Collusive insurance fraud causes large financial losses for insurance companies. While community detection algorithms based on modularity can be used to detect collusive insurance fraud, the related optimization problem is NP-hard, motivating the application of quantum computers. In this work, we employ a specialized quantum computer, the Coherent Ising Machine (CIM), for the insurance fraud detection problem. By modeling insurance fraud detection as a Quadratic Unconstrained Binary Optimization (QUBO) problem, we efficiently solve it on a physical CIM with over 1500 qubits. Our results demonstrate the applicability of CIM to insurance fraud detection and its significant acceleration capability compared to classical methods. Furthermore, CIM can obtain degenerate ground-state and near-optimal solutions due to its sampling capability and the community partitions of CIM manifest diversity while each solution maintaining a competitive fraud detection performance. We explore how this solution diversity can be used to strengthen CIM fraud detection performance and demonstrate that, when top 2% or less nodes are reviewed, the precision and recall rate of the solution ensemble are better than that of the best single solution up to 2.5 times and 2 times, respectively.
Collusive insurance fraud causes large financial losses for insurance companies. While community detection algorithms based on modularity can be used to detect collusive insurance fraud, the related optimization problem is NP-hard, motivating the application of quantum computers. In this work, we employ a specialized quantum computer, the Coherent Ising Machine (CIM), for the insurance fraud detection problem. By modeling insurance fraud detection as a Quadratic Unconstrained Binary Optimization (QUBO) problem, we efficiently solve it on a physical CIM with over 1500 qubits. Our results demonstrate the applicability of CIM to insurance fraud detection and its significant acceleration capability compared to classical methods. Furthermore, CIM can obtain degenerate ground-state and near-optimal solutions due to its sampling capability and the community partitions of CIM manifest diversity while each solution maintaining a competitive fraud detection performance. We explore how this solution diversity can be used to strengthen CIM fraud detection performance and demonstrate that, when top 2% or less nodes are reviewed, the precision and recall rate of the solution ensemble are better than that of the best single solution up to 2.5 times and 2 times, respectively.
Fraud detection within transaction data is crucial for maintaining financial security, especially in the era of big data. This paper introduces a novel fraud detection method that utilizes quantum computing to implement community detection in transaction networks. We model transaction data as an undirected graph, where nodes represent accounts and edges indicate transactions between them. A modularity function is defined to measure the community structure of the graph. By optimizing this function through the Quadratic Unconstrained Binary Optimization (QUBO) model, we identify the optimal community structure, which is then used to assess the fraud risk within each community. Using a Coherent Ising Machine (CIM) to solve the QUBO model, we successfully divide 308 nodes into four communities. We find that the CIM computes faster than the classical Louvain and simulated annealing (SA) algorithms. Moreover, the CIM achieves better community structure than Louvain and SA as quantified by the modularity function. The structure also unambiguously identifies a high-risk community, which contains almost 70% of all the fraudulent accounts, demonstrating the practical utility of the method for banks' anti-fraud business.
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.
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.
For complex optimization problems in the financial services industry, specialized quantum computer can server as a transformative tool. Focusing on loan portfolio pricing optimization, we demonstrate the application potentials and advantages of quantum computing in financial optimization problems. For the first time, the loan portfolio pricing problem is modeled as a Quadratic Unconstrained Binary Optimization (QUBO) problem, and then solved by a specialized quantum computer, Coherent Ising Machine (CIM) based on optical dissipative systems. By introducing an auxiliary qubit and conducting a two-stage search for the appropriate penalty coefficients, we demonstrate the applicability and advantages of CIM in the loan portfolio pricing optimization problem. The experimental results demonstrate that compared to state-of-the-art classical solvers, CIM achieves significant acceleration capability and energy efficiency beyond classical methods. Furthermore, by conducting numerical and analytical evaluations of CIM’s scalability in the loan portfolio pricing optimization, we demonstrate the convergence guarantees of CIM and its ability to achieve a better solution quality than the state-of-the-art classical solver in large-scale problems.
This book chapter explores the advanced computational techniques, particularly quantum computing (QC), to enhance fraud detection in financial sectors. We primarily concentrate on the quantum-enhanced fraud detection tool, which enhances the security and efficiency of financial systems. Threats such as cyberattacks and financial fraud escalate, thereby boosting the effectiveness of these systems, which becomes even more critical. We evaluate the combination of QC and machine learning (ML) methods to understand applications and fraud-related activities. This research used a method called comparative analysis and probing, which combines old ways of finding fraud (e.g., rule-based and statistical approaches) with new models that are based on quantum physics. This approach focuses on efficiency and accuracy while also exploring the potential of quantum algorithms. Combining QC with ML yields significant improvements in accuracy. QC algorithms, such as Grover's algorithms and quantum support vector machines (SVMs), process large datasets to identify fraud patterns that conventional techniques miss. This demonstrates how QC not only enhances risk models but also mitigates fraud in real-time scenarios. Financial institutions are advocating for the practical implementation of quantum–classical systems. These systems integrated QC capabilities into existing infrastructure to enhance fraud detection, risk management, and transaction security. They also improved quantum error corrections and addressed cybersecurity concerns related to quantum transactions. This book chapter focuses on the detection of quantum errors in fraud and related financial security solutions. The exponential acceleration of QC in fraud detection is revolutionizing methodologies and enhancing security and efficiency in financial systems.
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.