Unified Framework of Blockchain and AI for Business Intelligence in Modern Banking
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V3I4P105Keywords:
Blockchain, Artificial Intelligence, Real-time Business Intelligence, Digital Banking, Smart Contracts, Explainable AI, Fraud Detection, Predictive Analytics, Data Security, Distributed Ledger Technology (DLT)Abstract
Combining blockchain with AI is heavily transforming digital banking by facilitating intelligent, secure, and real-time decision-making processes. While financial institutions move away from legacy systems toward data-driven platforms, there is a growing need for real-time BI. Most transitional BI tools are thus limited by the presence of centralized data silos, slow data pipelines, and lack of transparency. In comparison, blockchain ensures a decentralized tamper-proof ledger infrastructure that gives assurances of data integrity, traceability, and auditability, whereas AI offers tools for extracting actionable insights such as predictive analytics, anomaly detection, and natural language processing. In pausing this study turns its focus on the synergistic integration of blockchain and AI toward real-time BI framework developments within digital banking ecosystems. A multi-layered architecture is thereby proposed wherein blockchain captures, validates, and stores transactional and behavioral data, whereas a layer of AI modules sit atop this secured data layer to generate intelligent patterns in real time. This research puts forward supervised learning models such as XGBoost and LSTM for fraud prediction and customer segmentation, while smart contracts trigger compliance workflows and rule-based alerts. Explainable AI techniques (e.g. SHAP, LIME) are also integrated for purposes of interpretability and regulatory compliance. Results indicate that fraud detection accuracy has been improved to 96%, latency to real-time insight generation has dropped substantially to a negligible level, and trust in AI results has been strengthened by the transparency of blockchain logging. Case studies of customer behavior analytics, transaction anomaly monitoring, and credit scoring show how this integrated approach outperforms traditional data infrastructures. Besides, this work has put forward other discussions on challenges in implementation such as interoperability, data privacy, computational costs, and regulatory acceptance. This research contributes to the fast-evolving discourse on digital transformation in finance, offering a scalable, secure, and interpretable blueprint for next-generation banking systems, which will take advantage of blockchain and AI in providing real-time intelligence
References
[1] Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052
[2] Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785
[3] Gai, K., Qiu, M., & Sun, X. (2018). A survey on FinTech. Journal of Network and Computer Applications, 103, 262–273. https://doi.org/10.1016/j.jnca.2017.10.011
[4] Goodman, B., & Flaxman, S. (2017). European Union regulations on algorithmic decision-making and the right to explanation’. AI Magazine, 38(3), 50–57. https://doi.org/10.1609/aimag.v38i3.2741
[5] Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. https://proceedings.neurips.cc/paper_files/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html
[6] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?” Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144. https://doi.org/10.1145/2939672.2939778
[7] Aste, T., Tasca, P., & Di Matteo, T. (2017). Blockchain technologies: The foreseeable impact on society and industry. Computer, 50(9), 18–28. https://doi.org/10.1109/MC.2017.3571056
[8] Christidis, K., & Devetsikiotis, M. (2016). Blockchains and smart contracts for the Internet of Things. IEEE Access, 4, 2292–2303. https://doi.org/10.1109/ACCESS.2016.2566339
[9] Conti, M., Kumar, S., Lal, C., & Ruj, S. (2018). A survey on security and privacy issues of Bitcoin. IEEE Communications Surveys & Tutorials, 20(4), 3416–3452. https://doi.org/10.1109/COMST.2018.2842460
[10] Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608. https://arxiv.org/abs/1702.08608
[11] Hevner, A. R., March, S. T., & Park, J. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105. https://doi.org/10.2307/25148625
[12] Kute, D. V., Pradhan, B., Shukla, N., & Alamri, A. (2021). Deep learning and explainable artificial intelligence techniques applied for detecting money laundering: A critical review. IEEE Access, 9, 147232–147251. https://doi.org/10.1109/ACCESS.2021.3124356
[13] Li, Y., Li, M., & He, Y. (2020). Fraud detection using ensemble learning in electronic transactions. Expert Systems with Applications, 139, 112873. https://doi.org/10.1016/j.eswa.2019.112873
[14] Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35. https://doi.org/10.1145/3457607
[15] Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. White Paper. https://bitcoin.org/bitcoin.pdf
[16] Nguyen, G., Dlugolinsky, S., Bobák, M., Tran, V., García, Á. L., Heredia, I., ... & Hluchý, L. (2019). Machine learning and deep learning frameworks and libraries for large-scale data mining: A survey. Artificial Intelligence Review, 52(1), 77–124. https://doi.org/10.1007/s10462-018-09679-z
[17] Nassar, M., Salah, K., Ur Rehman, M. H., & Jayaraman, R. (2020). Blockchain for explainable and trustworthy artificial intelligence. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(5), e1375. https://doi.org/10.1002/widm.1375
[18] Doddipatla, L., Ramadugu, R., Yerram, R. R., & Sharma R, S. T. (2021). Exploring the role of biometric authentication in modern payment solutions. European Chemical Bulletin, 220–229. https://doi.org/10.53555/ecb.v10:i1.17783
[19] Pilkington, M. (2016). Blockchain technology: Principles and applications. In Research Handbook on Digital Transformations (pp. 225–253). Edward Elgar. https://doi.org/10.4337/9781784717766.00019
[20] Russell, S. J., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
[21] Tapscott, D., & Tapscott, A. (2016). Blockchain revolution: How the technology behind bitcoin is changing money, business, and the world. Penguin.
[22] Walambe, R., Kolhatkar, A., Ojha, M., Kademani, A., & Raut, R. D. (2020). Integration of explainable AI and blockchain for credit risk assessment. International Advanced Research Journal in Science, Engineering and Technology, 7(6), 15–26. https://doi.org/10.1007/s10462-020-09845-6
[23] Zikopoulos, P., Eaton, C., deRoos, D., Deutsch, T., & Lapis, G. (2012). Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill.
[24] Binns, R., Veale, M., Van Kleek, M., & Shadbolt, N. (2018). 'It's reducing a human being to a percentage': Perceptions of justice in algorithmic decisions. CHI Conference on Human Factors in Computing Systems, 1–14. https://doi.org/10.1145/3173574.3173951
[25] Weller, A. (2019). Transparency: Motivations and challenges. In Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (pp. 23–40). Springer. https://doi.org/10.1007/978-3-030-28954-6_2
[26] L. Doddipatla, R. Ramadugu, R. R. Yerram, and T. Sharma, "Exploring The Role of Biometric Authentication in Modern Payment Solutions," International Journal of Digital Innovation, vol. 2, no. 1, 2021.
