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ORIGINAL RESEARCH article

Front. Artif. Intell.
Sec. AI in Finance
Volume 8 - 2025 | doi: 10.3389/frai.2025.1510341
This article is part of the Research Topic Applications of AI and Machine Learning in Finance and Economics View all 4 articles

Transfer Learning for Predicting of Gross Domestic Product (GDP) Growth Based on Remittance Inflows using RNN-LSTM Hybrid Model: A Case Study of The Gambia

Provisionally accepted
Haruna Jallow Haruna Jallow 1*Ronald Waweru Mwangi Ronald Waweru Mwangi 2Alieu Gibba Alieu Gibba 3Herbert Imboga Herbert Imboga 2
  • 1 Pan African University Institute of Basic Sciences, Technology and Innovation, Nairobi, Kenya
  • 2 Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
  • 3 School of Business and Public Administration, University of The Gambia, Kanifing, Gambia

The final, formatted version of the article will be published soon.

    3Insights into the magnitude and performance of an economy are crucial, with the growth rate of4real GDP frequently used as a key indicator of economic health, highlighting the importance of5the Gross Domestic Product (GDP). Additionally, remittances have drawn considerable global6interest in recent years, particularly in The Gambia. This study introduces an innovative model, a7hybrid of recurrent neural network and long-short-term memory (RNN-LSTM), to predict GDP8growth based on remittance inflow in The Gambia. The model integrates data sourced both from the World Bank Development Indicators and the Central Bank of The Gambia (1966-2022). Pearson’s correlation was applied to detect and choose the variables that exhibit the strongest relationship with GDP and remittances. Furthermore, a parameter transfer learning technique was employed to enhance the model’s predictive accuracy. The hyperparameters of the model were fine-tuned through a random search process, and Its effectiveness was assessed using RMSE, MAE, MAPE, and R2 metrics. The research results show, first, that it has good generalization capacity, with stable applicability in predicting GDP growth based on remittance inflows. Second, as compared to standalone models the suggested model surpassed in term of predicting accuracy attained the highest R2 score of 91.285%. Third, the predicted outcomes further demonstrated a strong and positive relationship between remittances and short-term economic growth. This paper addresses a critical research gap by employing artificial intelligence (AI) techniques to forecast GDP based on remittance inflows.

    Keywords: Economic indicators, Gross Domestic Product, LSTM, prediction, remittances, RNN, Survey, the Gambia

    Received: 12 Oct 2024; Accepted: 15 Jan 2025.

    Copyright: © 2025 Jallow, Mwangi, Gibba and Imboga. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Haruna Jallow, Pan African University Institute of Basic Sciences, Technology and Innovation, Nairobi, Kenya

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