The Remittance Blueprint: Data-driven Intelligence for Sri Lanka
2026-06-26 • Machine Learning
Machine LearningArtificial Intelligence
AI summaryⓘ
The authors studied Sri Lankan migration and money sent back home over 32 years. They found that changes in exchange rates and global oil prices mainly affect remittance amounts, more than local economic factors. They used advanced data analysis and machine learning, showing that newer models predict remittances much better than older methods. Their forecast suggests remittances will be about 9 billion USD in 2026 if things stay stable. The authors recommend focusing on good exchange rate policies and formal financial systems to help the economy.
remittancesexchange ratetime-series modelingmachine learningstationarityimpulse response analysisRidge RegressionVAR/VECMSARIMAmigration economics
Authors
Dhinanjaya Fernando, Dinura Ginige, Kalana Lakshan, Chanupa Gurusinghe, Lasana Pahanga, Subavarshana Arumugam, Sandeepa Weerasekara, Sandareka Wickramanayake, Nisansa de Silva
Abstract
This study analyzes Sri Lankan migration and remittances over 32 years (1994-2025). Using a 384-month harmonized dataset, we apply exploratory data analysis, stationarity corrected time-series modeling (ADF, Johansen, VAR/VECM), and supervised learning. Results reveal remittance inflows are primarily driven by external macroeconomic variables, specifically exchange rate dynamics and global oil prices, rather than domestic indicators. Impulse response analysis confirms the asymmetric impact of currency depreciation and oil price shocks. Predictively, multivariate machine learning models outperform traditional univariate approaches; Ridge Regression achieves a 73.8% accuracy improvement over SARIMA (Annualized RMSE: USD 494.8 Mn). The optimized framework projects 2026 remittances at USD 9,001 million under stable conditions. These findings highlight the structural dependence of remittances on global economies, emphasizing the need for robust exchange rate policies, skilled migration, and formal financial channels to enhance long-term economic resilience.