Accurate prediction of future loan defaults is a critical capability for financial institutions that provide lines of credit. For institutions that issue and manage extensive loan volumes, even a slight improvement in default prediction precision can significantly enhance financial stability and regulatory adherence, resulting in better customer experience and satisfaction. Datasets associated with credit default prediction often exhibit temporal correlations and high dimensionality. These attributes can lead to accuracy degradation and performance issues when scaling classical predictive algorithms tailored for these datasets. Given these limitations, quantum algorithms, leveraging their innate ability to handle high-dimensionality problems, emerge as a promising new avenue alongside classical approaches. To assess the viability and effectiveness of quantum methodologies, we investigate a hybrid quantum-classical algorithm, utilizing a publicly available "Default Prediction Dataset" released as part of a third-party data science competition. Specifically, we employ hybrid quantum-classical machine learning models based on projected quantum feature maps and their ensemble integration with classical models to examine the problem of credit card default prediction. Our results indicate that the ensemble models based on the projected quantum features were capable of slightly improving the purely classical results expressed via a "Composite Default Risk" (CDR) metric. Furthermore, we discuss the practical applicability of the studied quantum-classical machine learning techniques and address open questions concerning their implementation.
Accurate prediction of future loan defaults is a critical capability for financial institutions that provide lines of credit. For institutions that issue and manage extensive loan volumes, even a slight improvement in default prediction precision can significantly enhance financial stability and regulatory adherence, resulting in better customer experience and satisfaction. Datasets associated with credit default prediction often exhibit temporal correlations and high dimensionality. These attributes can lead to accuracy degradation and performance issues when scaling classical predictive algorithms tailored for these datasets. Given these limitations, quantum algorithms, leveraging their innate ability to handle high-dimensionality problems, emerge as a promising new avenue alongside classical approaches. To assess the viability and effectiveness of quantum methodologies, we investigate a hybrid quantum-classical algorithm, utilizing a publicly available "Default Prediction Dataset" released as part of a third-party data science competition. Specifically, we employ hybrid quantum-classical machine learning models based on projected quantum feature maps and their ensemble integration with classical models to examine the problem of credit card default prediction. Our results indicate that the ensemble models based on the projected quantum features were capable of slightly improving the purely classical results expressed via a "Composite Default Risk" (CDR) metric. Furthermore, we discuss the practical applicability of the studied quantum-classical machine learning techniques and address open questions concerning their implementation.
Predicting loan defaults is important to financial institutions for risk management and improving loaning policies. This research uses the LendingClub Loan Dataset comprising 2.26 million records with features such as credit score, debt income ratio, and loan amount, and applies a novel methodology for loan default and repayment behavior prediction with quantum-enhanced predictive analytics. The methodology uses embedded learning to disperse high-dimensional financial data into a low-dimensional quantum feature space. Coupled with a QML classifier, the framework demonstrated solid performance within a federated setting. The experimental findings indicate a ROC AUC of 0.897, surpassing classical machine learning baselines. The method preserves the data privacy through federated learning, thus applicable for sensitive financial contexts. This work demonstrates the ability of quantum analytics to enhance the predictive accuracy of loan defaults at scale.
The growing complexity, interdependencies, and rapid fluctuations inherent in modern financial markets create substantial challenges for accurate forecasting, portfolio optimization, and risk management. Conventional machine learning techniques, while powerful, often face limitations in capturing nonlinear relationships and processing high-dimensional datasets efficiently. Quantum machine learning (QML) has emerged as a promising paradigm that leverages quantum computing principles to enhance predictive modeling in finance. This study presents a comprehensive investigation into the application of QML methods—including variational quantum circuits, quantum kernel estimation, and quantum-enhanced support vector machines—for financial time-series prediction and asset price classification. We propose a hybrid quantum-classical framework that integrates quantum feature mapping with classical optimizers to improve model expressiveness and convergence. Empirical experiments are conducted using historical stock market data and synthetic datasets to benchmark QML approaches against established classical models such as long short-term memory networks and gradient boosting machines. The results demonstrate that QML techniques can achieve superior prediction accuracy and lower computational latency under certain data regimes, particularly when dealing with small-to-medium-sized datasets and high feature correlations. Additionally, the study examines scalability considerations, hardware constraints of near-term quantum devices, and the interpretability of quantum model outputs in financial decision-making contexts. The findings underscore the transformative potential of quantum machine learning as an innovative tool for advancing predictive analytics in finance and provide practical insights into how financial institutions can begin integrating QML capabilities into their workflows.
