International Journal on Science and Technology

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CardSheild: A Credit Card Fraud Detection System

Author(s) Sayala Guru Preethika, Damala Sushma Sri, MD Aman Ahmed, P Meghana
Country India
Abstract This project presents a machine learning-based credit card fraud detection system using Supervised algorithms (Logistic Regression, Decision Trees, Random Forest, SVM, XGBoost, ANN) and Unsupervised techniques (Autoencoders, Isolation Forests) for anomaly detection. To address data imbalance, SMOTE, oversampling, and undersampling are applied. The workflow includes preprocessing, EDA, model training, and evaluation using Accuracy, Precision, Recall, and AUC-ROC, with hyperparameter tuning for optimization. The best model is deployed via a web app/API for real-time detection, integrating with banking systems. The system enhances accuracy, reduces false positives, and supports transparency with explainable AI, offering a scalable, secure solution for financial fraud prevention.
Keywords Credit Card Fraud Detection, Machine Learning ,Fraudulent Transactions, Supervised Learning, Random Forest, Logistic Regression, Support Vector Machine (SVM), Gradient Boosting, Data Preprocessing, Classification Algorithms, Anomaly Detection, Financial Security Predictive Modeling, Real-time Fraud Detection, Data Analytics, Model Evaluation Metrics, Precision and Recall ROC-AUC Curve, Web Application for Fraud Detection, Transaction Data Analysis
Field Engineering
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-05-12
DOI https://doi.org/10.71097/IJSAT.v16.i2.3921
Short DOI https://doi.org/g9kc8c
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