Machine Learning and Stability: Predicting Economic and Financial Risks in Nagpur, Maharashtra

Abstract

The increasing financial uncertainty in urban India has heightened the need for predictive tools that can assess economic and financial risks at the individual and community level. This study explores the relationship between financial stress, mental and physical health, and awareness of machine learning (ML) applications in Nagpur, Maharashtra. Using survey responses (n = 164), statistical analyses including descriptive measures, Chi-square tests, and Spearman’s rank correlation were employed. Results indicate that financial stress significantly correlates with both mental and physical health challenges (ρ = 0.46 and ρ = 0.33 respectively), and is negatively associated with overall self-rated health (ρ = -0.24, p < 0.01). Moreover, mental and physical health difficulties are strongly interrelated (ρ = 0.62, p < 0.001), highlighting the compounding effect of economic strain. Demographic variables such as gender and income showed no significant influence on financial stress or health outcomes. While respondents expressed moderate awareness of ML, their willingness to share financial and health data remained cautious. These findings underscore the potential of ML-based predictive frameworks in identifying economic vulnerabilities, while also emphasizing the importance of ethical and transparent data use to build public trust.