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A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection

Author

Listed:
  • Abhishek Sawaika
  • Swetang Krishna
  • Tushar Tomar
  • Durga Pritam Suggisetti
  • Aditi Lal
  • Tanmaya Shrivastav
  • Nouhaila Innan
  • Muhammad Shafique

Abstract

Rapid growth of digital transactions has led to a surge in fraudulent activities, challenging traditional detection methods in the financial sector. To tackle this problem, we introduce a specialised federated learning framework that uniquely combines a quantum-enhanced Long Short-Term Memory (LSTM) model with advanced privacy preserving techniques. By integrating quantum layers into the LSTM architecture, our approach adeptly captures complex cross-transactional patters, resulting in an approximate 5% performance improvement across key evaluation metrics compared to conventional models. Central to our framework is "FedRansel", a novel method designed to defend against poisoning and inference attacks, thereby reducing model degradation and inference accuracy by 4-8%, compared to standard differential privacy mechanisms. This pseudo-centralised setup with a Quantum LSTM model, enhances fraud detection accuracy and reinforces the security and confidentiality of sensitive financial data.

Suggested Citation

  • Abhishek Sawaika & Swetang Krishna & Tushar Tomar & Durga Pritam Suggisetti & Aditi Lal & Tanmaya Shrivastav & Nouhaila Innan & Muhammad Shafique, 2025. "A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection," Papers 2507.22908, arXiv.org.
    • Handle: RePEc:arx:papers:2507.22908
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    References listed on IDEAS

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    1. Li, Quan & Chen, Huimin & Chen, Yang & Xiao, Tong & Wang, Li, 2023. "Digital economy, financing constraints, and corporate innovation," Pacific-Basin Finance Journal, Elsevier, vol. 80(C).
    2. Zhou, Zhongsheng & Li, Zhuo, 2023. "Corporate digital transformation and trade credit financing," Journal of Business Research, Elsevier, vol. 160(C).
    3. Sun, Guanglin & Li, Ting & Ai, Yongfang & Li, Qinghai, 2023. "Digital finance and corporate financial fraud," International Review of Financial Analysis, Elsevier, vol. 87(C).
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