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International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)

An Implementation of Quantum Machine Learning Technique to Determine Insurance Claim Fraud

Subodh Nath Pushpak, Sarika Jain

Published Oct 13, 2022• 11 citations
Publisher

Abstract

Quantum Machine Learning (QML) is an upcoming and active research field that uses quantum computing concepts for machine learning. This paper presents QML techniques for detecting fraud in housing insurance claims. Insurance fraud detection is challenging as the patterns may be complex. Therefore, various techniques and methods are employed to identify suspicious claims and prevent business losses due to these fraudulent claims. This paper explores Quantum Support Vector Machine (QSVM) techniques and feature engineering, feature selection, and parameter tweaking to identify fraudulent housing insurance claims. In addition, it compares Quantum SVM with classical SVM. Finally, this paper focuses on detecting property insurance claims fraud by using machine learning techniques.

DOI
10.1109/icrito56286.2022.9964828

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Quantum Machine Learning (QML) is an upcoming and active research field that uses quantum computing concepts for machine learning. This paper presents QML techniques for detecting fraud in housing insurance claims. Insurance fraud detection is challenging as the patterns may be complex. Therefore, various techniques and methods are employed to identify suspicious claims and prevent business losses due to these fraudulent claims. This paper explores Quantum Support Vector Machine (QSVM) techniques and feature engineering, feature selection, and parameter tweaking to identify fraudulent housing insurance claims. In addition, it compares Quantum SVM with classical SVM. Finally, this paper focuses on detecting property insurance claims fraud by using machine learning techniques.
This paper demonstrates a novel use of quantum machine learning (QML) algorithms for detecting fraudulent activities in the home insurance sector. Utilizing actual data and IBM Quantum processors through the Qiskit software stack, the study introduces an innovative method for selecting quantum features that are specifically designed to accommodate the limitations of Near Intermediate Scale Quantum (NISQ) technology by using the Quantum Support Vector Machine (QSVM) in conjunction with traditional machine learning techniques. A comprehensive comparison was conducted to evaluate their effectiveness in detecting fraud. The indicators such as accuracy, recall, and false positive rate are carefully analyzed. Despite the constraints of current quantum technology, QSVM shows excellent accuracy, especially on limited datasets, indicating its potential to enhance insurance fraud detection. The research emphasizes the crucial feature selection in optimizing QML algorithms for fraud detection tasks. It investigates the capacities of hybrid quantum/classical machine learning ensembles. Future research directions include expanding this study to actual hardware implementations to verify its practical feasibility. The work enhances financial security in the insurance business by using quantum computing technology in fraud detection approaches. It establishes the feasibility and efficacy of using quantum resources to solve difficult real-world issues, setting a foundation for or the broader application of QML in fraud detection and other fields.
Fraud detection in insurance system is a significant challenge because of the data imbalance and complex claim patterns. For these kinds of tasks classical machine learning models like support vector Machines have demonstrated very good performance. A promising paradigm that has surface recently for improving learning through quantum features is Quantum Machine Learning. In this research paper for the purpose of home insurance fraud detection a comparative study of the classical SVM with quantum support vector machines is done. A dataset with different features was used to evaluate the accuracy, false positives and recall. Qiskit ZZ Feature map and fidelity-based quantum kernels are used to implement the quantum support vector machines. The experimental results show that the QSVM achieves competitive accuracy but under class imbalance it struggles with fraud recall because of the limitations of quantum kernel methods. This paper gives the practical insights of Quantum Machine learning for the insurance fraud detection. Also, Explainable AI(XAI) technique was incorporated to analyze the fraud detections.
Healthcare fraud is a major issue in the medical and insurance industry, leading to significant financial losses and reduced trust in healthcare systems.Fraudulent claims include false billing, unnecessary treatments, and misuse of insurance services.Traditional fraud detection methods rely on manual verification and classical machine learning techniques, which often suffer from limited accuracy, high processing time, and difficulty in handling complex datasets.With the advancement of quantum computing, Quantum Machine Learning (QML) has emerged as a powerful solution that utilizes quantum properties such as superposition and entanglement to improve computational efficiency.This project focuses on developing a healthcare claim fraud detection system using quantum techniques like Quantum Support Vector Machine (QSVM) and