Quantum Machine Learning and Computing For Financial Fraud Detection

Title: Quantum Machine Learning and Computing For Financial Fraud Detection and Market Prediction

Summary:
Quantum Machine Learning (QML) and Quantum Computing (QC) offer the potential for improving financial technologies, particularly in fraud detection and market prediction. This research explores several QML models, including Quantum Support Vector Classifier (QSVCs), Quantum Graph Neural Networks (QGNNs), and Quantum Attention Deep Q-Networks (QADQNs), which are designed to process and analyze complex, graph-structured financial data. The study demonstrates that these models improve the accuracy of fraud detection and enhance decision-making capabilities in trading strategies by employing the computational efficiency of QC. The findings highlight the potential for future development of scalable, efficient, and accurate frameworks based on QML and QC to address ongoing financial challenges and improve analytical techniques within the financial sector. These advancements may enable more robust and data-driven decision-making processes in finance as quantum technologies continue to evolve.

Speaker:
Dr. Nouhaila Innan is a Research Team Lead at eBRAIN Lab and a Postdoctoral Associate at the Center for Quantum and Topological Systems (CQTS) at New York University (NYU) Abu Dhabi. She earned her PhD in Quantum Machine Learning from Hassan II University of Casablanca, where she also completed her Bachelor's in Physics & Applications and Master's in Physics & New Technologies, specializing in materials and nanomaterials. Her research focuses on quantum machine learning, quantum algorithms, and their applications in fields like finance and cybersecurity. Innan is passionate about mentoring and making quantum technologies accessible through global initiatives.

Moderator:
Dr. Sebastian Zajac, member of QPoland