Paper: arXiv 2404.02595

Authors: Nouhaila Innan, Alberto Marchisio, Mohamed Bennai, Muhammad Shafique

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.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 5.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents an innovative integration of Quantum Machine Learning and Federated Learning for fraud detection, offering strong theoretical novelty. However, like many quantum computing finance papers, empirical validation is likely limited to simulated quantum environments or small datasets, impacting reproducibility and practical rigor.

Research Flowchart

  flowchart TD
    A[Research Goal: Secure & Accurate Financial Fraud Detection] --> B[Input: Distributed Financial & Transaction Data]
    B --> C[QFNN-FFD Framework: Quantum Machine Learning + Federated Learning]
    C --> D[Dual-Phase Training Across Distributed Clients]
    D --> E[Outcome: >95% Precision & Privacy-Preserving Fraud Detection]
    D --> F[Outcome: High Noise Resilience ~80% Accuracy]

Cited References (52)

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0 of 52 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.