Home / Current Issue / Paper 1708928
Advancements in Real-Time Payment Systems: A Review of Blockchain and AI Integration for Financial Operations
Subject area: Science,Engineering and Technology · Area of research: Blockchain
Abstract
Real-time payment systems have revolutionized the financial landscape by enabling instantaneous transfer of funds, improving liquidity, and enhancing customer experience. Recent advancements focus on integrating emerging technologies such as blockchain and Artificial Intelligence (AI) to address existing challenges related to security, transparency, scalability, and fraud prevention. This review examines the current state of blockchain and AI integration within real-time payment systems, highlighting their synergistic potential to transform financial operations. Blockchain technology offers a decentralized, immutable ledger that enhances transactional transparency and security while reducing the reliance on centralized intermediaries. Its application in real-time payments facilitates faster settlement, lowers transaction costs, and mitigates fraud risks through cryptographic verification and consensus mechanisms. However, scalability and interoperability remain challenges, prompting ongoing research into hybrid blockchain models and cross-chain protocols. Simultaneously, AI contributes to real-time payments by improving risk assessment, fraud detection, and operational efficiency. Machine learning algorithms analyze transactional data patterns in real time to identify suspicious activities, optimize payment routing, and personalize customer interactions. AI-powered automation also streamlines compliance processes, reducing manual intervention and associated errors. The integration of blockchain and AI creates a robust ecosystem for real-time payment systems, combining blockchain?s secure infrastructure with AI?s intelligent analytics. This fusion supports enhanced decision-making, adaptive fraud prevention, and dynamic system optimization, enabling financial institutions to meet evolving regulatory and customer demands. This review synthesizes recent academic and industry research to map technological trends, identify implementation challenges, and propose future directions. It underscores the importance of developing standardized protocols, addressing privacy concerns, and fostering collaboration between technology providers, regulators, and financial institutions. The findings suggest that blockchain and AI integration holds significant promise for the next generation of real-time payment systems, offering improved security, efficiency, and inclusivity in financial operations worldwide.
Keywords
Advancements, Real-time, Payment systems, Blockchain, AI integration, Financial operations
References
[1] Abayomi, A. A., Mgbame, A. C., Akpe, O. E. E., Ogbuefi, E., & Adeyelu, O. O. (2021). Advancing equity through technology: Inclusive design of BI platforms for small businesses. Iconic Research and Engineering Journals, 5(4), 235–241.
[2] Abayomi, A. A., Ubanadu, B. C., Daraojimba, A. I., Ogeawuchi, J. C., Ogbuefi, E., & Adeyelu, O. O. (2021). A conceptual framework for real-time data analytics and decision-making in cloud-optimized business intelligence systems. IRE Journals, 4(9), 271-282.
[3] Adekunle, B.I., Chukwuma-Eke, E.C., Balogun, E.D. and Ogunsola, K.O., 2021. Machine learning for automation: Developing data-driven solutions for process optimization and accuracy improvement. Machine Learning, 2(1).
[4] Adekunle, B.I., Chukwuma-Eke, E.C., Balogun, E.D. and Ogunsola, K.O., 2021. A predictive modeling approach to optimizing business operations: A case study on reducing operational inefficiencies through machine learning. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), pp.791-799.
[5] Adewale, T.T., Olorunyomi, T.D. and Odonkor, T.N., 2021. Advancing sustainability accounting: A unified model for ESG integration and auditing. Int J Sci Res Arch, 2(1), pp.169-85.
[6] Adewale, T.T., Olorunyomi, T.D. and Odonkor, T.N., 2021. AI-powered financial forensic systems: A conceptual framework for fraud detection and prevention. Magna Sci Adv Res Rev, 2(2), pp.119-36.
[7] Adewoyin, M.A., 2021. Developing frameworks for managing low-carbon energy transitions: overcoming barriers to implementation in the oil and gas industry.
[8] Akinade, A.O., Adepoju, P.A., Ige, A.B., Afolabi, A.I. and Amoo, O.O., 2021. A conceptual model for network security automation: Leveraging AI-driven frameworks to enhance multi-vendor infrastructure resilience. International Journal of Science and Technology Research Archive, 1(1), pp.39-59.
