Intelligent Personalization in Digital Banking Using Quantum Computing and Real-Time Analytics
DOI:
https://doi.org/10.15662/IJEETR.2026.0804012Keywords:
Digital Banking, Intelligent Personalization, Quantum Computing, Real-Time Analytics, Quantum Machine Learning, Customer Behavior Analysis, Personalized Financial ServicesAbstract
The charge of quick development of digital banking has spurred the demand of clever and flexible and customer-oriented banking service. Traditional personalization techniques in most instances are constrained by handling speedy data, identifying complex modes of the customer behavior and make context based suggestions in real time. To enhance customer engagement and provision of services, the current paper proposes a smart personalization model of digital banking based on quantum computing theory and real time analytics. The framework is a combination of customer transaction history, spending behavior, online interaction and contextual data to generate personalized financial and product recommendations, spherical, as well as service experiences. Quantum-inspired optimization and quantum machine learning methods are also viewed as the opportunities to find complicated connections in customer data and help with customer segmentation and optimizing recommendations. Meanwhile, real-time analytics would enable processing real-time data and identifying the patterns of behavior, and adjusting towards new customer demands. Privacy protection, data security, explainability, and responsible personalization are also a part of the proposed framework to tackle the essential problems in digital financial services. Its possible performance can be evaluated based on the personalization relevance, the responsiveness of recommendations, contact with customers, computational efficiency and security. This paper builds the architectural foundation to the next generation of customized online banking due to the combination of new quantum computing strategies and real-time analysis capabilities. The framework presents the opportunities of the methods of improving the customer experience considering the realistic limitations that the existing quantum technologies put and the need to further substantiate it empirically.
References
[1] Egger, D. J., Gambella, C., Mareček, J., McFaddin, S., Mevissen, M., Raymond, R., Simonetto, A., Woerner, S., & Yndurain, E. (2020). Quantum computing for finance: State-of-the-art and future prospects. IEEE Transactions on Quantum Engineering, 1, Article 3101724. https://doi.org/10.1109/TQE.2020.3030314
[2] Grossi, M., Ibrahim, N., Radescu, V., Loredo, R., Voigt, K., von Altrock, C., & Rudnik, A. (2022). Mixed quantum–classical method for fraud detection with quantum feature selection. IEEE Transactions on Quantum Engineering, 3, 1–12. https://doi.org/10.1109/TQE.2022.3213474
[3] Alcazar, J., Cadarso, A., Katabarwa, A., Mauri, M., Peropadre, B., Wang, G., & Cao, Y. (2022). Quantum algorithm for credit valuation adjustments. New Journal of Physics, 24(2), Article 023036. https://doi.org/10.1088/1367-2630/ac5003
[4] Orús, R., Mugel, S., & Lizaso, E. (2019). Quantum computing for finance: Overview and prospects. Reviews in Physics, 4, Article 100028. https://doi.org/10.1016/j.revip.2019.100028
[5] Cerezo, M., Arrasmith, A., Babbush, R., Benjamin, S. C., Endo, S., Fujii, K., McClean, J. R., Mitarai, K., Yuan, X., Cincio, L., & Coles, P. J. (2021). Variational quantum algorithms. Nature Reviews Physics, 3(9), 625–644. https://doi.org/10.1038/s42254-021-00348-9
[6] Chakrabarti, S., Krishnakumar, R., Mazzola, G., Stamatopoulos, N., Woerner, S., & Zeng, W. J. (2021). A threshold for quantum advantage in derivative pricing. Quantum, 5, Article 463. https://doi.org/10.22331/q-2021-06-01-463
[7] Kaneko, K., Miyamoto, K., Takeda, N., & Yoshino, K. (2021). Quantum speedup of Monte Carlo integration with respect to the number of dimensions and its application to finance. Quantum Information Processing, 20(5), Article 185. https://doi.org/10.1007/s11128-021-03118-3
[8] Kaneko, K., Miyamoto, K., Takeda, N., & Yoshino, K. (2022). Quantum pricing with a smile: Implementation of local volatility model on quantum computer. EPJ Quantum Technology, 9, Article 7. https://doi.org/10.1140/epjqt/s40507-022-00128-1
[9] Stamatopoulos, N., Egger, D. J., Sun, Y., Zoufal, C., Iten, R., Shen, N., & Woerner, S. (2020). Option pricing using quantum computers. Quantum, 4, Article 291. https://doi.org/10.22331/q-2020-07-06-291
[10] Stamatopoulos, N., Mazzola, G., Woerner, S., & Zeng, W. J. (2022). Towards quantum advantage in financial market risk using quantum gradient algorithms. Quantum, 6, Article 770. https://doi.org/10.22331/q-2022-09-22-770
[11] Chou, Y.-H., Jiang, Y.-C., Hsu, Y.-R., & Kuo, S.-Y. (2022). A weighted portfolio optimization model based on the trend ratio, emotion index, and ANGQTS. IEEE Transactions on Emerging Topics in Computational Intelligence, 6(4), 867–882. https://doi.org/10.1109/TETCI.2021.3122971
[12] Lang, J., Zielinski, S., & Feld, S. (2022). Strategic portfolio optimization using simulated, digital, and quantum annealing. Applied Sciences, 12(23), Article 12288. https://doi.org/10.3390/app122312288
[13] Brandhofer, S., Braun, D., Dehn, V., Hellstern, G., Hüls, M., Ji, Y., Polian, I., Bhatia, A. S., & Wellens, T. (2023). Benchmarking the performance of portfolio optimization with QAOA. Quantum Information Processing, 22(1), Article 25. https://doi.org/10.1007/s11128-022-03766-5
[14] Kyriienko, O., & Magnusson, E. B. (2022). Unsupervised quantum machine learning for fraud detection (arXiv:2208.01203). arXiv. https://doi.org/10.48550/arXiv.2208.01203
[15] Canabarro, A., Mendonça, T. M., Nery, R., Moreno, G., Albino, A. S., de Jesus, G. F., & Chaves, R. (2022). Quantum finance: A tutorial on quantum computing applied to the financial market (arXiv:2208.04382). arXiv. https://doi.org/10.48550/arXiv.2208.04382
