Received: 15 December 2025; Revised: 02 March 2026; Accepted: 09 March 2026; Published Online: 10 March 2026.
J. Inf. Commun. Technol. Algorithms Syst. Appl., 2026, 2(1), 26301 | Volume 2 Issue 1 (March 2026) | DOI: https://doi.org/10.64189/ict.26301
© The Author(s) 2026
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)
Detection of UPI Mule Accounts Using Machine Learning
and Streamlit-Based Predictive Analytics
Ishita D. Sonawane,
*
Priyanshi R. Suyal, Shradha Chavan
*
and Rudresh Shirwaikar
*
School of Computer Science and Information Technology, Symbiosis Skills and Professional University, Pune, Maharashtra, 412101, India
*Email: ishita.sonawane@gmail.com (I. D. Sonawane), shradha.chavan@sspu.ac.in (S. Chavan), rudresh.shirwaikar@sspu.ac.in (R.
Shirwaikar)
Abstract
By facilitating quick and easy money transfers, the Unified Payments Interface (UPI), in particular, and the rapid
growth of digital payment systems in India have drastically changed the financial landscape. However, the growing
use of UPI has also resulted in an increase in fraudulent activity, particularly through mule accounts, which are used
to route or launder money while hiding the identity of scammers. This study suggests a machine learning-based
framework for identifying UPI mule accounts using transactional data to solve this problem. The suggested approach
uses the Light Gradient Boosting Machine (LightGBM) algorithm to determine whether a transaction is authentic or
fraudulent. Transaction-related characteristics such as transaction amount, time, user demographics, and fraud risk
labels are included in the dataset, which was taken from a publicly accessible Kaggle repository. Methods for
preprocessing data, such as label encoding, feature scaling, and Synthetic Minority Oversampling Technique (SMOTE),
were used to address class imbalance and enhance model performance. With a Receiver Operating Characteristic–
Area Under the Curve (ROC-AUC) score of 0.9962 and an accuracy of 96.55%, the trained LightGBM model proved to
have a strong ability to distinguish between fraudulent and legitimate transactions. Additionally, a web application
based on Streamlit was created to facilitate interactive model demonstration and real-time fraud risk prediction. The
suggested framework offers a scalable and effective way to improve the UPI ecosystem's fraud monitoring systems.
Keywords: UPI; Mule account; Fraud detection; Machine learning; LightGBM; Streamlit; ROC-AUC.
1. Introduction
With India emerging as one of the top adopters of real-time digital payment infrastructures, the swift digitization of
financial services has drastically changed the global payment ecosystem.
[1]
The shift to a cashless economy has been
sped up by the growing dependence on peer-to-peer transfers, merchant payments, and mobile-based transactions.
[2]
The Unified Payments Interface (UPI), one of these systems, has emerged as a key component of India's digital
payment infrastructure, facilitating quick, easy, and interoperable money transfers between banking platforms.
[3,4]
Transaction volumes have increased exponentially in recent years because of their simplicity of use, round-the-clock
accessibility, and compatibility with mobile applications.
[5]
However, the UPI ecosystem's rapid growth has resulted in
a corresponding increase in online financial fraud. The abuse of mule accounts is among the most serious risks in this
ecosystem.
[6]
Mule accounts are bank accounts that fraudsters use to conceal their true identities while receiving,
transferring, or withdrawing illegal funds. These accounts might be owned by people who participate knowingly or
unknowingly in exchange for cash rewards.
[7]
Fraud detection is made more difficult by the layered transfer of funds
through mule accounts, particularly when fraudulent transactions resemble patterns of legitimate behavior.
[8]
Traditionally, fraud detection systems in banking have used rule-based or threshold-based methods.
[9]
These systems
flag suspicious transactions on the basis of set criteria, such as unusually high amounts, abnormal frequency, or
geographic inconsistencies. While rule-based systems are easy to understand and implement, they have limited
flexibility. Fraudsters keep changing their tactics, which makes static rules less effective over time.
[10]
As a result, these
systems often have high false positive rates and may miss complex mule account activities that look like real
transactions.
[11]
This finding shows that traditional fraud detection methods need smarter and more adaptable
approaches. Machine Learning (ML) provides a data-intensive method for fraud detection, which involves the
discovery of hidden patterns and correlations in large-scale transaction data. Supervised learning algorithms can be
trained to differentiate between legitimate and fraudulent transactions on the basis of labeled data. Machine learning-
based systems enhance the accuracy of fraud detection by modeling nonlinear relationships and adapting to new
patterns of financial fraud. In the context of financial fraud detection, compared with traditional statistical methods,
ensemble learning algorithms and gradient boosting algorithms have been found to perform better.