[9] Akpe, O. E. E., Mgbame, A. C., Ogbuefi, E., Abayomi, A. A., & Adeyelu, O. O. (2021). Bridging the business intelligence gap in small enterprises: A conceptual framework for scalable adoption. Iconic Research and Engineering Journals, 5(5), 416–431.
[10] Alonge, E.O., Eyo-Udo, N.L., Ubanadu, B.C., Daraojimba, A.I., Balogun, E.D. and Ogunsola, K.O., 2021. Enhancing data security with machine learning: A study on fraud detection algorithms. Journal of Data Security and Fraud Prevention, 7(2), pp.105-118.
[11] Ang, L.L., Taylor, W. and Leon, M.P., 2020. Fintech developments and antitrust considerations in payments. Antitrust, 35, p.69.
[12] Armelius, H., Guibourg, G., Johansson, S. and Schmalholz, J., 2020. E-krona design models: pros, cons and trade-offs. Sveriges Riksbank Economic Review, 2, pp.80-96.
[13] Austin-Gabriel, B., Hussain, N.Y., Ige, A.B., Adepoju, P.A., Amoo, O.O. and Afolabi, A.I., 2021. Advancing zero trust architecture with AI and data science for enterprise cybersecurity frameworks. Open Access Research Journal of Engineering and Technology, 1(01), pp.047-055.
[14] BALOGUN, E.D., OGUNSOLA, K.O. and SAMUEL, A., 2021. A Risk Intelligence Framework for Detecting and Preventing Financial Fraud in Digital Marketplaces.
[15] Balogun, E.D., Ogunsola, K.O. and Samuel, A.D.E.B.A.N.J.I., 2021. A cloud-based data warehousing framework for real-time business intelligence and decision-making optimization. International Journal of Business Intelligence Frameworks, 6(4), pp.121-134.
[16] Barr, M.S., Harris, A., Menand, L. and Xu, W.M., 2020. Building the payment system of the future: How central banks can improve payments to enhance financial inclusion. U of Michigan Law & Econ Research Paper, (20-038).
[17] Chukwuma-Eke, E.C., Ogunsola, O.Y. and Isibor, N.J., 2021. Designing a robust cost allocation framework for energy corporations using SAP for improved financial performance. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), pp.809-822.
[18] Cunha, C.R., Mendonça, V., Morais, E.P. and Carvalho, A., 2018. The role of gamification in material and immaterial cultural heritage. In Proceedings of the 31st International Business Information Management Association Conference (IBIMA) (pp. 6121-6129). International Business Information Management Association (IBIMA).
[19] Dienagha, I.N., Onyeke, F.O., Digitemie, W.N. and Adekunle, M., 2021. Strategic reviews of greenfield gas projects in Africa: Lessons learned for expanding regional energy infrastructure and security.
[20] Edwards, Q., Mallhi, A.K. and Zhang, J., 2018. The association between advanced maternal age at delivery and childhood obesity. J Hum Biol, 30(6), p.e23143.
[21] Egbuhuzor, N.S., Ajayi, A.J., Akhigbe, E.E., Agbede, O.O., Ewim, C.P.M. and Ajiga, D.I., 2021. Cloud-based CRM systems: Revolutionizing customer engagement in the financial sector with artificial intelligence. International Journal of Science and Research Archive, 3(1), pp.215-234.
[22] Egbumokei, P.I., Dienagha, I.N., Digitemie, W.N. and Onukwulu, E.C., 2021. Advanced pipeline leak detection technologies for enhancing safety and environmental sustainability in energy operations. International Journal of Science and Research Archive, 4(1), pp.222-228.
[23] Eliezer, O. and Emmanuel, B., 2015. Relevance of forensic accounting in the detection and prevention of fraud in Nigeria. International Journal of Accounting Research, 2(7), pp.67-77.
[24] Ficco, M., 2019. Could emerging fraudulent energy consumption attacks make the cloud infrastructure costs unsustainable?. Information Sciences, 476, pp.474-490.
