Analytical Examination of Fraud Detection Techniques in Netbanking and Credit Card Systems

Internet banking and credit card fraud are growing concerns for financial companies and their clients due to the exponential development of online transactions. Conventional fraud detection methods often fall behind the ever-evolving methods used by fraudsters. According to this research, the most effective strategy for enhancing fraud detection is to use mixed deep learning models. Gathering data is the initial stage, followed by preprocessing and feature selection. And lastly, it detects fraud using hybrid deep learning. Data preparation, including normalization, dimensionality reduction, and management of missing values, is necessary to guarantee that the model receives high-quality, usable input. The model selects the most discriminating features for fraud detection using filter and wrapper methods. Because of this, the model is now more precise and effective. To implement our method, we used a deep learning model that mixed convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders. With the use of each architecture's unique strengths, the hybrid model is able to better detect fraud by capturing intricate data patterns and relationships. We put the proposed method through its paces using datasets of real-time credit card transactions and online banking. We evaluate ML classifier and rule-based fraud detection methods with a hybrid deep learning model. The accuracy parameters are used to assess the proposed approach. In terms of efficiency and accuracy, we discovered that the hybrid deep learning model outperforms the conventional methods. By utilizing the intricacy of data associated with online banking and credit card transactions, the hybrid approach enhances fraud detection, assisting financial institutions in mitigating dangers and safeguarding their clients.