The Remittance Blueprint: Data-driven Intelligence for Sri Lanka

Remittances — money sent home by migrant workers — are a lifeline for many developing economies, yet surprisingly hard to forecast reliably. This study applies rigorous time-series and machine learning methods to 32 years of Sri Lankan migration and remittance data, finding that external factors like exchange rates and global oil prices drive inflows far more than domestic indicators. A multivariate Ridge Regression model outperforms traditional approaches by 73.8% in accuracy, projecting 2026 remittances at approximately USD 9 billion. These findings can inform central bank policy, foreign exchange management, diaspora engagement strategies, and international development planning in remittance-dependent economies across South and Southeast Asia. Authors: Dhinanjaya Fernando, Dinura Ginige, Kalana Lakshan, Chanupa Gurusinghe, Lasana Pahanga, Subavarshana Arumugam, Sandeepa Weerasekara, Sandareka Wickramanayake, Nisansa de Silva Paper: https://arxiv.org/abs/2606.28190v1