Variational Quantum Classifier (VQC).The system analyzes patient and claim-related data such as age, medical history, billing details, and treatment records to identify fraudulent patterns.By encoding classical data into quantum states and processing it through quantum circuits, the model improves pattern recognition and classification accuracy.The proposed system integrates classical preprocessing with quantum algorithms to achieve better performance compared to traditional models, and experimental results show higher accuracy, faster processing, and improved efficiency, demonstrating the potential of quantum computing in healthcare fraud detection.In addition, the system reduces dependency on manual verification and increases automation in the fraud detection process.It also helps in identifying hidden patterns that are difficult to detect using traditional systems.The system contributes to improving decision-making processes in insurance companies.Overall, the proposed model provides a reliable and efficient solution for modern healthcare challenges.
Fraud detection is a Crucial aspect of securing financial systems and protecting against fraudulent activities. This paper presents a new method for Detecting fraud, combining quantum-inspired feature engineering and neural network modeling, which achieves a classification accuracy of 92%, with a precision and recall of 0.92 for non-fraudulent transactions. Utilizing QuantumCircuit from Qiskit, the approach encodes input features into quantum states through angle encoding, improving data representation and capturing complex feature interactions such as entanglement between qubits. The method employs a Quantum Neural Network (QNN) for classifying records and effectively identifying fraudulent transactions. Through real-world dataset experiments, we demonstrate the efficacy of the proposed approach, achieving a precision of 0.65 and recall of 0.68 for fraudulent transactions, while maintaining a false positive rate below 10%. This research leverages advanced machine learning techniques, including balanced sampling strategies to address class imbalance and hyperparameter tuning with Grid-SearchCV for optimal model performance. The paper presents a comprehensive evaluation of the approach, highlighting its strengths in adapting to dynamic and complex environments. Quantum-inspired methods offer the potential to significantly enhance fraud detection systems. However, challenges such as computational complexity and resource requirements remain areas for further investigation. Future research will aim to scale these models for larger datasets and investigate their applicability in fields such as healthcare and e-commerce. In conclusion, our work demonstrates the promising use of quantum-enhanced machine learning for fraud detection. This innovative approach paves the way for developing advanced detection systems that can effectively respond to the evolving landscape of fraudulent activities. Future studies could concentrate on refining these techniques and exploring their potential applications in other areas to further harness the advantages of quantum computing in machine learning.
Abstract: Financial fraud detection is a persistent challenge due to the dynamic, adversarial nature of fraudulent activities. This study investigates the potential of Quantum Machine Learning (QML) algorithms for pattern recognition in financial datasets characterized by high dimensionality and imbalance. Quantum Support Vector Machines (QSVM) and Variational Quantum Classifiers (VQC) were trained and tested using the Qiskit platform on a representative fraud dataset. Performance metrics were evaluated against classical benchmarks and analyzed using statistical and regression models. The findings highlight that QML offers meaningful performance enhancements, particularly in F1 score and recall, suggesting its viability for future adoption in financial surveillance systems. Keywords: Quantum Machine Learning, Financial Fraud Detection, Pattern Recognition, Quantum Computing, QSVM, VQC, Predictive Analytics
The rapid expansion of the fintech sector has brought with it an increasing demand for robust and sophisticated fraud detection systems capable of managing large volumes of financial transactions. Conventional machine learning (ML) approaches, while effective, often encounter limitations in terms of computational efficiency and the ability to model complex, high-dimensional data structures. Recent advancements in quantum computing have given rise to a promising paradigm known as quantum machine learning (QML), which leverages quantum mechanical principles to solve problems that are computationally infeasible for classical computers. The integration of QML with data science has opened new avenues for enhancing fraud detection frameworks by improving the accuracy and speed of transaction pattern analysis, anomaly detection, and risk mitigation strategies within fintech ecosystems. This paper aims to explore the potential of quantum-enhanced data science methodologies to bolster fraud detection and prevention mechanisms, providing a comparative analysis of QML techniques against classical ML models in the context of their application to financial data analysis. Fraud detection in fintech relies heavily on data-driven models to identify suspicious activities and prevent financial crimes such as identity theft, money laundering, and fraudulent transactions. Traditional