Revolutionizing financial risk assessment through deep learning-driven business analytics for maximized ROI and Resilience

Odion, Courage Oko and Okunuga, Aishat and Okunbor, Oluwatofunmi Ibukun (2025) Revolutionizing financial risk assessment through deep learning-driven business analytics for maximized ROI and Resilience. World Journal of Advanced Research and Reviews, 25 (1). pp. 2444-2461. ISSN 2581-9615

In an era of heightened financial complexity and volatility, the need for robust, dynamic risk assessment frameworks has become paramount. Deep learning, a powerful branch of artificial intelligence, is transforming business analytics by enabling real-time financial modelling, predictive insights, and data-driven decision-making. Unlike traditional methods, deep learning excels in handling vast, complex datasets, identifying intricate patterns, and delivering predictive insights that enhance accuracy and responsiveness. Algorithms such as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Generative Adversarial Networks (GANs) empower businesses to predict market trends, assess credit risks, and identify potential operational vulnerabilities. At a broader level, deep learning integrates structured and unstructured data sources, providing actionable insights that support strategic planning and resource optimization. It enables businesses to mitigate risks proactively, allocate capital efficiently, and achieve resilience against economic disruptions. The synergy of deep learning with real-time data integration systems further facilitates adaptive strategies, ensuring financial stability and maximized returns on investment (ROI). Focusing on specific applications, the paper examines case studies where deep learning has driven financial success. Examples include improved fraud detection in banking, enhanced credit risk assessment in lending institutions, and optimized investment strategies in asset management. The findings underscore the transformative potential of deep learning in revolutionizing financial risk assessment and fostering sustainable business growth. The paper concludes with recommendations for implementing deep learning-driven business analytics, emphasizing the need for collaboration between financial institutions, technology providers, and regulatory bodies to unlock its full potential and ensure compliance.