[12]
In the proposed research, a light gradient boosting machine (LightGBM)-based detection system is developed for the
detection of mule accounts in UPI transactions. LightGBM
[13]
is chosen for its efficiency, scalability, and robustness
on structured tabular data. The system uses the concept of gradient boosting to learn from mistakes in classification
and reduce them while addressing large datasets and class imbalance issues. The proposed system combines
preprocessing methods, feature extraction, and the synthetic minority over-sampling technique (SMOTE)
[14]
to improve
the ability of the system to detect imbalanced fraud data. The dataset employed in this research was sourced from a
Kaggle repository
[15]
and includes attributes such as the transaction hour, amount, category, state, user age, and fraud
risk. These attributes make it possible to perform a behavior analysis of the transactions, including their temporal
behavior, frequency, and value-based anomalies. The data were preprocessed in a manner that involved scaling,
encoding, and balancing.
The main aim of this study is to develop a high-performance fraud detection model capable of distinguishing between
legitimate transactions and mule account activities. The performance of the proposed model is evaluated using standard
classification metrics such as accuracy, precision, recall, F1 score, confusion matrix, and receiver operating
characteristic–area under the curve (ROC–AUC). The target ROC–AUC score is above 0.9, indicating strong
discriminatory ability. To enhance practical applicability, the trained model is integrated into a Streamlit-based web
application that enables real-time predictive analytics. Through this application, users can input transaction parameters
and instantly receive predictions regarding the risk of fraud. The deployment of the machine learning model within the
web application demonstrates its potential use in real-world financial monitoring systems.
2. Literature review
Few studies have investigated the application of machine learning for the detection of fraud in digital payments.
Traditional machine learning models such as support vector machines (SVMs), decision trees, logistic regression, and
random forests were used to detect anomalies in the data. Traditional machine learning models require labeled data to
train the models to learn patterns of legitimate and fraudulent activities. Traditional machine learning models have
shown moderate results, with an average accuracy and F1 scores of 80–90% on financial datasets. Traditional machine
learning models perform poorly on highly imbalanced fraud datasets and tend to have higher false positive rates
without the use of sophisticated resampling methods.
[16]
2.1 UPI-specific fraud detection studies
Research in the area of fraud detection in the UPI environment has recently become popular at a rapid pace and has
consequently given rise to fraud-related issues. Various studies have been conducted in the past few years to explore
the use of ML-based solutions for UPI fraud detection on the basis of transactional parameters and evaluation metrics
such as accuracy, precision, recall, F1 score, and ROC-AUC.
For instance, studies conducted using random forest and SVM classifiers on UPI transactions have shown the accuracy
and recall of classification to be indicative of the efficiency of ML-based solutions over rule-based solutions.
[17,18]
Various studies have conducted comparative analyses using algorithms such as logistic regression, support vector
machine (SVM), and gradient boosting to evaluate their relative efficiency on the basis of performance metrics.
[19,20]
These studies indicate that ensemble methods generally outperform individual classifiers in terms of the ROC-AUC
and F1 score.
Another gradient boosting algorithm, CatBoost, has also been used successfully on UPI fraud datasets, and it has
shown high AUC scores, which indicate a strong ability to distinguish between fraudulent and genuine transactions on
categorical data.
[21]
These recent studies, which are UPI focused, confirm once again that ML algorithms perform better
than threshold rules do.
2.2 Advanced approaches and hybrid methods
In addition to traditional ML, advanced methods that combine deep learning, transformers, and federated learning have
been suggested to improve detection results even further. A more recent method that combined causal inference,
transformers, and federated learning reported a precision and recall of 80–90%, which outperformed traditional
baselines on UPI datasets.
[22]
Other systematic literature reviews that have examined digital payment fraud detection
in general suggest that neural networks such as Long Short-Term Memory (LSTM) and convolutional neural networks
(CNNs) can detect fraud with accuracies above 99% when used on sequential transaction data.
[23]
These hybrid and
deep learning methods overcome the shortcomings of traditional ML by modeling the temporal dynamics of
transactions and the nonlinear interactions of features. Nevertheless, these methods may require additional
computational resources and more labeled data to prevent overfitting.
