Detection of Credit Card Frauds Using Machine Learning Techniques

Abstract

The popularity of credit cards in the modern world is associated with the growing rate of credit card fraud. Credit card technology has made e-shopping easier, but at the same time increased the possibility of fraudulent activities. Computer algorithms such as machine learning can help in identifying fraudulent activities. Machine learning is very effective in analyzing customer information. Recently, the number of credit card fraud cases has been constantly growing and has caused significant losses for customers, retailers, and banks. In the proposed research paper, various methods of identification of credit card fraud detection via machine learning will be analyzed and compared according to their effectiveness.

Introduction

The text explains credit card fraud detection using machine learning, focusing on increasing online transaction security and addressing the growing problem of fraudulent activities such as unauthorized usage, abnormal transactions, and expired card misuse. It highlights that credit card fraud is difficult to detect due to imbalanced datasets, evolving fraud patterns, and the dynamic nature of online transactions.

Machine learning is presented as the most effective solution, using both supervised learning (trained on labeled fraud/legit data) and unsupervised learning (detecting anomalies in spending behavior). The literature review compares multiple algorithms such as Logistic Regression, SVM, KNN, Naïve Bayes, Random Forest, and deep learning models, showing that Random Forest, ANN, and ensemble methods generally achieve the highest accuracy, often above 95–99%. However, challenges like class imbalance, false positives, and computational complexity remain.

The problem statement identifies that existing systems struggle with imbalanced datasets, leading to incorrect or delayed fraud detection. To address this, the study proposes an optimized Random Forest-based model with improved tuning and pipeline processing.

The objectives include reducing financial losses, building a user-friendly system, ensuring maintainability through modular design, and enabling real-time fraud detection.

The methodology involves collecting continuous transaction data (amount, time, merchant, location, user behavior), followed by preprocessing steps such as cleaning missing values, encoding categorical data, and normalization. These processed inputs are then used for machine learning-based classification to detect fraudulent transactions.

Conclusion

In conclusion, the main objective of this project is to find the most suited model for credit card fraud detection in terms of the machine learning techniques chosen for the project, Explored various computer methods for detecting fraudulent credit card transactions. Evaluated performance using metrics like accuracy, precision, and recall. Choose Random Forest Algorithm as the preferred method which scored 95.94% accuracy. Described it as a smart helper for identifying suspicious transactions. It emphasized the goal of ensuring the security of financial transactions. I believe that using this model will help to decrease the amount of credit card fraud and increase the customer\'s satisfaction as it will provide them with a better experience in addition to feeling secure.

References

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Copyright © 2026 Riya Wagh, Reena Kharat. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.