Jul 2026· International Journal for Research in Applied Science and Engineering Technology· 0 citations
TL;DR
A UPI Fraud Detection System using the Random Forest machine learning algorithm that helps banks and digital payment platforms identify suspicious transactions quickly, reducing financial losses and improving user security.
Abstract
UPI fraud has become a major challenge due to the rapid growth of digital payments. Fraudulent activities such as
fake payment requests, phishing, identity theft, and unauthorized transactions can cause significant financial losses to users and
banks. Therefore, detecting fraud at an early stage is important to ensure secure online transactions. This project develops a UPI
Fraud Detection System using the Random Forest machine learning algorithm. The system is trained using a dataset that
contains different transaction details such as transaction amount, transaction time, payment method, location, device
information, and transaction history. The Random Forest algorithm is chosen because it provides high accuracy, handles large
datasets effectively, and reduces the chances of incorrect predictions. The model analyses transaction patterns and classifies
each transaction as either genuine or fraudulent. Its performance is evaluated using standard metrics such as accuracy,
precision, recall, and F1-score. The results show that the Random Forest algorithm performs better than many traditional
machine learning algorithms in detecting fraudulent transactions. The proposed system helps banks and digital payment
platforms identify suspicious transactions quickly, reducing financial losses and improving user security. In the future, the
system can be enhanced by integrating real-time transaction monitoring, user behaviour analysis, and advanced AI techniques
to improve fraud detection accuracy and provide a safer digital payment experience
Credit card fraud is a serious issue in the financial sector and can cause financial losses for customers, banks, and payment service providers. As digital transactions continue to grow, it has become increasingly important to detect fraudulent activity quickly and accurately. Traditional fraud detection systems often rely on predefined rules, which can make it difficult to recognize new or changing patterns of fraud. Machine learning offers another approach by learning patterns from previous transaction data and using those patterns to classify new transactions as legitimate or fraudulent. This research compares four machine learning techniques for credit card fraud detection: Logistic Regression, Random Forest, Support Vector Machine, and XGBoost. The proposed process includes data preprocessing, handling class imbalance, preparing features, training the models, and evaluating their performance. Accuracy, precision, recall, F1-score, and ROC-AUC are considered as evaluation measures. Particular attention is given to precision and recall because fraud datasets are usually highly imbalanced, with fraudulent transactions making up only a small part of all transactions. Overall, simple models such as Logistic Regression can provide an understandable baseline, while ensemble methods such as Random Forest and XGBoost can capture more complicated transaction patterns. The study shows that machine learning can be useful for fraud detection when it is combined with suitable preprocessing, class-imbalance techniques, and careful evaluation.
Jabulani Khumalo, Min Joon Kim· Global Knowledge Academy· 0 citations
ABSTRACT The rapid growth of Unified Payments Interface (UPI) transactions has increased the risk of online payment fraud. This project presents an ML-Based Real-Time UPI Fraud Detection System that uses machine learning algorithms to identify fraudulent transactions efficiently. The system preprocesses transaction data by handling missing values, encoding categorical features, and splitting the dataset for training and testing. Machine learning models such as Support Vector Machine (SVM), Random Forest, and AdaBoost are trained to classify transactions as genuine or fraudulent. The models are evaluated using accuracy, confusion matrix, and classification metrics. Experimental results show that the Random Forest algorithm achieves the highest accuracy, making it the most effective model for fraud detection. The proposed system enhances the security of digital payment platforms by enabling real-time fraud detection, reducing financial losses, and improving the reliability of UPI transactions.
Keywords: UPI, Fraud Detection, Machine Learning, Random Forest, Real-Time Detection, Digital Payments.
Yekkirala Suvarcha, D. B M, Dr. Gattu Prasad· International Scientific Jou...· 0 citations
The rapid growth of digital banking, online transactions, and the electronic payment of the systems has been
significantly increased the use of credit cards, results in the parallel rise in fraudulent activities. Traditional methods of
detecting the fraud are often unable to handle large-scale transaction data and evolving fraud patterns efficiently. This paper
presents an AI-Based Credit Card Fraud Detection System using Machine Learning to identify suspicious transactions
accurately and in the real time. To identify patterns and detect potential fraud, the proposed system analyzes past
transaction data containing various transaction-related attributes amount, merchant details, category, time, and customer
information. Data preprocessing techniques including K-Nearest Neighbor (KNN) imputation are applied to handle missing
values, followed by label encoding and Z-score normalization to improve data quality and consistency. Feature engineering
is performed to extract meaningful transaction patterns. The processed data is then classified using the XGBoost algorithm,
which improves prediction accuracy through sequential learning and optimized decision trees. Experimental evaluation is
carried out using a credit card transaction dataset containing legitimate and fraudulent records.
