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How to cite this paper
APA: Lopez, J., Gonzales, A & Condori, P. (2025). Predictive models based on machine learning to analyze the adoption of digital payments in Latin America and the Caribbean. International Journal of Data and Network Science, 9(3), 411-418.
Chicago/Turabian: Lopez, J., Gonzales, A & Condori, P. 2025. "Predictive models based on machine learning to analyze the adoption of digital payments in Latin America and the Caribbean." International Journal of Data and Network Science 9, no. 3 (2025): 411-418.
AMA: Lopez, J., Gonzales, A & Condori, P. Predictive models based on machine learning to analyze the adoption of digital payments in Latin America and the Caribbean. International Journal of Data and Network Science. 2025;9(3):411-418.
References
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Al-Okaily, M., Lutfi, A., Alsaad, A., Taamneh, A., & Alsyouf, A. (2020). The Determinants of Digital Payment Systems’ Acceptance under Cultural Orientation Differences: The Case of Uncertainty Avoidance. Technology in Society, 63(101367). https://doi.org/10.1016/j.techsoc.2020.101367
Aurazo, J., & Vega, M. (2021). Why people use digital payments: Evidence from micro data in Peru. Latin American Journal of Central Banking, 2(4). https://doi.org/10.1016/j.latcb.2021.100044
Bara, A., & Mudzingiri, C. (2016). Financial innovation and economic growth: Evidence from Zimbabwe. Investment Management and Financial Innovations, 13(2), 65–75. https://doi.org/10.21511/imfi.13(2).2016.07
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17-August-2016. https://doi.org/10.1145/2939672.2939785
Chou, Y. K. (2007). Modeling financial innovation and economic growth: Why the financial sector matters to the real economy. Journal of Economic Education, 38(1), 78–90. https://doi.org/10.3200/JECE.38.1.78-91
Demirgüç-Kunt, A., Klapper, L., Singer, D., & Ansar, S. (2022). The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. World Bank. https://doi.org/10.1596/978-1-4648-1897-4
Development Research Group, Finance Unit and Private Sector Development. (2022). Global Financial Inclusion (Global Findex) Database 2021. World Bank, Development Data Group. https://doi.org/10.48529/JQ97-AJ70
Gardner, J. R., Kusner, M. J., Xu, Z. E., Weinberger, K. Q., & Cunningham, J. P. (2014). Bayesian optimization with inequality constraints. ICML, 2014, 937–945.
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., … Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585(7825), 357–362. https://doi.org/10.1038/s41586-020-2649-2
Hjelholt, M., & Damsgaard, J. (2012). The genesis and evolution of digital payment platforms - A framework for understanding payment transition routes. ECIS 2012.
Ismail, Q. F., Al-Sobh, E. S., Al-Omari, S. S., Bani Yaseen, T. M., & Abdullah, M. A. (2021). Using Machine Learning Algorithms to Predict the State of Financial Inclusion in Africa. 12th International Conference on Information and Communication Systems, ICICS 2021. https://doi.org/10.1109/ICICS52457.2021.9464590
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T. Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 2017-December.
