Hybrid Quantum-Classical Support Vector Machines for Financial Regime Detection: Performance and Efficiency Evaluation

Mathieu Mercadier

Konijeti Saranya, Kunuku Gnapika, Namana Lakshmi Harshita +4 more

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.

Sandhya S, Koj Roshni, Shakthi Priya V +2 more

Mathieu Mercadier

K. Deepa, Raunak Singh, Kondapuram Tharun

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.

Mathieu Mercadier