Journal of Advances in Developmental Research

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Streaming Analytics vs. Traditional BI in Financial Decision-Making: A Comparative Study

Author(s) Pavan Kumar Mantha
Country United States
Abstract Financial institutions operate under conditions of high transaction volumes, intense regulatory scrutiny, elevated risk exposure, and rapidly changing customer demands. Conventional Business Intelligence (BI) systems — built on batch-based extraction, transformation, and loading (ETL) processes, centralized data warehouses, and standardized dashboards — have been the workhorse of financial decision-making for decades. These systems excel in structured reporting, auditability, and historical analysis, making them essential for regulatory compliance, financial consolidation, and performance management. Nevertheless, the growing speed of digital transactions, online payment mechanisms, and remote interactions with customers has revealed what has limited the power of delayed insights on cases like fraud detection, dynamic credit risk determination, and personalized interactions. Streaming analytics have been developed as a supportive paradigm where data flows are processed continuously as new events take place and it becomes possible to have near real-time insights, alerts and automatic reactions. In comparison to the traditional BI that is about retrospective analysis, streaming analytics is immediate, governed by time, and decision-making. The implementation of streaming analytics in financial services has its potential albeit questioning in its foundational issues of governance, its explanation, cost, the readiness of organizations, as well as its acceptance by the regulatory community. The paper is a full-fledged comparative analysis of streaming architecture and conventional BI with respect to financial decision-making. It explores their architectural roots, latency aspects of their choice and appropriateness in various financial applications. A critical evaluation of the system defines the trade-offs between stability and speed, interpretability and automation, cost effectiveness and complexity of operation. The governance, compliance and explainability are particularly focused on, which are the dimensions that are inadequately explored in the current literature. The paper arrives at the conclusion that streaming analytics is not the replacement of traditional BI but rather its supplement. A financial institution has the most practical way out through a hybrid analytical architecture, which is based on decision criticality and sensitivity to time. This work presents a systematic decision-making model that would be used by the executive team and architects in choosing the most suitable analytical paradigm when making particular financial decisions.
Keywords Streaming Analytics, Business Intelligence, Financial Decision-Making, Real-Time Analytics, Governance, Decision Latency, Regulatory Compliance
Field Engineering
Published In Volume 13, Issue 1, January-June 2022
Published On 2022-06-03
DOI https://doi.org/10.71097/IJAIDR.v13.i1.2031
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Crossref DOI prefix of IJAIDR is 10.71097/IJAIDR

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