A Big Data–Driven Optimization Framework for Enterprise Financial Management: Enhancing Predictive Decision-Making, Risk Control, and Computational Efficiency

Abstract

The exponential growth of financial, operational, and market data has transformed the landscape of enterprise financial management, exposing critical limitations in conventional forecasting, budgeting, and risk assessment practices. Traditional financial systems characterized by fragmented data silos, rule-based decision routines, and delayed reporting cycles are no longer capable of supporting the speed, complexity, and granularity required in dynamic business environments. To address these challenges, this study proposes a big data–driven optimization framework that leverages large-scale data integration, advanced analytics, and intelligent optimization models to strengthen predictive decision-making, enhance enterprise-wide risk control, and achieve high computational efficiency. The proposed framework is structured around four interconnected layers. First, a multi-source data acquisition and integration layer consolidates heterogeneous data streams including ERP financial records, transactional logs, market indicators, supply chain data, customer interactions, regulatory updates, and alternative external datasets. This unified data repository provides comprehensive contextual visibility required for both micro-level financial insights and macro-level strategic planning. Second, a scalable big data infrastructure layer is designed using distributed storage, parallel computing, and streaming architectures capable of processing high-volume and high-velocity financial workloads with minimal latency. Third, an analytics and optimization intelligence layer integrates machine learning models for cash flow forecasting, anomaly detection, credit risk scoring, and cost prediction, combined with optimization algorithms for liquidity allocation, capital structure management, investment decisions, and real-time risk mitigation. Finally, a decision-support and visualization layer translates analytical outputs into actionable insights through interactive dashboards, predictive alerts, scenario analyses, and explainable AI modules tailored for CFOs, controllers, auditors, and risk management units. Through the fusion of predictive analytics, real-time data orchestration, and algorithmic optimization, the framework enables enterprises to transition from descriptive financial reporting toward proactive, predictive, and prescriptive financial management. This transition improves forecasting accuracy, strengthens resilience against market shocks, identifies early risk signals, and supports data-driven financial governance. The study further highlights the computational efficiency benefits achieved through distributed processing, workload balancing, and model optimization, significantly reducing processing time and enabling real-time decision flows. Overall, this research contributes a robust conceptual foundation and a scalable architectural pathway for deploying intelligent, big data–powered financial management systems. The framework offers a transformative direction for organizations seeking to enhance financial agility, strengthen risk governance, and achieve operational excellence in increasingly volatile and data-intensive business ecosystems