{"ID":2894498,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.22908","arxiv_id":"2507.22908","title":"A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection","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.","short_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...","url_abs":"https://arxiv.org/abs/2507.22908","url_pdf":"https://arxiv.org/pdf/2507.22908v1","authors":"[\"Abhishek Sawaika\",\"Swetang Krishna\",\"Tushar Tomar\",\"Durga Pritam Suggisetti\",\"Aditi Lal\",\"Tanmaya Shrivastav\",\"Nouhaila Innan\",\"Muhammad Shafique\"]","published":"2025-07-15T17:29:12Z","proceeding":"q-fin.CP","tasks":"[\"q-fin.CP\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}