MATHEMATICAL TECHNIQUES IN RISK MANAGEMENT: ANALYZING FINANCIAL STABILITY OF BUSINESSES IN GHANA

This research explores the application of mathematical techniques in risk management to enhance financial stability for businesses in Ghana. Using a mixed-method approach, data were collected through surveys, financial reports, and interviews, focusing on models such as Monte Carlo simulations, Value at Risk (VaR), and Probability of Default (PD). Findings reveal that Monte Carlo simulations explained 82% of variance in revenue predictability, while VaR highlighted the mining sector’s highest risk exposure at GHS 3,000,000 under a 99% confidence level. Moreover, the adoption of neural network models reduced risk exposure by 20% in the technology sector. Challenges such as limited technical expertise and resource constraints were identified as barriers to adopting these techniques. The study concludes that mathematical tools significantly enhance risk mitigation and recommends capacity building, policy support, and sector-specific strategies to optimize financial decision-making.