[25] Fredson, G., Adebisi, B., Ayorinde, O.B., Onukwulu, E.C., Adediwin, O. and Ihechere, A.O., 2021. Driving organizational transformation: Leadership in ERP implementation and lessons from the oil and gas sector. Int J Multidiscip Res Growth Eval [Internet].
[26] Fredson, G., Adebisi, B., Ayorinde, O.B., Onukwulu, E.C., Adediwin, O. and Ihechere, A.O., 2021. Revolutionizing procurement management in the oil and gas industry: Innovative strategies and insights from high-value projects. Int J Multidiscip Res Growth Eval [Internet].
[27] Hassan, Y.G., Collins, A., Babatunde, G.O., Alabi, A.A. and Mustapha, S.D., 2021. AI-driven intrusion detection and threat modeling to prevent unauthorized access in smart manufacturing networks. Artificial intelligence (AI), p.16.
[28] Hussain, N.Y., Austin-Gabriel, B., Ige, A.B., Adepoju, P.A., Amoo, O.O. and Afolabi, A.I., 2021. AI-driven predictive analytics for proactive security and optimization in critical infrastructure systems. Open Access Research Journal of Science and Technology, 2(02), pp.006-015.
[29] Ike, C.C., Ige, A.B., Oladosu, S.A., Adepoju, P.A., Amoo, O.O. and Afolabi, A.I., 2021. Redefining zero trust architecture in cloud networks: A conceptual shift towards granular, dynamic access control and policy enforcement. Magna Scientia Advanced Research and Reviews, 2(1), pp.074-086.
[30] ILORI, O., LAWAL, C.I., FRIDAY, S.C., ISIBOR, N.J. and CHUKWUMA-EKE, E.C., 2021. Enhancing Auditor Judgment and Skepticism through Behavioral Insights: A Systematic Review.
[31] ILORI, O., LAWAL, C.I., FRIDAY, S.C., ISIBOR, N.J. and CHUKWUMA-EKE, E.C., 2020. Blockchain-Based Assurance Systems: Opportunities and Limitations in Modern Audit Engagements.
[32] Imran, S., Patel, R.S., Onyeaka, H.K., Tahir, M., Madireddy, S., Mainali, P., Hossain, S., Rashid, W., Queeneth, U. and Ahmad, N., 2019. Comorbid depression and psychosis in Parkinson’s disease: a report of 62,783 hospitalizations in the United States. Cureus, 11(7).
[33] Isibor, N.J., Ewim, C.P.M., Ibeh, A.I., Adaga, E.M., Sam-Bulya, N.J. and Achumie, G.O., 2021. A generalizable social media utilization framework for entrepreneurs: Enhancing digital branding, customer engagement, and growth. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), pp.751-758.
[34] Iyabode, L.C., 2015. Career Development and Talent Management in Banking Sector. Texila International Journal.
[35] Lawal, C.I., 2015. Knowledge and awareness on the utilization of talent philosophy by banks among staff on contract appointment in commercial banks in Ibadan, Oyo State. Texila International Journal of Management, 3.
[36] Lazer, D.M., Pentland, A., Watts, D.J., Aral, S., Athey, S., Contractor, N., Freelon, D., Gonzalez-Bailon, S., King, G., Margetts, H. and Nelson, A., 2020. Computational social science: Obstacles and opportunities. Science, 369(6507), pp.1060-1062.
[37] Liu, X., Farahani, B. and Firouzi, F., 2020. Distributed ledger technology. Intelligent internet of things: From device to fog and cloud, pp.393-431.
[38] Maturo, F. and Hoskova-Mayerova, S., 2018. Analysing research impact via functional data analysis: a powerful tool for scholars, insiders, and research organizations. In Innovation Management and Education Excellence through Vision 2020 (pp. 1832-1842). International Business Information Management Association (IBIMA).
[39] Mgbame, A. C., Akpe, O. E. E., Abayomi, A. A., Ogbuefi, E., & Adeyelu, O. O. (2020). Barriers and enablers of BI tool implementation in underserved SME communities. Iconic Research and Engineering Journals, 3(7), 211–220. Published in International Journal of Scientific Research in Computer Science, Engineering and Information Technology
[40] Mgbame, A. C., Akpe, O. E., Abayomi, A. A., Ogbuefi, E., Adeyelu, O. O., & Mgbame, A. C. (2021). Building data-driven resilience in small businesses: A framework for operational intelligence. IRE Journals, 4(9), 253-265.
