QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection

TL;DR

QFNN-FFD combines quantum machine learning and federated learning to create a secure, high-performance framework for financial fraud detection, achieving over 95% precision and robust accuracy in a privacy-preserving manner.

Contribution

It introduces a novel quantum federated neural network framework that enhances privacy, performance, and robustness for financial fraud detection, setting a new benchmark in secure quantum-enabled financial technology.

Findings

Precision rates above 95% achieved.

Maintains 80% accuracy under noise.

Demonstrates robustness and security advantages.

Abstract

This study introduces the Quantum Federated Neural Network for Financial Fraud Detection (QFNN-FFD), a cutting-edge framework merging Quantum Machine Learning (QML) and quantum computing with Federated Learning (FL) for financial fraud detection. Using quantum technologies' computational power and the robust data privacy protections offered by FL, QFNN-FFD emerges as a secure and efficient method for identifying fraudulent transactions within the financial sector. Implementing a dual-phase training model across distributed clients enhances data integrity and enables superior performance metrics, achieving precision rates consistently above 95%. Additionally, QFNN-FFD demonstrates exceptional resilience by maintaining an impressive 80% accuracy, highlighting its robustness and readiness for real-world applications. This combination of high performance, security, and robustness against noise positions QFNN-FFD as a transformative advancement in financial technology solutions and establishes it as a new benchmark for privacy-focused fraud detection systems. This framework facilitates the broader adoption of secure, quantum-enhanced financial services and inspires future innovations that could use QML to tackle complex challenges in other areas requiring high confidentiality and accuracy.