Financial Fraud Detection with a Modified Quantum Vortex Search Algorithm for Enhanced CNN-based Classification

Abstract

Financial fraud detection is a critical challenge in the banking and financial sectors, where traditional methods often struggle to keep pace with evolving fraud tactics. This paper proposes a novel approach combining the power of Convolutional Neural Networks (CNNs) with a Modified Quantum Vortex Search Algorithm (MQVSA) to enhance the accuracy and efficiency of financial fraud detection systems. The MQVSA optimizes feature selection and model parameters, augmenting the CNN's ability to classify complex patterns indicative of fraud.