Integrating computational finance, machine learning, and risk analytics for optimized financial planning and analysis strategies

Omoruyi, Nosakhare (2025) Integrating computational finance, machine learning, and risk analytics for optimized financial planning and analysis strategies. World Journal of Advanced Research and Reviews, 26 (1). pp. 684-700. ISSN 2581-9615

In today’s dynamic financial landscape, the integration of computational finance, machine learning, and risk analytics is revolutionizing financial planning and analysis (FP&A). Computational finance leverages mathematical modeling, numerical simulations, and algorithmic techniques to optimize investment strategies and capital allocation. Meanwhile, machine learning enhances predictive capabilities, enabling data-driven decision-making that improves portfolio performance, market forecasting, and credit risk assessment. Risk analytics complements these advancements by quantifying uncertainties, mitigating financial volatility, and ensuring robust risk management frameworks. The convergence of these technologies offers a more refined and adaptive approach to financial strategy development. Computational finance provides the foundation for quantitative models, while machine learning algorithms refine predictions by identifying patterns in vast financial datasets. Risk analytics further strengthens financial decision-making by assessing potential vulnerabilities, stress testing scenarios, and ensuring compliance with regulatory requirements. This integration is particularly crucial in corporate finance, investment banking, and fintech sectors, where accurate forecasting and risk mitigation directly impact profitability and sustainability. As financial markets become increasingly complex, the adoption of advanced AI-driven risk analytics and machine learning-based forecasting models enhances efficiency, reduces operational risks, and improves financial resilience. However, challenges such as data quality, model interpretability, and ethical considerations must be addressed to fully realize the potential of these technologies. This study explores the synergy between computational finance, machine learning, and risk analytics, emphasizing their role in shaping the future of optimized FP&A strategies. By leveraging these innovations, financial institutions can enhance decision-making accuracy, improve regulatory compliance, and optimize financial performance in a rapidly evolving economic environment.