Every day machine learning in financial systems is increasing with financial institutions taking every steps in their power to detect and block counterfeit transactions. Today, modern electronic payment instruments and financial facilities are becoming more and more susceptible to new methods of fraud which are unobtrusively reproducing the activities of legitimate interests to the detriment of the economy which is losing incalculable sums of money. In order to overcome this the combination technique of quantum and classical methods of calculating the data is a great way to enhance the reliability and accuracy of the fraud detection. It is applied by means of Quantum Support Vector Machine, Variational Quantum Classifier, the classical algorithms of Support Vector Machine and K-Nearest Neighbor with an ensemble based on Quadratic Unconstrained Binary Optimization. These algorithms are used together to scan a stream of transactions in real time and classify what is legitimate behaviour and what is unfavourable behaviour. The work provides evidence to prove that hybrid quantum-classical techniques can improve on the performance of classical systems using improved recall, less false negatives and the speed of detection of new types of frauds. It is shown that the integration of quantum intelligence based upon classical methods of analysis is a powerful and fruitful means to enhance levels of secure financial practice and help combat large scale financial frauds in modern digital environments.
Every day machine learning in financial systems is increasing with financial institutions taking every steps in their power to detect and block counterfeit transactions. Today, modern electronic payment instruments and financial facilities are becoming more and more susceptible to new methods of fraud which are unobtrusively reproducing the activities of legitimate interests to the detriment of the economy which is losing incalculable sums of money. In order to overcome this the combination technique of quantum and classical methods of calculating the data is a great way to enhance the reliability and accuracy of the fraud detection. It is applied by means of Quantum Support Vector Machine, Variational Quantum Classifier, the classical algorithms of Support Vector Machine and K-Nearest Neighbor with an ensemble based on Quadratic Unconstrained Binary Optimization. These algorithms are used together to scan a stream of transactions in real time and classify what is legitimate behaviour and what is unfavourable behaviour. The work provides evidence to prove that hybrid quantum-classical techniques can improve on the performance of classical systems using improved recall, less false negatives and the speed of detection of new types of frauds. It is shown that the integration of quantum intelligence based upon classical methods of analysis is a powerful and fruitful means to enhance levels of secure financial practice and help combat large scale financial frauds in modern digital environments.
The quick pace of digital financial transactions and the development of new strategies that fraudsters use makes the process of financial fraud detection a more than a complex task. The methods of machine learning have proved highly effective in detecting fraudulent behaviour especially in comparison to the traditional rule based systems. Simultaneously, the recent development of quantum computing has encouraged the consideration of quantum machine learning methods as the possible alternative or improvements to the classical models. The following paper contains a detailed comparative study of the traditional and quantum machine learning models of detecting financial fraud with references to a peer-reviewed literature and real financial data features. Classical algorithms such as ensemble and deep learning algorithms are discussed, as well as quantum algorithms such as quantum support vector machines and variational quantum classifiers. A single system architecture is presented and a comparison of the reported performance trends is carried out. It analyzes the persistence of classical models in the large-scale deployments and the emergence of hybrid classical-quantum frameworks as a promising research direction. The paper ends with a description of the major research gaps and practical challenges that should be overcome in order to implement quantum techniques in the real world financial systems.
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.
Cross-border financial transactions have significantly increased in volume in recent times, giving rise to numerous complex challenges that result in substantial financial losses amounting to billions of dollars. For detecting frauds in the system, the constraints involved are complexity, various-regulatory environments. There exist some traditional Machine Learning algorithms like SVM (Support Vector Machine), Random Forest, Linear Regression etc. Among all these algorithm’s SVM play an effective role in detecting frauds in financial transactions. Significantly, classical SVM’s face limitations in processing high dimensional financial data and detecting sophisticated fraud patterns across international boundaries. Wide range of analysis will be provided in this research between classical Support Vector Machine and Quantum SVM for cross border fraud detection which includes quantum technology leveraging advantages in cybersecurity, finance and image processing etc. This study enforces both classical and quantum SVM models on a comprehensive dataset of cross border financial transactions, evaluating performance across multiple metrics including accuracy, precision, recall, f1-score and false-positive rates. Quantum utilizes various quantum circuits and feature maps designed for financial fraud patterns.
