Machine Learning Models for Predicting Migrant Remittance Flows: A Cross-Border Financial Analysis

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dc.contributor.authorDumanska, Ilona
dc.contributor.authorKuzmin, Andrii
dc.contributor.authorLevashenko, Vitaly
dc.contributor.authorLysak, Viktor
dc.contributor.authorHrytsyna, Lesia
dc.date.accessioned2026-02-17T14:27:02Z
dc.date.available2026-02-17T14:27:02Z
dc.date.issued2026-02-07
dc.description.abstractThe 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.
dc.identifier.citationDumanska I., Kuzmin A., Levashenko V., Lysak V., Hrytsyna L. Machine Learning Models for Predicting Migrant Remittance Flows: A Cross-Border Financial Analysis. CEUR Workshop Proceedings, Vol. 4163, 2026, p. 41-510. URL: https://ceur-ws.org/Vol-4163/paper4.pdf
dc.identifier.urihttps://elar.khmnu.edu.ua/handle/123456789/20790
dc.language.isoen
dc.publisherCEUR
dc.subjectmachine learning
dc.subjectremittances
dc.subjectmigration
dc.subjectmodel
dc.subjectdata
dc.subjectpredictor
dc.titleMachine Learning Models for Predicting Migrant Remittance Flows: A Cross-Border Financial Analysis
dc.typeСтаття
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Кафедра міжнародних економічних відносин