Toward Efficient Credit Card Fraud Detection: Leveraging Quantum Neural Networks and Modified Feature Selection Techniques
Authors: N Deepa , R Jayaraj , M Suguna , Nanduri Sireesha , Ramana Murthy Banda SNV , D Jebakumar Immanuel 
Published: January 2025

Credit cards are a common form of payment not only because they are extremely convenient to use but also because they are widely accepted. Credit cards are not only very easy to use, but they are also readily available. On account of the fact that it is so widely used, there is a substantial amount of concern regarding the protection of sensitive data from fraudulent activities and access by unauthorised individuals. For the purpose of preserving the trust and confidence of users, it is of the utmost importance to make certain that proper security measures are in place. Quantum machine learning (QML) is gaining popularity for classification applications, and a considerable number of the suggestions that have been made for it involve the utilisation of many qubits. This type of learning is becoming increasingly common. It is essential to make every effort to optimise the efficiency and effectiveness of each qubit before adding additional qubits. This should be done before adding more qubits. This is due to the fact that it is probable that these circuits will not always be able to function effectively in the generation of noisy intermediate-scale quantum (NISQ) systems. By utilising a single qubit, the objective of this research is to provide a description of a novel deep quantum neural network that is designed for classification purposes. In comparison to past studies, this network reduces the number of parameters by replicating various tactics that are frequently utilised in convolutional neural networks (CNNs). This is accomplished by reducing the number of parameters. The modified shuffle frog leaping algorithm, also known as MSFLA, is often utilised in order to decide which traits are the most significant while also lowering the amount of computing that is necessary. The purpose is to validate the concept of the first proposal and offer a tested framework for the later development of the application. This will be accomplished through the demonstration of the classification performance of the architecture that is based on a single qubit. Using a dataset that includes records of credit card transactions done by Europeans, the model is assessed in a setting that is reflective of the real world. This is accomplished by using the dataset. A number of components are included in the technique of the proposed model. These components include data pre-processing, feature engineering, ideal selection, evaluation and evaluation, and evaluation and evaluation. The usage of the computational resources provided by Google Colab allows for the training and testing of the model to be carried out with greater efficiency. When compared to individual classifiers, traditional machine learning approaches, and the model that was recommended, it was discovered that the proposed model was more effective in reducing the obstacles connected with detecting credit card fraud. This concluded that the proposed model was more effective. When compared to earlier models, the model that was suggested has a greater degree of performance in terms of accuracy, precision, recall, and F1-score performance characteristics. This is the case when those parameters are measured. The findings that have been provided here provide a foundation for the creation of fraud detection algorithms that are more resilient and flexible. This is something that will become increasingly required as the number of methods that credit card fraud is committed continues to expand.