ML approaches, such as decision trees, support vector machines, and deep learning, have laid the foundation for these systems. However, these approaches often fall short when faced with the challenges posed by high-dimensional, noisy, and complex financial data. Quantum machine learning, by leveraging quantum bits or qubits, possesses the unique ability to represent and process data in an exponentially larger state space, allowing for more efficient pattern recognition and computationally intensive analysis. Quantum algorithms such as the Quantum Support Vector Machine (QSVM), Quantum Principal Component Analysis (QPCA), and Quantum Neural Networks (QNNs) have been studied for their potential to outperform classical counterparts in specific problem domains, including fraud detection. This research delves into the theoretical foundations of quantum computing, outlining how quantum superposition, entanglement, and quantum interference can be harnessed to perform operations that exponentially accelerate data processing. Quantum algorithms are presented as capable of achieving faster data transformations and more nuanced pattern recognition through their ability to process all potential combinations of data simultaneously. The implementation of QML algorithms on quantum hardware, although still in its nascent stages, is beginning to demonstrate tangible benefits in terms of the speed and complexity of computations for fraud detection tasks. For example, quantum-enhanced anomaly detection can lead to the identification of rare, complex patterns that classical ML might overlook, contributing to a more proactive approach to fraud prevention. The paper also examines the integration of data science techniques with quantum-enhanced fraud detection, considering data preprocessing, feature engineering, and the application of quantum-enhanced statistical methods. Data preprocessing, a crucial step in building effective fraud detection models, involves the transformation and normalization of financial data to ensure that models can learn from relevant features without overfitting or underfitting. Quantum data structures offer the potential to represent data with a higher degree of complexity and interrelations, which is critical for capturing the multifaceted nature of financial transactions and detecting subtle signs of fraudulent activity. Quantum data encoding schemes such as Quantum Random Access Memory (QRAM) enable efficient storage and retrieval of data, providing a scalable solution for processing large datasets in real-time. A comprehensive analysis of case studies demonstrates the real-world applicability of quantum machine learning frameworks in fintech. The research highlights projects where quantum algorithms have been tested in controlled environments to detect anomalies in simulated transaction data, showcasing improvements in the identification of complex fraud scenarios over classical ML approaches. For instance, Quantum Support Vector Machines have been utilized to perform higher-dimensional classification tasks that are essential for distinguishing between legitimate and fraudulent transactions based on transaction history and user behavior. Furthermore, quantum algorithms that operate on hybrid systems, combining quantum and classical resources, are also explored to mitigate the limitations imposed by current quantum hardware, which is still constrained by issues such as noise and qubit coherence time. The paper also addresses key challenges and limitations associated with the integration of QML into practical fraud detection systems. Quantum hardware, although advancing rapidly, still faces significant challenges, including the need for error correction, qubit stability, and hardware scalability. Quantum computers with sufficient qubits and coherence time are necessary to implement complex algorithms for fraud detection effectively. Additionally, a practical approach to harnessing QML would require the development of quantum software frameworks and quantum programming languages that can operate in tandem with existing fintech systems and data infrastructure. Another area of focus is the synergy between quantum machine learning and classical machine learning models in creating hybrid systems that leverage the strengths of both methodologies. Quantum-enhanced feature extraction and dimensionality reduction can be combined with classical algorithms for final decision-making processes. This allows for a more comprehensive approach where quantum algorithms handle the computationally intensive parts of data analysis, while classical systems can be utilized for integrating real-time data and refining output for human interpretation. The paper discusses potential pathways for integrating these hybrid models, including considerations for API development, data interoperability, and the standardization of quantum-classical workflows. The discussion extends to the practical implications of implementing quantum-based fraud detection systems, particularly in terms of security and privacy. The use of quantum encryption and quantum key distribution can complement QML by ensuring that the data fed into fraud detection models is protected from external tampering. Quantum-resistant cryptography solutions are also explored, providing a comprehensive view of how quantum technologies could enhance the overall security posture of fintech ecosystems while promoting trust and compliance.