A Development of Advanced Credit Card Fraud Detection Through Deep Learning Approaches with Data Analytics for Secured Banking Application

Abstract

Credit Card Fauld Detection in Banking application technological advancements complicate cybercrime and hinder credit card fraud detection. Traditional methods are ineffective. A company developed an event-based temporal graph (ETG) to detail transactions among users, cards, devices, and accounts. This review examines machine learning algorithms for detecting financial fraud amid rising digital transactions. We examine many forms of fraud, such as money laundering, insurance fraud, credit card fraud, and financial statement fraud, as well as the unique detection difficulties associated with each new model Credit Card Fauld Detection Through Deep Learning Approaches with Data Analytics (CCFDDA). A method for credit card fraud detection that integrates deep learning and traditional machine learning with data preprocessing. Using a Kaggle dataset of 284, 807 transactions (492 fraudulent), we perform Principal Component Analysis (PCA) for feature anonymization and address class imbalance. Our analysis identifies key fraud indicators through statistical methods, and we enhance data completeness by removing incomplete rows, improving model learnability. In the Feature Normalization phase, Min-Max scaling normalizes numerical attributes to the range of 0 to 1 to mitigate scale bias. The cleaned transaction data is represented using overlapping sliding windows to maintain temporal dependencies. The machine-learning fraud detection component evaluates several models, including Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), XGBoost, CatBoost, and Logistic Regression (LR), to identify fraudulent transactions and addresses class imbalance through various classification and ensemble techniques. Softmax assigns class probabilities, while a class-weighted loss function addresses class imbalance. The architecture employs machine learning, statistical analysis, structured preprocessing, and temporal graph-based deep learning for fraud detection. Its Deep Learning module utilizes a Dynamic Graph Neural Network (DGNN) for time-dependent classification and message passing, enabling it to model changing transaction interactions over time for effective fraud detection.