P. Ravikumar, Gowrav A. S., A. C. N. et al.· International Journal of Inn...· 0 citations
Financial transaction fraud is an ongoing threat with significant economic loss and on customers' trust. This paper discusses Fraud Detection in detail with machine learning technique on a given data set of a transaction. We investigate the patterns revealed from the users and the transactions in the database when the user is performing fraudulent transactions, and test several classification models that can be used to detect fraud, which includes logistic regression model, random forests, support vector machines, gradient boosting, and neural networks. The performance of the models is investigated in terms of accuracy, precision, recall, F1 score and ROC-AUC metrics. Based on our experiments, the best detection overall performances are obtained for the tree-based ensemble models (Random Forest and XGBoost) with XGBoost getting the most optimum fraud Recall and F1-Score. Through the data analysis results (such as account age, transaction frequency etc.) and the model comparison, we expound an improved method which is based on combining the ensemble of best models with data imbalance countermeasures to increase the recall of fraudulent cases. We also have an end-to-end machine learning pipeline on Python to detect frauds from preprocessing the data, training the models, evaluating them, and deploying for fraud prediction. Also, a literature review of twenty-five recent studies on fraud detection is given, and the algorithms used, datasets and major contributions of these studies were summarized. The textbook ensemble technique, as proposed gives better fraud detection performance as it gains on the order of ~3-5% improvement against the best single model performance on F1-score, with acceptable precision, thereby underscoring the usefulness of hybrid modeling with specialized techniques for this field. The results emphasize that utilizing various models and domain-specific feature engineering can be of great benefit in fraudulent transaction detection, while also neg
Financial fraud has emerged as one of the most significant challenges in the modern digital economy due to the rapid growth of online banking, mobile payments, electronic commerce, and digital financial services. Traditional fraud detection systems primarily rely on predefined rules and expert-generated patterns; however, such approaches often fail to identify newly emerging fraud techniques and sophisticated fraudulent behaviors. Consequently, there is an increasing need for intelligent and adaptive fraud detection systems capable of learning from historical transaction data and identifying suspicious activities with high accuracy.
This research presents an intelligent machine learning-based approach for fraud detection in financial transactions. The study investigates and compares the effectiveness of multiple machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN), in detecting fraudulent financial activities. The proposed methodology incorporates data preprocessing, feature engineering, handling class imbalance, model training, and comprehensive performance evaluation.
A publicly available credit card transaction dataset is utilized for experimental analysis. Data preprocessing techniques such as normalization, missing value handling, and feature selection are applied to improve model performance. Since fraud datasets are typically highly imbalanced, class balancing techniques are incorporated to enhance the detection of minority fraudulent transactions.
The performance of the selected algorithms is evaluated using Accuracy, Precision, Recall, F1-Score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC).
Comparative analysis demonstrates that ensemble learning methods, particularly Random Forest and XGBoost, outperform traditional classification techniques by achieving higher detection rates and lower false positive rates. Furthermore, the study highlights the practical applicability of machine learning models in real-world financial environments where rapid and accurate fraud detection is essential.
The findings of this research contribute to the growing field of financial fraud analytics by providing a comparative framework for evaluating machine learning algorithms under consistent experimental conditions. The proposed approach offers valuable insights for financial institutions, banking organizations, and cybersecurity professionals seeking to enhance fraud prevention systems and minimize financial losses.
Keywords: Fraud Detection, Machine Learning, Financial Transactions, Random Forest, XGBoost, Artificial Neural Networks, Classification, Financial Security.
Dr. Abdul Majid Farooqi Dr. Abdul Majid Farooqi, Laiba Khan LAIBA KHAN· International Scientific Jou...· 0 citations
The rapid growth of digital banking has increased the number of online transactions, making fraud detection an important challenge for financial institutions. This project presents a real-time bank transaction fraud detection system that combines Apache Kafka with Machine Learning to identify suspicious transactions as they occur. Apache Kafka is used to stream transaction data continuously, while a Random Forest classifier analyzes each transaction and predicts whether it is genuine or fraudulent. Before training the model, the transaction dataset is cleaned, encoded, and normalized to improve prediction accuracy. A Flask-based web application provides an easy-touse interface for generating transaction streams and displaying fraud detection results in real time. The proposed system reduces the delay associated with traditional batch-processing methods and enables faster decision-making. Its modular design also allows the system to handle increasing transaction volumes, making it suitable for modern banking environments that require secure, reliable, and scalable fraud detection solutions.
Thakur Nivedita, Singh, Dr.P.JOHN Paul· International Journal of Dat...· 0 citations