[41] Mgbame, A. C., Akpe, O. E., Abayomi, A. A., Ogbuefi, E., Adeyelu, O. O., & Mgbame, A. C. (2020). Bridging the business intelligence gap in small enterprises: A conceptual framework for scalable adoption. IRE Journals, 4(2), 159-173.
[42] Mgbame, A. C., Akpe, O. E., Abayomi, A. A., Ogbuefi, E., Adeyelu, O. O., & Mgbame, A. C. (2020). Barriers and enablers of BI tool implementation in underserved SME communities. IRE Journals, 3(7), 211-223.
[43] Nookala, G., Gade, K.R., Dulam, N. and Thumburu, S.K.R., 2020. Data Virtualization as an Alternative to Traditional Data Warehousing: Use Cases and Challenges. Innovative Computer Sciences Journal, 6(1).
[44] Nwaozomudoh, M.O., Odio, P.E., Kokogho, E., Olorunfemi, T.A., Adeniji, I.E. and Sobowale, A., 2021. Developing a conceptual framework for enhancing interbank currency operation accuracy in Nigeria's banking sector. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), pp.481-494.
[45] Odio, P.E., Kokogho, E., Olorunfemi, T.A., Nwaozomudoh, M.O., Adeniji, I.E. and Sobowale, A., 2021. Innovative financial solutions: A conceptual framework for expanding SME portfolios in Nigeria's banking sector. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), pp.495-507.
[46] Ofori-Asenso, R., Ogundipe, O., Agyeman, A.A., Chin, K.L., Mazidi, M., Ademi, Z., De Bruin, M.L. and Liew, D., 2020. Cancer is associated with severe disease in COVID-19 patients: a systematic review and meta-analysis. Ecancermedicalscience, 14, p.1047.
[47] Ofori-Asenso, R., Ogundipe, O., Agyeman, A.A., Chin, K.L., Mazidi, M., Ademi, Z., De Bruin, M.L. and Liew, D., 2020. Cancer is associated with severe disease in COVID-19 patients: a systematic review and meta-analysis. Ecancermedicalscience, 14, p.1047.
[48] Ogbuefi, E., Mgbame, A. C., Akpe, O. E., Abayomi, A. A., Adeyelu, O. O., & Ogbuefi, E. (2021). Affordable automation: Leveraging cloud-based BI systems for SME sustainability. IRE Journals, 4(12), 393-404.
[49] Ogeawuchi, J. C., Akpe, O. E., Abayomi, A. A., Agboola, O. A., Ogbuefi, E., & Owoade, S. (2021). Systematic review of advanced data governance strategies for securing cloud-based data warehouses and pipelines. IRE Journals, 5(1), 476-488.
[50] Ogundipe, O., Mazidi, M., Chin, K.L., Gor, D., McGovern, A., Sahle, B.W., Jermendy, G., Korhonen, M.J., Appiah, B., Ademi, Z. and De Bruin, M.L., 2021. Real-world adherence, persistence, and in-class switching during use of dipeptidyl peptidase-4 inhibitors: a systematic review and meta-analysis involving 594,138 patients with type 2 diabetes. Acta Diabetologica, 58, pp.39-46.
[51] Ogunmokun, A.S., Balogun, E.D. and Ogunsola, K.O., 2021. A Conceptual Framework for AI-Driven Financial Risk Management and Corporate Governance Optimization.
[52] Ogunnowo, E., Ogu, E., Egbumokei, P., Dienagha, I. and Digitemie, W., 2021. Theoretical framework for dynamic mechanical analysis in material selection for highperformance engineering applications. Open Access Research Journal of Multidisciplinary Studies, 1(2), pp.117-131.
[53] Ogunsola, K.O., Balogun, E.D. and Ogunmokun, A.S., 2021. Enhancing financial integrity through an advanced internal audit risk assessment and governance model. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), pp.781-790.
