IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2507.22908.html

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
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2507.22908
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lei, Hao & Gao, Renyuan & Ning, Chutong & Sun, Guanglin, 2025. "Green finance and corporate green innovation," Finance Research Letters, Elsevier, vol. 72(C).
    2. Li, Yu & Bai, Tianpeng & Sha, Ya & Xu, Zitong, 2025. "Concentration of supply chain, internal control, and corporate risk-taking," International Review of Financial Analysis, Elsevier, vol. 103(C).
    3. Wang, Mansi & Yu, Xinxin & Hong, Xinyi & Yang, Xiaotao, 2025. "Can digital finance promote green innovation collaboration in enterprises?," Global Finance Journal, Elsevier, vol. 65(C).
    4. Yin, Lei & Sun, Guanglin & Kong, Tao, 2025. "Regional big data development and corporate financial fraud," Pacific-Basin Finance Journal, Elsevier, vol. 90(C).
    5. Li, Lin & Xia, Qinqin & Du, Minzhe, 2024. "Regional banks, financing constraints and manufacturing enterprises' total factor productivity: A quasi-natural experiment of China's city commercial banks," Economic Analysis and Policy, Elsevier, vol. 84(C), pages 1652-1669.
    6. Wang, Peipei & Wang, Hongli, 2025. "Can digital financial development improve the accuracy of corporate earnings forecasts," Finance Research Letters, Elsevier, vol. 75(C).
    7. Liao, Hongji & Zhang, Jiarui & Gong, Runze & Zhang, Wen, 2025. "The mitigating effect of digital inclusive finance development on urban environmental pollution: Insights from innovation capacity and financing constraints," International Review of Financial Analysis, Elsevier, vol. 97(C).
    8. Liang, Yixin & Wei, Ran & Duan, Dingkang, 2025. "Digital financial development and commercial bank stability," International Review of Economics & Finance, Elsevier, vol. 97(C).
    9. Enkeleda LULAJ, 2025. "Digitalization As Catalysts Of Change In Finance, Accounting, And Reporting: Uncovering Symbiotic Relationships Among Financial Factors," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 20(1), pages 97-124, April.
    10. Shang, Zhaoyan & Chen, Hanwen, 2025. "Supply chain transparency and corporate financial fraud," Finance Research Letters, Elsevier, vol. 77(C).
    11. Guo, Xiaoxu & Wang, Yihan, 2025. "Effects of digital finance on enterprise total factor productivity: Evidence from the perspective of innovation level," Finance Research Letters, Elsevier, vol. 72(C).
    12. Li, Jing, 2025. "Corporate governance, fraud learning cycles, and financial fraud detection: Evidence from Chinese listed firms," Research in International Business and Finance, Elsevier, vol. 76(C).
    13. Wu, Ke & Zhang, Yongfang & Chen, Ying & Li, Miao, 2025. "Impact of digital finance on corporate ESG," International Review of Financial Analysis, Elsevier, vol. 104(PA).
    14. Zhang, Liming & Zhao, Yuting & Liu, Liang & Sun, Helin & Wang, Yue, 2025. "The spatial effects of green-innovation coupling and coordinated development from the perspective of digital inclusive finance: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 210(C).
    15. He, Yan & Lin, Chen, 2025. "Dynamic impact of media coverage on corporate risk management: Moderating effect of environmental policies and heterogeneity analysis," International Review of Economics & Finance, Elsevier, vol. 100(C).
    16. Saeed, Asif & Hamid, Samreen & Manita, Riadh & Suntraruk, Phassawan, 2025. "CSR decoupling and financial fraud: Unveiling the hidden nexus in US-listed firms," Research in International Business and Finance, Elsevier, vol. 75(C).
    17. Zhou, Yong & Liu, Siting & Song, Wei, 2025. "Bankruptcy court establishment and corporate financial fraud," International Review of Economics & Finance, Elsevier, vol. 100(C).
    18. Zhang, Ping & Wang, Yiru, 2025. "Digital finance and stock price crash: Evidence from China," Emerging Markets Review, Elsevier, vol. 66(C).
    19. Qi, Yu & Su, Hang, 2025. "Can artificial intelligence mitigate corporate fraud? Exploring the influence of institutional cross-holdings and financial misallocation," Pacific-Basin Finance Journal, Elsevier, vol. 92(C).
    20. Xia, Yun & Xie, Linling & Chen, Xiao & Lin, Ziang, 2025. "Artificial intelligence and corporate fraud: Evidence from China," Economic Analysis and Policy, Elsevier, vol. 86(C), pages 1391-1408.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2507.22908. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.