Khan, Hafiz Aziz and Hossain, Mohammad Sazzad and HOSSAIN, MD SHAHADAT and ALI, MOHAMMAD and Soumik, MD Shadman and Hussain, Mohammad Kabir and Khan, MD Minar and RAHAMAN, MD ARIFUR (2025) Business Intelligence Dashboards for Real-Time Financial Risk Monitoring. TIJER – INTERNATIONAL RESEARCH JOURNAL, 12 (10). pp. 486-498. ISSN 2349-9249

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Abstract

BI dashboards have emerged as fundamental tools of real time financial risk assessment to allow decisionmakers to transform high-velocity, high-variety data into actionable understanding. Capital markets and fintech and payment ecosystems Capital Risk exposures in banking can change by the minute as liquidity positions, counterparty reliability, and market indicators change. The traditional periodic reports notwithstanding their importance in the governance do not have the strength to reveal the emergent anomalies at a rate faster to eliminate losses. The paper will create and assess a BI dashboard architecture that incorporates streaming pipelines, risk metrics and alerts that are curated, and drill through diagnostics associated with both financial and operational data streams. The implementation is a mixture of descriptive, diagnostic, and predictive analytics that transforms intricate risk constructs (e.g. liquidity coverage, credit deterioration, fraud propensity, cyber anomalies) into role specific analyst, managerial, and executive views. It also integrates data governance, model controls and explainability controls in order to uphold trust in dashboard outputs. The results show that effective dashboards enhance the time-to-insight, lessen the noise in the form of meaningful thresholds, and enable proactive interventions. It is grounded on three contributions: (1) an implementation playbook, which bridges streaming data engineering, human-neutral visualization, and governance; (2) a catalog of KPIs-and-alerts applicable to liquidity, market, credit, fraud, and operational risk; and (3) a practical reference architecture, based on real-time risk dashboards. Finally, the study is completed with the recommendations of the phased adoption, model validation, and the continuous improvement cycle helping to align dashboards with the changing regulatory and business needs

Item Type:	Article
Subjects:	A General Works > AI Indexes (General)
Depositing User:	admin eprints
Date Deposited:	07 Jun 2026 23:33
Last Modified:	07 Jun 2026 23:33
URI:	http://eprints.umsida.ac.id/id/eprint/16534

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