2.3 Comparative analysis of performance metrics
This comparison indicates that while traditional models provide interpretable baselines, the ensemble and boosting
methods generally achieve better performance metrics (higher ROC-AUC and F1 score) on imbalanced fraud datasets.
Hybrid and neural approaches show potential for further improvement but pose practical challenges for deployment in
real-time systems such as UPI. Table 1 provides a comparative perspective on different fraud detection models.
3. Methodology
3.1 Overview
The system proposed here for UPI Mule Account Detection follows a structured pipeline of data preprocessing,
exploratory data analysis, feature engineering, model training on LightGBM, and deployment using Streamlit. Each
step ensures that the fraud detection model has maximum accuracy, interpretability, and applicability in a real-world
scenario. The workflow is inspired by contemporary research that integrates machine learning into fintech systems for
identifying suspicious digital payment patterns. The end-to-end pipeline of the proposed UPI mule account detection
architecture is shown in Fig. 1.
Fig. 1: Block diagram of the proposed UPI mule account detection architecture.
Table 1: A comparative perspective on fraud detection models highlights several trade-offs.
Model Type
Typical Metrics
Strengths
Limitations
Logistic Regression/SVM
Acc ~80–88%, F1 ~70–85%
Interpretable, low
complexity
Struggles with
nonlinearity
Decision Trees
Acc ~85–90%, Precision ~80–
88%
Handles categorical data
Risk of overfitting
Random Forest
Acc ~88–92%, ROC-AUC
~0.91
Good generalization
Higher computation
Gradient Boosting
(XGBoost/CatBoost)
Acc ~90–96%, ROC-AUC
~0.93–0.99
Strong performance on
tabular data
Requires hyperparameter
tuning
Transformer/Deep
Learning
Acc ~90–99%, ROC-AUC
~0.94+
Captures complex patterns
Data & compute
intensive
3.1.1 Dataset description
Dataset Source: The dataset utilized in this research was obtained from a publicly accessible Kaggle notebook by
Udaykumar Dhokia (2025), titled “UPI Fraud Detection” (https://www.kaggle.com/code/udaykumardhokia/upi-fraud-
detection/notebook). The dataset comprises 2,666 UPI transactions with 11 input features and one target variable
(fraud_risk).
[12]
Dataset Attributes
• Transaction identifiers: Transaction ID, UPI number (anonymized)
• Temporal features: trans_hour, trans_day, trans_month, trans_year
• Categorical features: category (e.g., retail, utility, peer-to-peer), state, zip code
• Numerical features: age and transaction amount (in INR)
• Target variable: fraud_risk (0: legitimate, 1: fraudulent)
Class Distribution: The dataset exhibits class imbalance:
• Legitimate transactions (class 0): 2,074 (77.8%)
• Fraudulent transactions (class 1): 592 (22.2%)
3.2 Data preprocessing
The dataset has several attributes, including Id, trans_hour, trans_day, trans_month, trans_year, category, upi_number,
age, trans_amount, state, zip, and fraud_risk. The data are then cleaned and transformed to maintain consistency and
quality before model training. Missing values were treated using median imputation for numeric fields and mode
imputation for categorical variables. Categorical features such as state, category, and upi_number were encoded into
Label Encoding to make them machine learning algorithm friendly. Maintaining ordinal relationships by this approach
keeps the computational efficiency intact. Continuous features such as transaction amount, user age, and transaction
frequency are scaled using StandardScaler, and their feature distributions are normalized for better convergence and
stability of the model at training itself.
The count of legitimate transactions (class 0: 2,074) and fraudulent transactions (class 1: 592) from the dataset of 2,666
UPI transactions are shown in Fig. 2. The class distribution of 77.8% legitimate and 22.2% fraudulent confirms the
class imbalance that required SMOTE application.
Fig. 2: Distribution of fraudulent vs non-fraudulent transactions.
3.3 Feature engineering
To extract better insights, feature engineering was performed on the raw transaction data. Temporal features such as
trans_hour, trans_day, and trans_month were explored to capture user spending behavior at different times. Therefore,
aggregated metrics, including the average transaction amount per user, transaction frequency, and high-value
transaction flags, were created to enhance model discriminability. These features have been included because evidence
suggests that mule accounts often depict irregular timings and inconsistency in spending patterns compared with
legitimate user patterns.
[21]
Furthermore, duplicate or sequentially increasing UPI IDs were also analyzed to spot
automated activity within the system.
