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Credit Card Fraud Prediction System using Machine Learning Algorithms and Power BI Integration
Subject area: Science,Engineering and Technology · Area of research: Machine Learning, Data Science, Financial Analytic
DOI: https://doi.org/10.64388/IREV9I10-1717007
Abstract
With the rapid growth of digital payments and online transactions, credit card fraud has become a major concern for financial institutions and customers worldwide. This paper presents a Credit Card Fraud Prediction System using machine learning algorithms integrated with Power BI for effective visualization and decision-making. The system utilizes supervised learning techniques such as Logistic Regression, Random Forest, and Gradient Boosting to analyze historical transaction data and detect fraudulent activities. Data preprocessing techniques, including normalization and SMOTE, are applied to handle class imbalance and improve model performance. The models are evaluated using accuracy, precision, recall, and F1-score to ensure reliable detection. Furthermore, Power BI dashboards provide interactive visualization of fraud trends, transaction patterns, and risk analysis, enabling stakeholders to monitor and respond to suspicious activities in real time. The results demonstrate that the proposed system enhances fraud detection accuracy and provides a scalable and efficient solution for modern financial systems.
How to cite this paper
@article{1717007,
author = {Kuldeep, Sushant Ranjan, Priyanka Shukla},
title = {Credit Card Fraud Prediction System using Machine Learning Algorithms and Power BI Integration},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {3495-3498},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717007.pdf},
abstract = {With the rapid growth of digital payments and online transactions, credit card fraud has become a major concern for financial institutions and customers worldwide. This paper presents a Credit Card Fraud Prediction System using machine learning algorithms integrated with Power BI for effective visualization and decision-making. The system utilizes supervised learning techniques such as Logistic Regression, Random Forest, and Gradient Boosting to analyze historical transaction data and detect fraudulent activities. Data preprocessing techniques, including normalization and SMOTE, are applied to handle class imbalance and improve model performance. The models are evaluated using accuracy, precision, recall, and F1-score to ensure reliable detection. Furthermore, Power BI dashboards provide interactive visualization of fraud trends, transaction patterns, and risk analysis, enabling stakeholders to monitor and respond to suspicious activities in real time. The results demonstrate that the proposed system enhances fraud detection accuracy and provides a scalable and efficient solution for modern financial systems. },
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1717007}
}