An Integrated Predictive Analytics Model for Enhancing Strategic Financial Forecasting and Decision Accuracy
Year: 2024
Type: article
Abstract: Accurate financial forecasting and strategic decision-making are essential for organizational sustainability, competitiveness, and growth. This study presents an integrated predictive analytics model designed to enhance the accuracy and reliability of financial forecasts, supporting data-driven strategic decision-making. The model leverages historical financial data, operational metrics, and market indicators to generate forward-looking insights into revenue, expenses, cash flows, and profitability. By integrating predictive algorithms, statistical modeling, and scenario analysis, the framework enables organizations to anticipate financial trends, identify potential risks, and optimize resource allocation. A core feature of the model is its ability to integrate multiple sources of financial and operational data, providing a holistic view of organizational performance. Predictive analytics facilitates the identification of patterns and correlations that are not immediately apparent through conventional analysis, thereby improving forecast precision. Scenario-based modeling further allows management to simulate the potential impact of market fluctuations, regulatory changes, and operational contingencies on financial outcomes, supporting risk-adjusted strategic planning. The model also incorporates real-time monitoring capabilities, enabling executives to make timely, informed decisions based on current financial conditions and projected trends. The adoption of this integrated predictive analytics model contributes to enhanced organizational agility, operational efficiency, and financial resilience. By improving the accuracy of forecasts and linking financial insights to strategic objectives, organizations can optimize investment decisions, prioritize initiatives, and maintain liquidity under varying market conditions. Empirical validation across multiple industries demonstrates the model’s effectiveness in reducing forecast errors, strengthening financial control, and increasing confidence among stakeholders, including investors, management, and regulators. This integrated predictive analytics framework provides a robust, data-driven approach to strategic financial forecasting, combining predictive accuracy, risk assessment, and scenario analysis to improve decision-making quality and support sustainable organizational growth.
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4416399066