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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.22908 |
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| _version_ | 1866908689509646336 |
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| author | Sawaika, Abhishek Krishna, Swetang Tomar, Tushar Suggisetti, Durga Pritam Lal, Aditi Shrivastav, Tanmaya Innan, Nouhaila Shafique, Muhammad |
| author_facet | Sawaika, Abhishek Krishna, Swetang Tomar, Tushar Suggisetti, Durga Pritam Lal, Aditi Shrivastav, Tanmaya Innan, Nouhaila Shafique, Muhammad |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_22908 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection Sawaika, Abhishek Krishna, Swetang Tomar, Tushar Suggisetti, Durga Pritam Lal, Aditi Shrivastav, Tanmaya Innan, Nouhaila Shafique, Muhammad Computational Finance Artificial Intelligence Machine Learning I.2 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. |
| title | A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection |
| topic | Computational Finance Artificial Intelligence Machine Learning I.2 |
| url | https://arxiv.org/abs/2507.22908 |