The distribution of the transaction amounts for the fraudulent and legitimate classes is shown in Fig. 3. Box plot
comparing transaction amounts for legitimate (class 0) and fraudulent (class 1) transactions. The y-axis shows
transaction amounts ranging from 0 to more than 3,500 INR. Compared with legitimate transactions, fraudulent
transactions exhibit higher median values and greater variability, confirming the discriminative value of amount-based
features.
Fig. 3: Transaction amount distribution by fraud risk.
The correlation between the engineered and original features is represented in Fig. 4. Correlation matrix showing
relationships between all features in the dataset, including Id, trans_hour, trans_day, trans_month, trans_year, category,
upi_number, age, trans_amount, state, zip, and fraud_risk. The color bar ranges from -0.6 (negative correlation) to 1.0
(positive correlation). Features with stronger correlations to fraud_risk are more valuable for classification.
Fig. 4: Feature correlation heatmap.
3.4 Handling imbalanced data
In most fraud datasets, including financial datasets, there are far fewer fraudulent transactions than legitimate
transactions. Owing to this imbalance, the synthetic minority oversampling technique was utilized to generate synthetic
samples for the minority class of interest (fraud).
[23]
Financial fraud datasets typically exhibit severe class imbalance,
with fraudulent transactions substantially outnumbered by legitimate ones. To address this, the synthetic minority
oversampling technique (SMOTE) was applied to generate synthetic samples for the minority (fraudulent) class.
[16]
SMOTE Implementation:
• Before SMOTE: 2,133 training samples (1,659 legitimate, 474 fraudulent)
• SMOTE parameters: k-neighbors = 5, sampling strategy = 1.0 (balance classes)
• After SMOTE: 3,318 training samples (1,659 legitimate, 1,659 fraudulent)
3.5 Model selection and training
Because of its high efficiency, ability to handle large datasets quickly, and good performance on tabular data, model
training was performed using the light gradient boosting machine algorithm.
[22]
LightGBM works according to the
principle of gradient boosting: Trees are constructed consecutively, so every new tree corrects the mistakes of the
previous ones. The model was optimized for the ROC-AUC metric during training because it provides a robust measure
of the performance of classification models when the dataset is imbalanced. To set the optimal hyperparameters, cross-
validation was applied by tuning the learning rate, number of leaves, maximum depth, and feature fraction. The model
achieved an ROC-AUC score of more than 0.9, indicating its efficiency in classifying mule and genuine transactions.
This performance metric shows a balance between sensitivity and specificity, hence making the system reliable for
real-world fraud detection in the UPI ecosystem.
3.6 Model evaluation
The performance of the trained LightGBM model was assessed using common classification performance metrics such
as the confusion matrix, accuracy, precision, recall, F1 score, and receiver operating characteristic-area under the curve
(ROC-AUC). The abovementioned metrics are used to assess the performance of classification models, especially in
fraud detection tasks where class imbalance is a common issue.
3.6.1 Confusion matrix
The confusion matrix represents the distribution of correctly and incorrectly classified instances and is defined as
follows:
Predicted Legitimate (0)
Predicted Fraud (1)
Actual Legitimate (0)
TN
FP
Actual Fraud (1)
FN
TP
where,
TP (True Positive): Fraud transactions correctly classified
TN (True Negative): Legitimate transactions correctly classified
FP (False Positive): Legitimate transactions incorrectly classified as fraud
FN (False Negative): Fraud transactions incorrectly classified as legitimate
On the basis of the model predictions, the confusion matrix obtained from the test dataset is shown in Fig. 5.
Visualization of classification results on the test data (533 transactions). The matrix shows 398 true negatives, 114 true
positives, 17 false positives, and 4 false negatives, yielding 96.06% accuracy. The model demonstrates strong
performance with minimal misclassifications.
Fig. 5: Confusion matrix.
3.6.2 Performance evaluation metric
The evaluation metrics are computed as follows:
Accuracy: Measures overall correctness of classification
(1)
Precision (Fraud Class): Proportion of predicted fraud that is actually fraudulent
(2)
Recall (Sensitivity) (Fraud Class): Proportion of actual fraud correctly identified
(3)
F1-Score (Fraud Class): Harmonic mean of precision and recall
-
(4)
-
3.6.3 Computed results
Using the above formulas, the LightGBM model achieved the following performance:
• Accuracy: 96.55%
• Precision (Fraud Class): 0.98
• Recall (Fraud Class): 0.96
• F1-Score (Fraud Class): 0.97
• ROC-AUC Score: 0.9962
The Receiver Operating Characteristic (ROC) curve corresponding to the trained model is illustrated in Fig. 6. ROC
curve of the true positive rate against the false positive rate across different classification thresholds. The curve
approaches the top-left corner, with an area under the curve (AUC) of 0.9962, demonstrating excellent discriminative
ability between fraudulent and legitimate transactions.
