Related papers: A Data Balancing and Ensemble Learning Approach fo…

Credit card fraud detection is a critical challenge in the financial sector, demanding sophisticated approaches to accurately identify fraudulent transactions. This research proposes an innovative methodology combining Neural Networks (NN)…

Spurious credit card transactions are a significant source of financial losses and urge the development of accurate fraud detection algorithms. In this paper, we use machine learning strategies for such an aim. First, we apply a mixed…

Machine learning has automated much of financial fraud detection, notifying firms of, or even blocking, questionable transactions instantly. However, data imbalance starves traditionally trained models of the content necessary to detect…

In the era of the digitally driven economy, where there has been an exponential surge in digital payment systems and other online activities, various forms of fraudulent activities have accompanied the digital growth, out of which credit…

Credit scoring is vital in the financial industry, assessing the risk of lending to credit card applicants. Traditional credit scoring methods face challenges with large datasets and data imbalance between creditworthy and non-creditworthy…

The rise of digital payments has accelerated the need for intelligent and scalable systems to detect fraud. This research presents an end-to-end, feature-rich machine learning framework for detecting credit card transaction anomalies and…

Machine learning has opened up new tools for financial fraud detection. Using a sample of annotated transactions, a machine learning classification algorithm learns to detect frauds. With growing credit card transaction volumes and rising…

The credit card has become the most popular payment method for both online and offline transactions. The necessity to create a fraud detection algorithm to precisely identify and stop fraudulent activity arises as a result of both the…

Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions. In this…

Credit card fraud remains a significant challenge due to class imbalance and fraudsters mimicking legitimate behavior. This study evaluates five machine learning models - Logistic Regression, Random Forest, XGBoost, K-Nearest Neighbors…

In an increasingly digitalized commerce landscape, the proliferation of credit card fraud and the evolution of sophisticated fraudulent techniques have led to substantial financial losses. Automating credit card fraud detection is a viable…

Addressing class imbalance is a central challenge in credit card fraud detection, as it directly impacts predictive reliability in real-world financial systems. To overcome this, the study proposes an enhanced workflow based on the…

Financial institutions and businesses face an ongoing challenge from fraudulent transactions, prompting the need for effective detection methods. Detecting credit card fraud is crucial for identifying and preventing unauthorized…

Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions. In this article,…

With the proliferation of various online and mobile payment systems, credit card fraud has emerged as a significant threat to financial security. This study focuses on innovative applications of the latest Transformer models for more robust…

Credit card fraud is an ongoing problem for almost all industries in the world, and it raises millions of dollars to the global economy each year. Therefore, there is a number of research either completed or proceeding in order to detect…

Various problems of any credit card fraud detection based on machine learning come from the imbalanced aspect of transaction datasets. Indeed, the number of frauds compared to the number of regular transactions is tiny and has been shown to…

With growing credit card transaction volumes, the fraud percentages are also rising, including overhead costs for institutions to combat and compensate victims. The use of machine learning into the financial sector permits more effective…

In the era of big data, the utilization of credit-scoring models to determine the credit risk of applicants accurately becomes a trend in the future. The conventional machine learning on credit scoring data sets tends to have poor…

Fraud detection is a challenging task due to the changing nature of fraud patterns over time and the limited availability of fraud examples to learn such sophisticated patterns. Thus, fraud detection with the aid of smart versions of…