Quantum AI for Banking: Secure Transactions, Portfolio Optimization, and Ethical Governance

Tayyabah Hassan, Fahad Ahmed

R. Deepa, Arasuraja Ganesan, Umidbek M. Abdalov +3 more

Balaji Dhashanamoorthi

Quantum AI for Banking: Secure Transactions, Portfolio Optimization, and Ethical Governance explores the convergence of artificial intelligence and quantum computing in financial services. The study synthesizes advances in quantum key distribution (QKD), post-quantum cryptography (PQC), and quantum machine learning (QML) to address critical challenges in transaction security, portfolio optimization, fraud detection, and risk analytics. Key contributions include: Hybrid AI–Quantum Workflows: Practical architectures combining classical AI with quantum subroutines for optimization, anomaly detection, and secure communications. Portfolio Optimization: QUBO formulations, quantum annealing, and QAOA variants for complex asset allocation under realistic constraints. Fraud Detection & Credit Scoring: Quantum support vector machines (QSVMs) and quantum neural networks (QNNs) applied to high-dimensional payment data, integrated with classical ensembles. Security & Governance: Integration of QKD with cryptographic stacks, alignment with NIST-led PQC standards, and quantum-safe ledger protocols. Ethical & Regulatory Dimensions: Addressing bias, explainability, auditability, workforce reskilling, and equitable access to quantum AI resources. The paper emphasizes NISQ-era constraints (limited qubits, short coherence times, error rates) and proposes noise-resilient algorithms, compact encodings, and hybrid orchestration strategies. It also highlights business outcomes such as smarter decision-making, real-time risk management, optimized operations, and enhanced customer experience. This work provides a roadmap for responsible adoption of quantum-enabled AI in banking, combining technical innovation with ethical governance to ensure secure, transparent, and future-ready financial infrastructures.

Tushar Saini, Kashish Solanki, Sandeeo Raj +1 more

Pratibha, Pummy Dhiman, Sudha +1 more

Quantum Artificial Intelligence (Quantum AI) is a popular and new computational field that incorporates the use of quantum computing in enhancing Artificial Intelligence (AI). This chapter addresses the use of Quantum AI in three sectors, namely healthcare, finance, and cybersecurity, with a view that the deployment of AI integrated with Quantum computing shall take the respective industry to another level. Quantum AI enhances diagnosis and medical imaging, pattern recognition, and diagnostics, which increases the rate of accuracy or detection and diagnosis of diseases and illnesses. Moreover, Quantum AI optimizes portfolio management, risk evaluation, and prediction, and fraud detection in the financial industry, as well as enhances the computational rate in comparison to current rates. Quantum is used to enhance investment solutions and approaches to assess large amounts of big data as soon as possible, resulting in more potent risk management tools. Also, they improve the detection of fraud in financial transactions by providing a means of detecting the variation in such transactions, making security in online/mobile banking and other facets of electronic payment firmer. On the same level, they defend computer networks and systems from cyber-attacks through massive and complicated structures of analyzing large data sets in an incredibly short period. Technology has certain drawbacks, which include issues in quantum computer hardware, errors that could be present in quantum structures, and moral issues relating to the safety of data and information. A solution to these challenges could only be good if Quantum AI becomes widely implemented across the numerous domains of industry today. This Chapter gives an elaborate overview of the relevance of Quantum AI in healthcare, finance, and cybersecurity, which defines the trend of intelligent and secure computing systems.