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Документ Machine Learning Models for Predicting Migrant Remittance Flows: A Cross-Border Financial Analysis(CEUR, 2026-02-07) Dumanska, Ilona; Kuzmin, Andrii; Levashenko, Vitaly; Lysak, Viktor; Hrytsyna, LesiaThe paper proposes a machine learning framework for forecasting migrant remittance flows, focusing on the Ukraine–Poland corridor during 2022–2025. The methodology integrates diverse data sources— migration volumes, conflict intensity indices, exchange rates, host-country employment rates, and social sentiment—into a unified time-series dataset. Four models are evaluated: Linear Regression (baseline), Random Forest, XGBoost, and LSTM. LSTM is expected to outperform others due to its ability to capture long-term dependencies and crisis-driven shocks. Feature-importance analysis will likely highlight migration volume, employment rate, and exchange rate as key predictors, while sentiment data should enhance short-term responsiveness. The case study illustrates how remittance flows correlate with refugee inflows, labor integration, and policy interventions. Overall, the framework shows the potential of deep learning and ensemble methods to improve forecasting under humanitarian and economic stress.