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.
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.
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.
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 presents a first end-to-end application of a Quantum Support Vector Machine (QSVM) algorithm for a classification problem in the financial payment industry using the IBM Safer Payments and IBM Quantum Computers via the Qiskit software stack. Based on real card payment data, a thorough comparison is performed to assess the complementary impact brought in by the current state-of-the-art Quantum Machine Learning algorithms with respect to the Classical Approach. A new method to search for best features is explored using the Quantum Support Vector Machine's feature map characteristics. The results are compared using fraud specific key performance indicators: Accuracy, Recall, and False Positive Rate, extracted from analyses based on human expertise (rule decisions), classical machine learning algorithms (Random Forest, XGBoost) and quantum-based machine learning algorithms using QSVM. In addition, a hybrid classical-quantum approach is explored by using an ensemble model that combines classical and quantum algorithms to better improve the fraud prevention decision. We found, as expected, that the results highly depend on feature selections and algorithms that are used to select them. The QSVM provides a complementary exploration of the feature space which led to an improved accuracy of the mixed quantum-classical method for fraud detection, on a drastically reduced data set to fit current state of Quantum Hardware.
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.
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