AI-Driven Financial Analytics: Enhancing Forecast Accuracy, Risk Management, and Decision-Making in Corporate Finance - Celestin, Mbonigaba; Mishra, Anjay Kumar (Janajyoti Journal, 2025)
The integration of Artificial Intelligence (AI) in financial analytics has significantly enhanced corporate finance by improving forecasting accuracy, risk management, and decision-making efficiency. This study examines AI-driven financial analytics, focusing on its transformative role in corporate finance. The research employs a mixed-methods approach, incorporating predictive modeling, regression analysis, and AI impact assessments to analyze financial performance before and after AI implementation. The findings reveal that AI-driven forecasting models improve prediction accuracy by up to 92%, significantly outperforming traditional statistical methods. AI-based risk management systems enhance risk detection rates by 90%, mitigating financial losses more effectively. Additionally, AI-driven decision-making tools reduce processing time by 85%, enabling firms to make data-driven strategic decisions more rapidly. Statistical analysis confirms a moderate positive correlation (r = 0.396) between AI-driven forecasting and financial performance, while regression models indicate that AI-driven risk management (β = 1.246) has the strongest impact on corporate financial optimization. The study concludes that AI-driven financial analytics enhances corporate resilience, improves risk mitigation, and streamlines financial decision-making. It recommends that firms invest in AI-driven financial strategies, enhance data governance, and adopt regulatory-compliant AI frameworks to maximize financial performance.