Unified Framework of Blockchain and AI for Business Intelligence in Modern Banking

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