Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms

IEEE BASE PAPER ABSTRACT:

People can use credit cards for online transactions as it provides an efficient and easy-to-use facility. With the increase in usage of credit cards, the capacity of credit card misuse has also enhanced. Credit card frauds cause significant financial losses for both credit card holders and financial companies. In this research study, the main aim is to detect such frauds, including the accessibility of public data, high-class imbalance data, the changes in fraud nature, and high rates of false alarm. The relevant literature presents many machines learning based approaches for credit card detection, such as Extreme Learning Method, Decision Tree, Random Forest, Support Vector Machine, Logistic Regression and XG Boost. However, due to low accuracy, there is still a need to apply state of the art deep learning algorithms to reduce fraud losses. The main focus has been to apply the recent development of deep learning algorithms for this purpose. Comparative analysis of both machine learning and deep learning algorithms was performed to find efficient outcomes. The detailed empirical analysis is carried out using the European card benchmark dataset for fraud detection. A machine learning algorithm was first applied to the dataset, which improved the accuracy of detection of the frauds to some extent. Later, three architectures based on a convolutional neural network are applied to improve fraud detection performance. Further addition of layers further increased the accuracy of detection. A comprehensive empirical analysis has been carried out by applying variations in the number of hidden layers, epochs and applying the latest models. The evaluation of research work shows the improved results achieved, such as accuracy, f1-score, precision and AUC Curves having optimized values of 99.9%,85.71%,93%, and 98%, respectively. The proposed model outperforms the state-of-the-art machine learning and deep learning algorithms for credit card detection problems. In addition, we have performed experiments by balancing the data and applying deep learning algorithms to minimize the false negative rate. The proposed approaches can be implemented effectively for the real-world detection of credit card fraud.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

Gradient Boosting Classifier.

OUR PROPOSED ABSTRACT:

Credit cards are now potentially the most popular mode of payment for both offline and online purchases thanks to new developments in electronic commerce systems and communication technology; as a result, there is much more fraud involved with such transactions. Every year, fraudulent credit card transactions cause businesses and individuals to lose a lot of money, and con artists are constantly looking for new tools and techniques to commit fraud.

Researchers face a difficult task when trying to identify credit card theft since criminals are quick-thinking and inventive. The dataset provided for credit card fraud detection is severely unbalanced, making it difficult for the system to detect fraud. The use of credit cards is quite important in today’s economy. It is an essential component of every family, company, and global enterprise.

While using credit cards responsibly and safely can have many benefits, engaging in fraudulent behaviour can have a negative impact on your credit and finances. There have been several solutions proposed to address the escalating credit card theft. The increased use of electronic payments is now significantly impacted by the detection of fraudulent transactions. As a result, methods that are efficient and effective for identifying fraud in credit card transactions are required.

Gradient Boosting Classifier, a machine learning methodology, is suggested in this research as a smart method for identifying fraud in credit card transactions. The experimental results show that the suggested approach worked better than other machine learning algorithms and reached the maximum accuracy performance, with training accuracy of 100% and test accuracy of 91%.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.
Hard Disk : 500 GB.
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
Ram : 4 GB

SOFTWARE REQUIREMENTS:

Operating System : Windows 10 / 11.
Coding Language : Python 3.8.
Web Framework : Flask.
Frontend : HTML, CSS, JavaScript.

REFERENCE:

FAWAZ KHALED ALARFAJ, IQRA MALIK, HIKMAT ULLAH KHAN, NAIF ALMUSALLAM, MUHAMMAD RAMZAN, AND MUZAMIL AHMED, “Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms”, IEEE Access (Volume: 10), 2022.

Python IEEE Projects

Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms