A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

Aim:

     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. The main aim is to detect fraudulent transactions using credit cards with the help of ML algorithms and deep learning algorithms.

Abstract:

     Credit card fraud remains a pervasive issue, leading to substantial financial losses for both financial institutions and cardholders. To combat this threat effectively, this study presents a novel approach to credit card fraud detection using a deep learning ensemble coupled with data resampling techniques. The proposed system combines multiple deep learning models to enhance the classification of fraudulent transactions, while employing resampling methods to address the class imbalance prevalent in credit card transaction data. Through extensive experimentation and evaluation on diverse datasets, our ensemble demonstrates notable improvements in fraud detection accuracy, outperforming single-model approaches and conventional sampling techniques. The results reveal the system’s robustness in identifying fraudulent activities while minimizing false alarms, providing a valuable tool for financial security and risk mitigation in today’s digital transaction landscape.

Existing System:

           The relevant literature present many machines learning based approaches for credit card detection, such as Decision Tree classifier, K-Nearest neighbors, Random Forest classifier, Support Vector classifier, Logistic Regression and XG Boost, which results low accuracy. In 2020, there were 393,207 cases of CCF out of approximately 1.4 million total reports of identity theft. CCF is now the second most prevalent sort of identity theft recorded as of this year, only following government documents and benefits fraud. In 2020, there were 365,597 incidences of fraud perpetrated using new credit card accounts. The number of identity theft complaints has climbed by 113% from 2019 to 2020, with credit card identity theft reports increasing by 44.6%. Payment card theft cost the global economy $24.26 billion last year. With 38.6% of reported card fraud losses in 2018, the United States is the most vulnerable country to credit theft.

Problem Definition:

       By proposing Machine learning Algorithms, based approaches for credit card detection, such as Extreme Learning Method, Decision Tree classifier, K-Nearest neighbors, Random Forest classifier, Support Vector classifier, Logistic Regression and XG Boost. The model results leds to low accruracy.

Proposed System:

          Deep learning (DL) algorithms applied applications in computer network, intrusion detection, banking, insurance, mobile cellular networks, health care fraud detection, medical and malware detection, detection for video surveillance, location tracking, Android malware detection, home automation, and heart disease prediction. we explore DL Algorithms to identify credit card thefts in the banking industry in this model. It uses a number of deep learning algorithms for detecting CCF. However, in this model, we choose the CNN model and its layers to determine if the original fraud is the normal transaction of qualified datasets.