[54] OJIKA, F.U., OWOBU, W.O., ABIEBA, O.A., ESAN, O.J., UBAMADU, B.C. and IFESINACHI, A., 2021. Optimizing AI Models for Cross-Functional Collaboration: A Framework for Improving Product Roadmap Execution in Agile Teams.
[55] OJIKA, F.U., OWOBU, W.O., ABIEBA, O.A., ESAN, O.J., UBAMADU, B.C. and IFESINACHI, A., 2021. A Conceptual Framework for AI-Driven Digital Transformation: Leveraging NLP and Machine Learning for Enhanced Data Flow in Retail Operations.
[56] Okolie, C.I., Hamza, O., Eweje, A., Collins, A., Babatunde, G.O. and Ubamadu, B.C., 2021. Leveraging digital transformation and business analysis to improve healthcare provider portal. Iconic Research and Engineering Journals, 4(10), pp.253-257.
[57] OKOLO, F.C., ETUKUDOH, E.A., OGUNWOLE, O., OSHO, G.O. and BASIRU, J.O., 2021. Systematic Review of Cyber Threats and Resilience Strategies Across Global Supply Chains and Transportation Networks.
[58] Oladosu, S.A., Ike, C.C., Adepoju, P.A., Afolabi, A.I., Ige, A.B. and Amoo, O.O., 2021. The future of SD-WAN: A conceptual evolution from traditional WAN to autonomous, self-healing network systems. Magna Scientia Advanced Research and Reviews.
[59] Oladosu, S.A., Ike, C.C., Adepoju, P.A., Afolabi, A.I., Ige, A.B. and Amoo, O.O., 2021. Advancing cloud networking security models: Conceptualizing a unified framework for hybrid cloud and on-premises integrations. Magna Scientia Advanced Research and Reviews.
[60] Omisola, J.O., Etukudoh, E.A., Okenwa, O.K. and Tokunbo, G.I., 2020. Innovating Project Delivery and Piping Design for Sustainability in the Oil and Gas Industry: A Conceptual Framework. perception, 24, pp.28-35.
[61] Onifade, A. Y., Ogeawuchi, J. C., Abayomi, A. A., Agboola, O. A., Dosumu, R. E., & George, O. O. (2021). A conceptual framework for integrating customer intelligence into regional market expansion strategies. ICONIC Research and Engineering Journals, 5(2), 189-194. https://doi.org/10.54660/IJMOR.2023.2.1.254-260.
[62] Onukwulu, E.C., Agho, M.O. and Eyo-Udo, N.L., 2021. Framework for sustainable supply chain practices to reduce carbon footprint in energy. Open Access Research Journal of Science and Technology, 1(2), pp.12-34.
[63] Onukwulu, E.C., Agho, M.O. and Eyo-Udo, N.L., 2021. Advances in smart warehousing solutions for optimizing energy sector supply chains. Open Access Res J Multidiscip Stud. 2021; 2 (1): 139–57 [online]
[64] Onukwulu, E.C., Dienagha, I.N., Digitemie, W.N. and Egbumokei, P.I., 2021. Predictive analytics for mitigating supply chain disruptions in energy operations. IRE Journals.
[65] Onukwulu, E.C., Dienagha, I.N., Digitemie, W.N. and Egbumokei, P.I., 2021. AI-driven supply chain optimization for enhanced efficiency in the energy sector. Magna Scientia Advanced Research and Reviews, 2(1), pp.087-108.
[66] Oyedokun, O.O., 2019. Green human resource management practices and its effect on the sustainable competitive edge in the Nigerian manufacturing industry (Dangote) (Doctoral dissertation, Dublin Business School).
[67] Oyeniyi, L.D., Igwe, A.N., Ofodile, O.C. and Paul-Mikki, C., 2021. Optimizing risk management frameworks in banking: Strategies to enhance compliance and profitability amid regulatory challenges. Journal Name Missing.
[68] Parenti, R., 2020. Regulatory sandboxes and innovation hubs for FinTech. Study for the Committee on Economic and Monetary Affairs, Policy Department for Economic, Scientific and Quality of Life Policies, European Parliament, Luxembourg, 65.