Credit Card Fraud Detection (CCFD) is a major concern for the financial industry, requiring rapid and accurate identification of fraudulent transactions amidst vast datasets. The rapid evolution of quantum computing presents transformative opportunities for enhancing Machine Learning (ML) applications, particularly in the domain of financial security. This paper explores the implementation of Quantum Machine Learning (QML) techniques for CCFD, a critical challenge in the financial industry characterized by imbalanced datasets. Traditional ML models, while effective, often struggle with scalability and computational efficiency when processing large-scale transactional data. QML leverages quantum phenomenon namely entanglement and superposition to potentially outperform classical algorithms in feature extraction and pattern recognition. Our study proposes a hybrid quantumclassical framework, integrating Variational Quantum Circuits (VQC) with classical Neural Networks (NN) to classify fraudulent transactions. Using a simulated quantum environment, we preprocess a publicly available credit card transaction dataset, encoding features into quantum states for processing through a parameterized quantum circuit. The model is trained to improves its ability to detect fraud accurately with fewer false positives, addressing the critical issue of customer inconvenience.
Abstract Non-performing loans present a significant challenge to financial institutions, driven by the complexity of the dataset, default probability, and default correlation(Bellotti et al., 2019). To mitigate this risk, this study investigates the potential of Machine Learning (ML) and Quantum Machine Learning (QML) algorithms for forecasting loan risk. Using a dataset from Kaggle, we conducted a comparative analysis between Support Vector Machine (SVM) and Quantum Support Vector Machine (QSVM).Our result using a dataset of 12,368 records and 12 features shows that the QSVM model outperformed SVM, with a higher true positive rate (93.2.%) and true negative rate (87.6%), demonstrating better performance in identifying both default and non-default cases. Additionally, QSVM exhibits a lower false negative rate indicating its superior ability to minimize clients likely to default. The AUC score of 1.0 for the QSVM further demonstrates its exceptional ability in loan prediction. While the dataset used allowed for a solid comparison, QSVM demonstrated its capacity to continue improving with larger datasets, showing its scalability and strong potential application in loan risk forecasting especially with larger datasets.
Financial sectors are based on credit risk modeling to make decisions on lending and regulatory compliance. Although classical machine learning has been used to increase predictiveness, such models are faced with mounting limitations as complex and high-dimensional financial data is processed. Quantum Machine Learning (QML) is an aspect of quantum computing that has offered a potential resolution to the issues, integrating quantum computing and machine learning to speed up the calculations and extract more insight into the patterns. QML has theoretical benefits in classifying credit risk via Quantum Support Vector Machines, Quantum Neural Networks, and hybrid quantum-classical models, through superposition and entanglement. Initial applications show good performance in portfolio optimization, default forecasting, and simulation of risks with the existing hardware constraints. QML demonstrates specific potential in the context of using non-traditional data sources and finding hidden correlations that could reflect creditworthiness, which may allow inclusion-based lending practices without sacrificing risk assessment. With the development of quantum hardware, the financial services sector will gradually adopt quantum capabilities via realistic hybrid strategies that eventually revolutionize the way credit risk is assessed in the world markets.
Abstract Quantum Machine Learning (QML) presents a novel paradigm for financial modeling, particularly for the challenge of few-shot credit risk assessment, which is a critical issue in inclusive finance where data scarcity limits conventional models. To address this, we propose and implement a hybrid quantum-classical workflow. Our methodology first utilizes a classical model ensemble for dimensionality reduction, creating a dense, three-dimensional feature representation from the original data. This representation then serves as input to a Quantum Neural Network (QNN), trained with the parameter-shift rule, which acts as the core classifier. The framework’s efficacy was validated through numerical simulations and on real hardware using the Quafu Cloud Platform’s ScQ-P21 superconducting processor. On a real-world credit dataset of 279 samples, our QNN achieved a robust average AUC of 0.852 ± 0.027 in simulations and an impressive AUC of 0.89 in the hardware experiment. This performance surpasses a suite of classical benchmarks, with a particularly strong result on the recall metric—crucial for minimizing false negatives in risk control. This study provides a pragmatic blueprint for applying QML to data-constrained financial scenarios and offers valuable empirical evidence of its potential in the NISQ era.