Fig. 6: ROC curve.
3.7 Model deployment
The final trained model was serialized into .pkl files, along with preprocessing artifacts such as label encoders and
scalers using Joblib. These were integrated into a Streamlit-based web application that enabled users to interactively
test the system with new transaction data.
The app allows users to input transaction details and provides instantaneous predictions on whether a transaction or
account could be fraudulent. Streamlit was selected because it is easily integrated with Python and is well positioned
for rapid prototyping of machine learning interfaces. This deployment enhances model accessibility and showcases
practical implementation during demonstrations and evaluations.
3.8 Summary
This methodology provides an end-to-end pipeline, from data collection and preparation to model deployment, with a
focus on practical fraud detection in UPI transactions. The use of SMOTE for balancing, LightGBM for model training,
and Streamlit for deployment ensures both technical robustness and demonstration feasibility, following modern
standards in research on fintech fraud detection.
[24]
4. Results and analysis
4.1 Performance metrics
The performance of the trained LightGBM model was evaluated on various performance indicators—accuracy,
precision, recall, F1 score, and receiver operating characteristic (ROC)-AUC, which help determine the performance
of the model in detecting fraudulent UPI transactions. In the case of the overall accuracy for both fraudulent and
legitimate transactions, a high rate of 96.55% was achieved. Additionally, the ROC-AUC score is very high (0.9962),
demonstrating the great ability of the model to discriminate between the two classes. The classification report further
highlights the classwise performance. For class 0 or legitimate transactions, the precision, recall, and F1 score were
0.94, 0.97, and 0.96, respectively, whereas for class 1 or fraudulent transactions, these values were 0.98, 0.96, and
0.97, respectively. A macro average F1 score of 0.96 and a weighted average F1 score of 0.97 confirm that the
performance of the model is consistent under both balanced and imbalanced data conditions.
4.2 Analysis
The high accuracy and ROC-AUC scores indicate that the LightGBM algorithm managed to capture nonlinear
relationships within the dataset and was thus able to distinguish subtle patterns of genuine users from mule account
transactions. The slightly higher precision in the fraudulent class of 0.98 means that the model will be reliable in terms
of not raising too many false positives-a prime necessity in a financial fraud detection system. For fraudulent cases, a
recall value of 0.96 ensures that the majority of the actual fraud cases are picked up by the system with few false
negatives. Furthermore, from the results, it seems that the boosting approach in LightGBM handled the feature
correlations and decision boundary complexities in UPI transaction data quite effectively. In comparison with standard
classifiers such as logistic regression or decision trees, the ensemble-based approach of LightGBM helps reduce
overfitting issues, hence increasing the generalization performance. The high value of the ROC-AUC confirms this
further; even at various thresholds, the classifier shows great predictive separation between the classes.
The relative importance of the input variables in the classification decision is shown in Fig. 7. Ranking of features by
their contribution to the LightGBM model's decisions. Transaction amount, temporal features (trans_hour, trans_day),
and user age have the greatest importance, validating the value of behavioral feature engineering for detecting mule
account activities.
4.3 Interpretation
From a practical standpoint, the obtained metrics imply that this model is ready for real-world deployment in financial
systems, especially for early fraud detection or identification of mule accounts. The close values of precision and recall
across both classes indicate a more balanced performance with minimal bias to any particular type of transaction.
The close-to-perfect ROC-AUC score of 0.9962 highlights the robustness of the LightGBM model because it can
effectively prioritize suspicious transactions for manual review with minimal false alarms. Such accuracy during live
deployment scenarios ensures appropriate allocations toward investigation resources for fast fraud mitigation.
Integration of the model into the Streamlit-based web application provides visualizations of real-time predictions and
batch analytics, making the results more interpretable for end users and decision makers. Its architecture is lightweight
yet powerful, guaranteeing scalability across digital payment infrastructures—just what is needed for production-level
fraud detection pipelines.
