Author

Credit card fraud has emerged as one of the most critical threats in today’s digital payment ecosystem, leading to substantial financial losses and compromising customer trust. Traditional fraud detection mechanisms, while effective to a certain extent, often struggle to manage the challenges of highly imbalanced transaction data, the dynamic nature of fraudulent patterns, and the necessity for real-time detection. This project proposes an intelligent fraud detection framework that leverages the Random Forest algorithm, a robust ensemble learning method known for its high accuracy and resilience against overfitting. The model is trained and validated on a benchmark credit card transaction dataset, where it efficiently learns hidden fraud patterns from skewed data distributions. To enhance the practicality of the solution, the system is integrated with an automated notification mechanism that promptly alerts stakeholders, such as financial institutions and customers, when suspicious or anomalous activities are detected. This real-time alerting feature not only helps mitigate immediate risks but also supports proactive decision-making in fraud prevention. Experimental results demonstrate that the proposed model achieves high accuracy, precision, and recall compared to conventional approaches, ensuring minimal false alarms while maximizing fraud detection rates. Furthermore, the system is designed to be scalable and adaptable, making it suitable for deployment in diverse financial environments. Overall, this project presents a reliable, efficient, and user-centric fraud detection system that significantly contributes to safeguarding digital transactions in the evolving financial landscape.

This project titled “Credit Card Fraud Detection Using Random Forest and Notifications” successfully demonstrates the application of machine learning techniques to detect fraudulent credit card transactions efficiently and accurately. With the increasing adoption of online payment systems, fraud detection has become a critical requirement for financial institutions. Traditional rule-based systems are no longer sufficient due to their inability to adapt to evolving fraud patterns. The proposed system utilizes the Random Forest algorithm, which effectively handles large, complex, and highly imbalanced datasets. Through proper data preprocessing, model training, and evaluation, the system achieved high accuracy (approximately 99%), along with strong precision, recall, and ROC-AUC values. This confirms the reliability and robustness of the model in distinguishing between legitimate and fraudulent transactions. Additionally, the integration of an automated email notification system enhances real-time fraud prevention by immediately alerting users or banks when suspicious activity is detected. This proactive approach helps reduce financial loss and improves customer trust. Overall, the project meets its objectives by providing a scalable, accurate, and efficient fraud detection system, making it suitable for deployment in real-world banking and fintech environments.

1.Dal Pozzolo, A., Bontempi, G., Snoeck, M., & Snoeck, C., “Adapting machine learning models to concept drift in credit card fraud detection,” IEEE Intelligent Systems, 2014. 2.Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J.C., “Data mining for credit card fraud: A comparative study,” Decision Support Systems, 2011. 3.Whitrow, C., et al., “Transaction aggregation as a strategy for credit card fraud detection,” Data Mining and Knowledge Discovery, 2009. 4.Kaggle Dataset – Credit Card Fraud Detection https://www.kaggle.com/mlg-ulb/creditcard 5.Scikit-learn Documentation https://scikit-learn.org Python Official Documentation https://docs.python.org

IJORET Paper Submission Call for Papers About IJORET Scope of the Journal Review Process Online Submission (Form)