and quality are the first challenges, as
these networks require massive labeled datasets for
training. These datasets might not always be available.
Meanwhile, traditional models perform better with
smaller datasets (Alarfaj et al., 2022). Class imbalance is
another limitation; fraud cases are rare, mostly less than
1% of the data. Therefore, these models suffer from this
limitation. As fraud patterns evolve rapidly, model
updating is another challenge, and neural networks can
be retrained frequently. However, the process is
computationally intensive. It is important to note that
traditional systems require manual rule updates. We
have adversarial attacks as neural networks are
vulnerable to adversarial manipulation. Therefore,
fraudsters can exploit model weaknesses, especially in
black-box models. Hilal et al. (2022) highlight that
despite the success of RNNs, they lack accountability,
and this limits their acceptability in regulated financial
environments. Albuquerque Filho et al. (2022)
emphasizes the need for explainable AI in production-
grade anomaly detection.
E.
Quantum and Emerging Models
According to Innan et al. (2024), a quantum graph neural
network is effective in achieving enhanced fraud
detection accuracy. This method can guarantee
accuracy with minimal training overhead. It therefore
suggests that the future of fraud analytics will likely
include quantum and edge-based models.
III.
Methodology
The research adopts a comparative analysis framework
using secondary data from recent peer-reviewed
studies. The fraud detection models were evaluated
based on accuracy, interpretability, precision, and recall.
They were also evaluated based on adaptability to
imbalanced data, scalability, and resource demand.
Algorithms that were compared included neural
network models, such as CNNs, Ensemble, QGNNs,
Graph NNs, DCNN, and RNNs. Traditional models,
including SVMs, logistic regression, and decision trees,
were also compared. The performance indicators were
extracted and compared in structured tables. A hybrid
detection architecture is highlighted to demonstrate
how different models can be integrated in practice.
IV.
Results and Analysis
Table 1: Performance Metrics from Recent Literature
Model
Accuracy
(%)
Precision
Recall
Interpretability
Logistic Regression
89.0
0.84
0.81
High
Decision Tree
90.7
0.87
0.85
High
Support Vector
Machine
91.3
0.89
0.86
Medium
Dilated CNN
96.4
0.92
0.90
Low
Ensemble Neural
Network
97.1
0.94
0.95
Low
Quantum GNN
98.2
0.96
0.95
Very Low
V.
Proposed Hybrid Architecture
We propose a hybrid fraud detection pipeline to
reconcile the strengths of both approaches.
[Transaction]
↓
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[Rule-
Based Engine] → [Anomaly Score]
↓
[Neural Network Classifier] → [Fraud Decision]
This architecture enhances performance while ensuring
compliance. As depicted in Hilal et al. (2022), integrating
a rule-based stage can reduce false positives by 30%.
Accuracy and False Positives
The neural network outperforms traditional models
regarding precision and recall, especially with
imbalanced datasets. Traditional models, however, offer
fewer false positives in well-engineered rule-based
systems.
Interpretability
Neural networks are criticized due to their black-box
nature. Decision trees and logistic regression provide
clear reasoning for each decision. This is an important
approach for regulatory compliance.
Computational Efficiency
While traditional models are lightweight and efficient,
neural networks require huge computational resources
and GPU support.
Real-World Deployment
In many real-world financial systems, hybrid models are
effective. These models combine rule-based filters with
deep learning models, which have been proven to be
effective and reliable.
Conclusion
Traditional algorithms and neural networks have their
place in fraud detection. They have an important role to
play in fraud detection, and therefore, understanding
their strengths and limitations is critical in employing
them as required. Traditional models are effective as
they offer fast performance and, therefore, can be
reliable in handling emergencies. They offer
interpretability, which is essential in allowing one to
comprehend and ease of deployment; thus,
implementation is easy. However, traditional models
lack adaptability, which limits their use. Neural networks
provide higher accuracy and, therefore, reliability. They
offer better detection of subtle fraud patterns. They,
however, require large datasets and computational
resources, and this can limit small players.
The future of fraud detection lies in hybrid systems.
These systems are effective as they leverage the
strengths of both approaches. This is important for
robust adaptive systems, as they can guarantee
explainable fraud detection frameworks. Companies
should ass their specific needs. They must meet
regulatory requirements, response time, and dataset
size to design or select the most effective system. It is,
therefore, important to implement a hybrid model as
this will guarantee better fraud detection.
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