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Smart innovation, systems and technologies

Enhancing the Financial Sector with Quantum Computing: A Comprehensive Review of Current and Future Applications

Claudiu Brândaș, Cosmin Eugen Enache, Otniel Didraga, Andrei ALBU

Published Jan 1, 2024• 7 citations
Publisher
DOI
10.1007/978-981-99-6529-8_17

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The financial sector faces escalating cybersecurity threats, exacerbated by the potential emergence of quantum computers capable of compromising widely used classical cryptographic methods. The paper includes an AI-enhanced general review on the use of quantum cryptography to secure financial transactions. Using machine learning methods to assess the principles, potential, and constraints to Quantum Key Distribution (QKD) and PostQuantum Cryptography (PQC), the review is an improvement of the existing view of the current state of quantumsafe technologies. The opportunities and challenges in adopting these technologies in the key financial services, such as banking, digital payments, and blockchain, are analyzed. It has been identified that a hybrid approach that utilizes both QKD to perform secure key exchange and PQC to protect data over the long term can be considered an effective strategy. The review ends by identifying the main future research opportunities and the necessity of standardized, cost-efficient, and scalable quantum security solutions, guided by AI-informed insights, to protect the financial sector against future quantum attacks.
Abstract : The financial sector faces escalating cybersecurity threats, exacerbated by the potential emergence of quantum computers capable of compromising widely used classical cryptographic methods. The paper includes an AI-enhanced general review on the use of quantum cryptography to secure financial transactions. Using machine learning methods to assess the principles, potential, and constraints to Quantum Key Distribution (QKD) and PostQuantum Cryptography (PQC), the review is an improvement of the existing view of the current state of quantumsafe technologies. The opportunities and challenges in adopting these technologies in the key financial services, such as banking, digital payments, and blockchain, are analyzed. It has been identified that a hybrid approach that utilizes both QKD to perform secure key exchange and PQC to protect data over the long term can be considered an effective strategy. The review ends by identifying the main future research opportunities and the necessity of standardized, cost-efficient, and scalable quantum security solutions, guided by AI-informed insights, to protect the financial sector against future quantum attacks.
Quantum computing is revolutionizing computational methods in finance by enhancing efficiency and accuracy in financial modeling and risk management. This review explores the impact of quantum computing in finance, focusing on derivative pricing, risk management, and portfolio optimization. It analyzes the benefits, limitations, and future implications of integrating quantum technologies with classical financial systems. A comprehensive review of quantum Monte Carlo methods, quantum algorithms, and their financial applications was conducted. Theoretical foundations, including Chebyshev’s inequality and Grover’s search method, were evaluated for their role in enhancing sampling efficiency and predictive accuracy. Empirical studies and use cases demonstrate the practical implications of quantum computing in pricing financial derivatives and managing risk through Value at Risk and Conditional Value at Risk assessments. The critical findings reveal that Quantum Monte Carlo algorithms provide substantial efficiency gains, reducing sample size requirements by up to fourfold compared to classical methods. Techniques employed in prior studies highlight the effectiveness of Quantum Amplitude Estimation for derivative pricing and risk analysis. However, challenges persist in scalability, data quality, integration with classical systems, and compliance with regulatory standards. This research contributes to the current body of knowledge by providing a comprehensive analysis of quantum algorithms’ practical applications in derivative pricing, risk management, and portfolio optimization, demonstrating the efficiency gains and limitations of quantum approaches. Future research should explore hybrid quantum–classical frameworks, quantum applications in blockchain, and enhanced quantum cryptography to ensure secure and efficient financial transactions.