The increasing complexity and volume of financial transactions pose significant challenges to traditional fraud detection systems. This technical report investigates and compares the efficacy of classical, quantum, and quantum-hybrid machine learning models for the binary classification of fraudulent financial activities. As of our methodology, first, we develop a comprehensive behavioural feature engineering framework to transform raw transactional data into a rich, descriptive feature set. Second, we implement and evaluate a range of models on the IBM Anti-Money Laundering (AML) dataset. The classical baseline models include Logistic Regression, Decision Tree, Random Forest, and XGBoost. These are compared against three hybrid classic quantum algorithms architectures: a Quantum Support Vector Machine (QSVM), a Variational Quantum Classifier (VQC), and a Hybrid Quantum Neural Network (HQNN). Furthermore, we propose Fraud Detection for Quantum Computing (FD4QC), a practical, API-driven system architecture designed for real-world deployment, featuring a classical-first, quantum-enhanced philosophy with robust fallback mechanisms. Our results demonstrate that classical tree-based models, particularly \textit{Random Forest}, significantly outperform the quantum counterparts in the current setup, achieving high accuracy (97.34%) and F-measure (86.95%). Among the quantum models, \textbf{QSVM} shows the most promise, delivering high precision (77.15%) and a low false-positive rate (1.36%), albeit with lower recall and significant computational overhead. This report provides a benchmark for a real-world financial application, highlights the current limitations of quantum machine learning in this domain, and outlines promising directions for future research.
Innovative methods for information security and fraud prevention are required in today's digital environment due to the expanding volume of data and the increasing complexity of cyber threats. The use of quantum computing techniques to improve fraud detection and classification systems is investigated in this study. The study's machine learning framework integrates three distinct quantum algorithms to improve classification techniques. The first technique uses a Pauli feature map and a Quantum Support Vector Classifier (QSVC) that leverages a quantum kernel to transform classical input into quantum states. The second technique use a ZZ feature map with "linear" entanglement and a support vector classifier model, utilizing quantum kernels to enhance quantum systems. The third method utilizes Variational Quantum Circuits (VQC) with actual amplitudes, which integrate quantum and conventional machine learning techniques to provide optimized classification. The best results were obtained by the QSVC using ZZ feature maps and linear entanglement, which had a precision of 1.0 and a notable decrease in false positives. In order to improve fraud detection systems' accuracy and dependability and offer strong solutions to financial institutions, this study shows how quantum computing has the potential to completely transform cybersecurity.
Detecting fraudulent financial transactions is inherently challenging due to extreme class imbalance, complex high-dimensional feature spaces, and the adaptive strategies employed by fraudsters.To overcome these limitations, this work presents a hybrid quantum-classical fraud detection framework that strategically combines conventional machine learning models with quantum computing techniques to enhance predictive reliability.Rich feature representations are constructed by integrating transactional, behavioral, and graph-based attributes to capture both local and relational fraud characteristics.A Quantum Autoencoder (QAE) is utilized to perform efficient dimensionality reduction, enabling compact feature encoding while retaining essential discriminatory patterns.The reduced feature set is subsequently processed using Cat-Boost, alongside Quantum Neural Networks (QNNs) or Variation Quantum Classifiers (VQCs) to leverage complementary learning capabilities.Model outputs are aggregated through a stacking-based meta-classification strategy, resulting in improved generalization and decision stability.Performance evaluation is carried out using fraud-oriented metrics, including recall, precision, accuracy, and false positive rate.Experimental analysis indicates that the proposed hybrid approach consistently surpasses standalone classical and quantum models, achieving superior fraud recall with notably fewer false alarms.These results demonstrate the robustness, scalability, and practical viability of quantum-classical integration, positioning the proposed framework as a promising foundation for next-generation financial fraud detection systems.