QFDNN: A Resource-Efficient Variational Quantum Feature Deep Neural Networks for Fraud Detection and Loan Prediction

• URL: http://arxiv.org/abs/2504.19632v1
• Date: Mon, 28 Apr 2025 09:47:28 GMT
• Title: QFDNN: A Resource-Efficient Variational Quantum Feature Deep Neural Networks for Fraud Detection and Loan Prediction
• Authors: Subham Das, Ashtakala Meghanath, Bikash K. Behera, Shahid Mumtaz, Saif Al-Kuwari, Ahmed Farouk,
• Abstract summary: We propose a quantum feature deep neural network (QFDNN) to solve credit card fraud detection and loan eligibility prediction challenges.<n>QFDNN is resource efficient, noise-resilient, and supports sustainability through its resource-efficient design and minimal computational overhead.<n>Our findings highlight QFDNN potential to enhance trust and security in social financial technology by accurately detecting fraudulent transactions.
• Score: 22.867189884561768
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Social financial technology focuses on trust, sustainability, and social responsibility, which require advanced technologies to address complex financial tasks in the digital era. With the rapid growth in online transactions, automating credit card fraud detection and loan eligibility prediction has become increasingly challenging. Classical machine learning (ML) models have been used to solve these challenges; however, these approaches often encounter scalability, overfitting, and high computational costs due to complexity and high-dimensional financial data. Quantum computing (QC) and quantum machine learning (QML) provide a promising solution to efficiently processing high-dimensional datasets and enabling real-time identification of subtle fraud patterns. However, existing quantum algorithms lack robustness in noisy environments and fail to optimize performance with reduced feature sets. To address these limitations, we propose a quantum feature deep neural network (QFDNN), a novel, resource efficient, and noise-resilient quantum model that optimizes feature representation while requiring fewer qubits and simpler variational circuits. The model is evaluated using credit card fraud detection and loan eligibility prediction datasets, achieving competitive accuracies of 82.2% and 74.4%, respectively, with reduced computational overhead. Furthermore, we test QFDNN against six noise models, demonstrating its robustness across various error conditions. Our findings highlight QFDNN potential to enhance trust and security in social financial technology by accurately detecting fraudulent transactions while supporting sustainability through its resource-efficient design and minimal computational overhead.

Related papers

• Quantum-driven Zero Trust Framework with Dynamic Anomaly Detection in 7G Technology: A Neural Network Approach [0.0]
  We propose the Quantum Neural Network-Enhanced Zero Trust Framework (QNN-ZTF) for enhanced security.<n>We integrate Zero Trust Architecture, Intrusion Detection Systems, and Quantum Neural Networks (QNNs) for enhanced security.<n>We show improved cyber threat mitigation, demonstrating the framework's effectiveness in reducing false positives and response times.
  arXiv  Detail & Related papers  (2025-02-11T18:59:32Z)
• LEP-QNN: Loan Eligibility Prediction Using Quantum Neural Networks [4.2435928520499635]
  We propose a novel approach that employs Quantum Machine Learning (QML) for Loan Eligibility Prediction using Quantum Neural Networks (LEP-QNN)<n>Our innovative approach achieves an accuracy of 98% in predicting loan eligibility from a single, comprehensive dataset.<n>This research showcases the potential of QML in financial predictions and establishes a foundational guide for advancing QML technologies.
  arXiv  Detail & Related papers  (2024-12-04T09:35:03Z)
• Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis [50.18156030818883]
  Anomaly and missing data constitute a thorny problem in industrial applications. Deep learning enabled anomaly detection has emerged as a critical direction. The data collected in edge devices contain user privacy.
  arXiv  Detail & Related papers  (2024-11-06T15:38:31Z)
• QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection [4.2435928520499635]
  This study introduces the Quantum Federated Neural Network for Financial Fraud Detection (QFNN-FFD)<n>QFNN-FFD is a framework merging Quantum Machine Learning (QML) and quantum computing with Federated Learning (FL) for financial fraud detection.<n>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.
  arXiv  Detail & Related papers  (2024-04-03T09:19:46Z)
• Quantum Algorithms: A New Frontier in Financial Crime Prevention [0.0]
  The study showcases advanced methodologies such as Quantum Machine Learning (QML) and Quantum Artificial Intelligence (QAI) These quantum approaches leverage the inherent computational capabilities of quantum computers to overcome limitations faced by classical methods. Financial institutions can improve their ability to identify and mitigate risks, leading to more robust risk management strategies.
  arXiv  Detail & Related papers  (2024-03-27T07:52:10Z)
• Reconfigurable Intelligent Surface (RIS)-Assisted Entanglement Distribution in FSO Quantum Networks [62.87033427172205]
  Quantum networks (QNs) relying on free-space optical (FSO) quantum channels can support quantum applications in environments where establishing an optical fiber infrastructure is challenging and costly. A reconfigurable intelligent surface (RIS)-assisted FSO-based QN is proposed as a cost-efficient framework providing a virtual line-of-sight between users for entanglement distribution.
  arXiv  Detail & Related papers  (2024-01-19T17:16:40Z)
• Generative AI-enabled Quantum Computing Networks and Intelligent Resource Allocation [80.78352800340032]
  Quantum computing networks execute large-scale generative AI computation tasks and advanced quantum algorithms. efficient resource allocation in quantum computing networks is a critical challenge due to qubit variability and network complexity. We introduce state-of-the-art reinforcement learning (RL) algorithms, from generative learning to quantum machine learning for optimal quantum resource allocation.
  arXiv  Detail & Related papers  (2024-01-13T17:16:38Z)
• Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Models [0.0]
  The Quantum Support Vector model achieved the highest performance, with F1 scores of 0.98 0.98 for fraud and non-fraud classes. The article provides solutions to overcome current limitations and contributes new insights to the field of Quantum Machine Learning in fraud detection.
  arXiv  Detail & Related papers  (2023-08-09T21:47:50Z)
• Elastic Entangled Pair and Qubit Resource Management in Quantum Cloud Computing [73.7522199491117]
  Quantum cloud computing (QCC) offers a promising approach to efficiently provide quantum computing resources. The fluctuations in user demand and quantum circuit requirements are challenging for efficient resource provisioning. We propose a resource allocation model to provision quantum computing and networking resources.
  arXiv  Detail & Related papers  (2023-07-25T00:38:46Z)
• Potential and limitations of quantum extreme learning machines [55.41644538483948]
  We present a framework to model QRCs and QELMs, showing that they can be concisely described via single effective measurements. Our analysis paves the way to a more thorough understanding of the capabilities and limitations of both QELMs and QRCs.
  arXiv  Detail & Related papers  (2022-10-03T09:32:28Z)
• QSAN: A Near-term Achievable Quantum Self-Attention Network [73.15524926159702]
  Self-Attention Mechanism (SAM) is good at capturing the internal connections of features. A novel Quantum Self-Attention Network (QSAN) is proposed for image classification tasks on near-term quantum devices.
  arXiv  Detail & Related papers  (2022-07-14T12:22:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.