[69] Paul, P.O., Abbey, A.B.N., Onukwulu, E.C., Agho, M.O. and Louis, N., 2021. Integrating procurement strategies for infectious disease control: Best practices from global programs. prevention, 7, p.9.
[70] Rahman, H. and Hussain, M.I., 2020. A comprehensive survey on semantic interoperability for Internet of Things: State‐of‐the‐art and research challenges. Transactions on Emerging Telecommunications Technologies, 31(12), p.e3902.
[71] Schreiber, Z., 2020. K-Root-n: An efficient algorithm for avoiding short term double-spending alongside distributed ledger technologies such as blockchain. Information, 11(2), p.90.
[72] Sharpe, M., Sullivan, M., Hyllner, J. and Thompson, K., 2020. The Role of Governments in the Commercial Emergence of Radical Innovation: The Case of the United Kingdom. In Second Generation Cell and Gene-based Therapies (pp. 659-688). Academic Press.
[73] Townsend, R.M., 2019. Distributed ledgers: Innovation and regulation in financial infrastructure and payment systems. URL: http://www. robertmtownsend. net/sites/default/files/files/papers/working_papers/Dis tributed% 20Ledgers-first% 20circulation-041819. pdf.
[74] Yang, G., Jan, M.A., Rehman, A.U., Babar, M., Aimal, M.M. and Verma, S., 2020. Interoperability and data storage in internet of multimedia things: investigating current trends, research challenges and future directions. IEEE Access, 8, pp.124382-124401.
[75] Zhang, L., Xie, Y., Zheng, Y., Xue, W., Zheng, X. and Xu, X., 2020. The challenges and countermeasures of blockchain in finance and economics. Systems Research and Behavioral Science, 37(4), pp.691-698.
How to cite this paper
@article{1708928,
author = {Chigozie Regina Nwangele, Ademola Adewuyi, Ayodeji Ajuwon, Abiola Oyeronke Akintobi},
title = {Advancements in Real-Time Payment Systems: A Review of Blockchain and AI Integration for Financial Operations},
journal = {Iconic Research And Engineering Journals},
year = {2021},
volume = {4},
number = {8},
pages = {206-221},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708928.pdf},
abstract = {Real-time payment systems have revolutionized the financial landscape by enabling instantaneous transfer of funds, improving liquidity, and enhancing customer experience. Recent advancements focus on integrating emerging technologies such as blockchain and Artificial Intelligence (AI) to address existing challenges related to security, transparency, scalability, and fraud prevention. This review examines the current state of blockchain and AI integration within real-time payment systems, highlighting their synergistic potential to transform financial operations. Blockchain technology offers a decentralized, immutable ledger that enhances transactional transparency and security while reducing the reliance on centralized intermediaries. Its application in real-time payments facilitates faster settlement, lowers transaction costs, and mitigates fraud risks through cryptographic verification and consensus mechanisms. However, scalability and interoperability remain challenges, prompting ongoing research into hybrid blockchain models and cross-chain protocols. Simultaneously, AI contributes to real-time payments by improving risk assessment, fraud detection, and operational efficiency. Machine learning algorithms analyze transactional data patterns in real time to identify suspicious activities, optimize payment routing, and personalize customer interactions. AI-powered automation also streamlines compliance processes, reducing manual intervention and associated errors. The integration of blockchain and AI creates a robust ecosystem for real-time payment systems, combining blockchain?s secure infrastructure with AI?s intelligent analytics. This fusion supports enhanced decision-making, adaptive fraud prevention, and dynamic system optimization, enabling financial institutions to meet evolving regulatory and customer demands. This review synthesizes recent academic and industry research to map technological trends, identify implementation challenges, and propose future directions. It underscores the importance of developing standardized protocols, addressing privacy concerns, and fostering collaboration between technology providers, regulators, and financial institutions. The findings suggest that blockchain and AI integration holds significant promise for the next generation of real-time payment systems, offering improved security, efficiency, and inclusivity in financial operations worldwide.},
keywords = {Advancements, Real-time, Payment systems, Blockchain, AI integration, Financial operations},
month = {February},
}