2019· International Journal of Commerce, Finance and Digital Economy· Vol 2, pp. 1-17· 0 citations
TL;DR
The findings indicate that BI frameworks significantly enhance financial planning, governance, and strategic decision-making despite challenges related to data integration, organizational resistance, and implementation costs.
Abstract
Business Intelligence (BI) systems have become essential tools for strategic financial management by transforming large volumes of financial data into actionable insights for planning, forecasting, budgeting, investment analysis, risk management, and decision-making. Unlike traditional financial management methods that relied on historical reporting and manual analysis, BI technologies automate data collection, integrate information from multiple sources, and provide real-time analytical capabilities. Key BI components such as data warehousing, OLAP, dashboards, reporting tools, data mining, and predictive analytics help organizations monitor performance, identify trends, assess risks, and develop evidence-based financial strategies. BI adoption across industries, including banking, manufacturing, healthcare, and telecommunications, has improved decision quality, reporting efficiency, forecasting accuracy, financial transparency, and organizational competitiveness. This study presents a systematic review of BI systems in strategic financial management before 2019, examining their evolution, architecture, analytical methods, and impact on financial performance. The findings indicate that BI frameworks significantly enhance financial planning, governance, and strategic decision-making despite challenges related to data integration, organizational resistance, and implementation costs. Overall, BI technologies serve as a foundation for modern data-driven financial decision support systems.
The literature suggests that business analytics has evolved from a reporting tool into a strategic capability that supports evidence-based financial management, and organizations are likely to achieve greater value from business analytics when technological capabilities are combined with managerial expertise, sound governance, and a data-driven organizational culture.
Rohit R. Khot, Manoj Kumar, Shashank S. Channayyanavar· 0 citations
Big Data Analytics (BDA) is transforming corporate financial planning by enabling organizations to analyze large volumes of financial and business data for accurate, data-driven decision-making. Unlike traditional forecasting methods, BDA integrates machine learning, predictive analytics, cloud computing, and artificial intelligence to improve budgeting, cash flow forecasting, risk management, investment planning, and resource allocation. The proposed framework combines data preprocessing, predictive modeling, optimization, and business intelligence dashboards to enhance financial forecasting accuracy and operational efficiency. It also addresses challenges such as data privacy, cybersecurity, regulatory compliance, model interpretability, and computational scalability. Overall, the framework supports intelligent, scalable, and real-time financial planning for modern enterprises.
Seshagiri N· International Journal of Com...· 0 citations
This study examines how the integration of Artificial Intelligence (AI), Machine Learning (ML), and Strategic Human Resource Management (SHRM) can optimize financial risk management to anticipate financial crises and enhance corporate financial stability. The research employs a qualitative library research approach using primary and secondary data collected from books, peer-reviewed journals, scientific reports, and other relevant academic literature. Data were analyzed using content analysis to identify theoretical patterns, conceptual relationships, and emerging trends related to intelligent financial risk management. The findings indicate that AI and ML significantly improve financial forecasting, fraud detection, and risk prediction through advanced predictive analytics, while SHRM strengthens organizational capabilities by developing adaptive leadership, digital competencies, and strategic decision-making. The integration of these technological and organizational resources creates a comprehensive financial risk management framework that enhances organizational resilience and supports sustainable financial performance. The study concludes that combining AI, ML, and SHRM enables organizations to proactively manage financial uncertainty, improve crisis preparedness, and achieve long-term corporate financial stability in an increasingly dynamic business environment.
Haris Aulia Rahman, Ardilla Ayu Kirana, Moh. Sholeh et al.· Mandalika Journal of Busines...· 0 citations
- Finance operations are increasingly required to accelerate closing cycles, improve forecast accuracy, strengthen controls, and provide decision support without increasing administrative costs. Predictive analytics enables the anticipation of cash shortfalls, late payments, atypical transactions, workload peaks, and close delays. However, predictions alone do not improve processes unless they are translated into governed actions. This review analyses how predictive analytics and decision intelligence jointly enable business process improvement across procure-to-pay, order-to-cash, record-to-report, financial planning and analysis, treasury, and compliance. A structured integrative review of thirty publications from 2020 to 2025 synthesises research on business intelligence, process management, machine learning, process mining, automation, and responsible financial analytics. The analysis identifies four mechanisms of improvement: earlier exception detection, dynamic prioritisation, resource and working-capital optimisation, and closed-loop learning from decision outcomes. It further finds that value creation relies more on data quality, workflow integration, explainability, accountability, and feedback design than on model sophistication. This paper presents a Finance Decision Intelligence Improvement Framework that links event data, predictive systems, decision rules, human judgement, automated execution, and performance monitoring. The framework distinguishes prediction quality from decision quality and process value, thereby reducing the risk of technically accurate models that fail operationally. The review concludes that predictive analytics achieves sustainable finance improvement when integrated as a controlled decision service rather than as a stand-alone dashboard.
Muhammad Zeeshan Qureshi· Iconic research and engineer...· 0 citations
Business analytics has become an important component of strategic management today in banking, where large
volumes of customer, transaction, operational, and market data are generated. This study examines the role of
business analytics in strategic decision making with reference to HDFC Bank. It focuses on decision quality, risk
management, customer relationship management, operational effectiveness, predictive analytics, and business
intelligence. Primary information from employees was examined using percentage analysis. The findings indicate
that analytics improves decision quality, supports risk assessment, strengthens customer service, and assists
strategic planning. Overall, business analytics contributes to informed decisions, organizational performance,
competitiveness, and sustainable banking growth.
Keywords: HDFC Bank, Business Analytics, Business Intelligence, Strategic Decision Making, Data
Analytics
Banoth Julee, D. Lavanya· International Journal of Sci...· 0 citations
Intelligent accounting and tax management systems have become critical components of modern financial operations, enabling organizations to improve efficiency, accuracy, and regulatory compliance in an increasingly digital business environment. This paper explores innovative approaches to developing advanced accounting and tax systems that enhance financial performance and decision-making capabilities. The study examines the integration of emerging technologies such as artificial intelligence (AI), machine learning (ML), predictive analytics, cloud computing, and automated compliance monitoring within accounting frameworks. Particular attention is given to data-driven financial reporting, real-time tax assessment, fraud detection, and intelligent audit support mechanisms. The paper provides insights into the design, implementation, and optimization of next-generation accounting systems capable of managing complex financial data while ensuring transparency and compliance with evolving tax regulations. Through practical scenarios and industry-oriented applications, the study demonstrates how intelligent accounting and tax management platforms can improve operational effectiveness, reduce administrative costs, and support strategic financial governance in modern enterprises.
Areej Mustafa· Euro Vantage Journal of Arti...· 0 citations
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