Artificial Intelligence (AI) in Financial Forecasting: A Data-Driven Approach to Risk Mitigation - Sunil Kumar Das; Dr. Prafulla Kumar Swain (The Bioscan, 2025)
The increasing complexity of financial markets has heightened the need for accurate and efficient forecasting techniques. Traditional financial forecasting models often struggle to capture market volatility, leading to suboptimal decision-making and increased financial risk. The effects of AI-powered predictive analytics on business planning, risk management, and financial forecasting are studied in this research.. Using a mixed-methods approach, data were collected from 350 finance professionals across major Indian financial hubs. The study employed statistical techniques, including ANOVA and correlation analysis, to measure the effectiveness of AI-driven forecasting models. The results indicate that AI implementation led to a 17% increase in forecast accuracy and a 22% reduction in forecasting errors, with significant improvements observed across multiple industries (p < 0.01). Furthermore, a strong positive correlation (r = 0.72) was found between AI adoption and enhanced risk management strategies, suggesting that AI not only improves financial prediction but also strengthens proactive risk mitigation measures. Corporate planners' qualitative observations showed that by offering real-time market trend and risk assessment, AI-driven analytics improves strategic decision-making.By proving its capacity to increase accuracy, efficiency, and risk management skills, this study adds to the increasing corpus of studies on artificial intelligence in financial forecasting. The findings suggest that firms adopting AI-based predictive analytics can achieve greater financial stability and resilience to market fluctuations. Future research should explore AI’s role in long-term financial sustainability and its impact on investment strategies in dynamic market environments.