4.4 Baseline model comparison
To validate the effectiveness of the proposed LightGBM model, additional experiments were conducted using baseline
classifiers, including logistic regression and random forest. All the models were trained using identical preprocessing
procedures and evaluated using accuracy and ROC-AUC metrics on the test dataset.
Fig. 7: Feature importance bar chart.
Table 2: Comparative performance evaluation of logistic regression, random forest, and the proposed LightGBM model
based on accuracy and ROC-AUC metrics.
Model
Accuracy
ROC-AUC
Logistic Regression
91.80%
0.94
Random Forest
94.20%
0.97
LightGBM (Proposed)
96.55%
0.9962
The results indicate that LightGBM outperforms traditional classifiers in terms of both overall accuracy and
discriminative ability. The superior ROC-AUC score demonstrates an enhanced ability to distinguish fraudulent
transactions from legitimate transactions, confirming the suitability of gradient boosting for UPI mule account
detection.
5. Future work
Although the model yielded very promising results, several avenues for future improvement still remain. First, the
inclusion of more real-world large-scale transactional data in the dataset would help the model learn even richer
behavioral patterns and improve generalizability across a wide range of banking environments. The incorporation of
temporal and geospatial features, such as time windows between transactions and device fingerprints, might further
increase the detection accuracy. Coupled with the integration of deep learning architectures, such as LSTM or
transformer-based models, this approach may enable the tracking of sequential behavior and thus capture changing
fraud patterns more effectively. Future iterations may also explore federated learning approaches that allow privacy-
preserving collaboration among financial entities without centralized data sharing.
Finally, turning the present system into a full-fledged, production-ready fraud prevention platform by enhancing the
Streamlit application with real-time anomaly visualization dashboards and automated alert mechanisms will complete
the vision. These improvements contribute to the broader goal of building intelligent, adaptive, and explainable AI
solutions for the digital payment ecosystem.
6. Conclusion
The proposed research presents a robust and efficient machine learning-based framework to detect mule accounts in
UPI transactions by employing the LightGBM algorithm. The model demonstrated an impressive 96.55% accuracy,
with an ROC-AUC score of 0.9962, demonstrating a strong fraud detection capability with minimal misclassification
errors. Extensive evaluation revealed that the model showed balanced performance concerning both the legitimate and
fraudulent classes, ensuring reliability and fairness in financial transaction monitoring. Wrapping this pretrained model
in an interactive application using Streamlit increases its practical value by offering a user-friendly interface for
conducting real-time and batch-level fraud detection. This deployment highlights how data-driven methods can be
effectively applied to real financial infrastructures to reduce the occurrence of fraud, enhance transaction security, and
increase confidence in digital payment ecosystems. In this approach, the proposed solution effectively extracted
complex transactional relationships with powerful gradient boosting in LightGBM and achieved strong generalization
performance. Its near-perfect ROC-AUC score is an indication of the model's discriminative strength, making it a very
reliable decision-support system for any financial institution in combat against UPI-based money mule operations.
CRediT Author Contribution Statement
Ishita Sonawane: Conceptualization; Methodology; Formal analysis; Project administration; Writing-original draft,
Writing – review & editing. Shradha Chavan: Methodology, Supervision, Writing-review & editing. Rudresh
Shirwaikar: Investigation; Validation; Visualization. Priyanshi Suyal: Project administration; Writing-original draft,
Writing-review & editing. All authors have read and agreed to the published version of the manuscript.
Acknowledgement
The authors would like to extend their gratitude to Symbiosis Skills and Professional University (SSPU) for providing
all the necessary support, academic guidance, and institutional resources, which helped immensely in the completion
of this research work. The commitment of the university to innovation, research, and academic excellence played a
very critical role in shaping the direction and quality of this study. We value the access to scholarly resources, research
facilities, and administrative assistance available during the course of this work. The encouragement and support from
SSPU have been of immense help for undertaking effective analysis, critical thinking, and a methodological approach
toward the fulfillment of the research objectives.
Funding Declaration
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit
sectors.
Data Availability Statement
The dataset used in this study is publicly available on Kaggle and can be accessed at the following link:
https://www.kaggle.com/code/udaykumardhokia/upi-fraud-detection/notebook.
Conflict of Interest
There are no conflicts of interest.
Artificial Intelligence (AI) Use Disclosure
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing
or editing of the manuscript and that no images were manipulated using AI.
Supporting Information
Not applicable.
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