Jul 2026· International Research Journal on Advanced Engineering Hub (IRJAEH)· 0 citations· 16 references
TL;DR
The suggested framework employs a stacked ensemble approach in which Logistic_Regression, Decision_Tree, and XGBoost act as base models, while a meta-classifier produces the final fraud prediction.
Abstract
The increasing use of online financial services, including online banking, mobile payment platforms, UPI transactions, and electronic fund transfers, has significantly increased the risk of financial fraud in today's digital economy. Conventional rule-based fraud detection approaches are often ineffective in handling continuously changing fraud techniques, leading to considerable losses for both customers and financial organizations. To resolve these limitations, this work presents a Hybrid Financial_Fraud Detection System that combines multiple machine_learning techniques to achieve higher_detection accuracy and enable real-time transaction analysis. The suggested framework employs a stacked ensemble approach in which Logistic_Regression, Decision_Tree, and XGBoost act as base models, while a meta-classifier produces the final fraud prediction. The model evaluates several transaction-related parameters, including transaction amount, account balance, transaction frequency, average transaction value, account age, previous fraud history, transaction time, and indicators of foreign transactions. Customer details are automatically obtained from a customer database, allowing the system to perform transaction-specific and personalized risk assessment. A banking-oriented application_interface has been developed to enable users to carry out transactions through different payment channels such as UPI, bank_transfer, debit_card, credit_card, NEFT, RTGS, and IMPS. Each transaction is processed and analyzed instantly, assigned a fraud risk score, and categorized as either genuine or fraudulent. In addition, the system records transaction history, updates account balances, and provides a monitoring dashboard to support continuous fraud analysis. By integrating ensemble learning_techniques with behavioral transaction analysis and real-time monitoring, the proposed system improves the overall effectiveness of fraud detection. The framework is designed to strengthen transaction security, minimize financial_losses, and offer a scalable and intelligent solution for fraud prevention in modern digital financial environments.
An AI-Based Credit Card Fraud Detection System using Machine Learning to identify suspicious transactions accurately and in the real time and improves prediction accuracy through sequential learning and optimized decision trees.
P. Ravikumar, Gowrav A. S., A. N et al.· International Journal of Inn...· 0 citations
The rise of digital payments has magnified credit card fraud issues the complexity, scope and therefore the attack strategies have evolved to become an enormous obstacle for the traditional, static (rule based) and single- modal methods of ML in learning dynamic transactional patterns. In this paper we introduced a multimodal fraud detection approach combining the typical numerical features characterizing single transactional entities with their time- series transaction behaviour so that we could achieve higher accuracy in identifying frauds. The former represents properties for each transaction; temporal patterns in user spending are captured by a Gated Recurrent Unit (GRU), a network effectively modelling sequences of events (purchase history in our context) and then combined to finally perform binary classification in which either fraudulent or real transaction class is detected. We use the public domain 284,807 transaction records from credit card dataset; fraud transactions account for 0.172 of the samples. The experiment shows our novel framework (multimodal GRU) outperformed existing ML algorithms and single-mode techniques, having the recall reach 0.89, F1-score value 0.90 and AUC score 0.99. It’s highly scalable and efficient to use in real time monitoring applications. Our proposed method of combining two types of features: a set of single number characteristics and sequence-based information to successfully solve this challenging problem in modern finance.
J. J, S. S, Sabari Rr· 2026 International Conferenc...· 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
The fact that banks may find fraud, minimize risks before they happen, and make their clients happy by combining ML and CRM standard data models together in an effective manner is demonstrated.
Satyendra Kumar Vanapalli· International Journal of Mac...· 0 citations
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
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, P. Paul· International Journal of Dat...· 0 citations
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