Machine Learning Enabled Early Warning System For Financial Distress Using Real-Time Digital Signals

International Journal of Applied Mathematics Volume 38 No. 5 s, 2025 ISSN: 1311 - 1728 (printed version); ISSN: 1314 - 8060 (on - line version) Received: August 0 7, 2025 550 Abstract The growing instability of both global and domestic economic environments has increased the risk of financial distress at the household level. However, traditional econometric models often rely on delayed and aggregated data, limiting their effectiveness. This study introduces a machine learning - based early warning system that utilizes real - time digital and macroeconomic signals to identify financial distress in near real - time. Using a panel dataset of 750 households tracked over three monitoring rounds spa nning 13 months, the framework combines socioeconomic attributes, macroeconomic indicators (such as GDP growth, inflation, and foreign exchange fluctuations), and digital economy measures (including ICT demand and market volatility). Through data preproces sing and feature engineering, we introduce lagged variables, volatility measures, and interaction terms to capture both gradual and sudden changes in financial stability. We benchmark baseline classifiers, such as logistic regression and decision trees, ag ainst advanced ensemble models including random forests, XGBoost, and LightGBM. Our results indicate that the engineered features from the digital economy significantly enhance predictive accuracy. The system performs reliably for both binary distress dete ction and multi - class severity classification, with SHAP - based explanations identifying inflation volatility and ICT demand as key predictors. Crucially, the framework is International Journal of Applied Mathematics Volume 38 No. 5 s, 2025 ISSN: 1311 - 1728 (printed version); ISSN: 1314 - 8060 (on - line version) Received: August 0 7, 2025 551 By implementing machine learning in a transparent and interpretable manner, this study demonstrates the feasibility and impact of providing near - real - time early warnings of financial distress. This offers actionable insights that can strengthen household resilience and guide preemptive intervention strategies. Keywords: Financial Distress, Early Warning Systems, Machine Learning, Digital Economy, Temporal Classification, Explainable AI 1. Introduction 1.1 Background and Motivation The prediction of financial distress has long been recognized as a critical element for ensuring economic resilience and mitigating systemic risk across households, firms, and national economies.