Klapper, L., & Singer, D. (2017). The opportunities and challenges of digitizing government-to- person payments. World Bank Research Observer, 32(2), 211–226. https://doi.org/10.1093/wbro/lkx003
Lee, H. S., Kuang, K. S., Liew, P. X., Har, W. M., & Low, C. W. (2023). Supporting Financial Inclusion with Digital Financial Services: Comparative Analysis of Machine Learning Models. 5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023. https://doi.org/10.1109/IICAIET59451.2023.10291296
Maehara, R., Benites, L., Talavera, A., Aybar-Flores, A., & Muñoz, M. (2024). Predicting Financial Inclusion in Peru: Application of Machine Learning Algorithms. Journal of Risk and Financial Management, 17(1). https://doi.org/10.3390/jrfm17010034
Maherali, A. (2017). Financial Inclusion, Digital Payments and Their Impact on Income and Tax Revenue Around the World [Master’s thesis, Harvard University]. https://dash.harvard.edu/handle/1/33826588
McKinney, W. (2010). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference, 56–61. https://doi.org/10.25080/Majora-92bf1922-00a
Mollaahmetoǧlu, E., & Akcąli, B. Y. (2019). The missing-link between financial development and economic growth: financial innovation. Procedia Computer Science, 158, 696–704. https://doi.org/10.1016/j.procs.2019.09.105
Obidat, A., Almahameed, M., & Alalwan, M. (2022). An empirical examination of factors affecting the post-adoption stage of mobile wallets by consumers: A perspective from a developing country. Decision Science Letters, 11(3). https://doi.org/10.5267/j.dsl.2022.2.005
Patil, P. P., Dwivedi, Y. K., & Rana, N. P. (2017). Digital payments adoption: An analysis of literature. In Springer (Ed.), Lecture Notes in Computer Science: Vol. 10595 LNCS (pp. 61–70). https://doi.org/10.1007/978-3-319-68557-1_7
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12(85), 2825–2830. https://www.jmlr.org/papers/v12/pedregosa11a.html
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). Catboost: Unbiased boosting with categorical features. Advances in Neural Information Processing Systems, 2018-December.
Qamruzzaman, M., & Jianguo, W. (2017). Financial innovation and economic growth in Bangladesh. Financial Innovation, 3(1). https://doi.org/10.1186/s40854-017-0070-0
Qamruzzaman, M., & Wei, J. (2018). Financial innovation, stock market development, and economic growth: An application of ARDL model. International Journal of Financial Studies, 6(3). https://doi.org/10.3390/ijfs6030069
Rakipi, A., Shurdi, O., & Imami, J. (2023). Utilization of data mining and machine learning in digital and electronic payments in banks. Corporate and Business Strategy Review, 4(4 Special Issue). https://doi.org/10.22495/cbsrv4i4siart5
Sahay, R., Cihák, M., N’Diaye, P., & Barajas, A. (2015). Rethinking financial deepening: stability and growth in emerging markets. Revista de Economia Institucional, 17(33), 73–108. https://doi.org/10.18601/01245996.v17n33.04
Setor, T. K., Senyo, P. K., & Addo, A. (2021). Do digital payment transactions reduce corruption? Evidence from developing countries. Telematics and Informatics, 60(101577). https://doi.org/10.1016/j.tele.2021.101577
Allen, J., Carbo-Valverde, S., Chakravorti, S., Rodriguez-Fernandez, F., & Pinar Ardic, O. (2022). Assessing incentives to increase digital payment acceptance and usage: A machine learning approach. PLoS ONE, 17(11 November). https://doi.org/10.1371/journal.pone.0276203
Al-Okaily, M., Lutfi, A., Alsaad, A., Taamneh, A., & Alsyouf, A. (2020). The Determinants of Digital Payment Systems’ Acceptance under Cultural Orientation Differences: The Case of Uncertainty Avoidance. Technology in Society, 63(101367). https://doi.org/10.1016/j.techsoc.2020.101367
Aurazo, J., & Vega, M. (2021). Why people use digital payments: Evidence from micro data in Peru. Latin American Journal of Central Banking, 2(4). https://doi.org/10.1016/j.latcb.2021.100044
Bara, A., & Mudzingiri, C. (2016). Financial innovation and economic growth: Evidence from Zimbabwe. Investment Management and Financial Innovations, 13(2), 65–75. https://doi.org/10.21511/imfi.13(2).2016.07
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17-August-2016. https://doi.org/10.1145/2939672.2939785
Chou, Y. K. (2007). Modeling financial innovation and economic growth: Why the financial sector matters to the real economy. Journal of Economic Education, 38(1), 78–90. https://doi.org/10.3200/JECE.38.1.78-91
Demirgüç-Kunt, A., Klapper, L., Singer, D., & Ansar, S. (2022). The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. World Bank. https://doi.org/10.1596/978-1-4648-1897-4
Development Research Group, Finance Unit and Private Sector Development. (2022). Global Financial Inclusion (Global Findex) Database 2021. World Bank, Development Data Group. https://doi.org/10.48529/JQ97-AJ70
Gardner, J. R., Kusner, M. J., Xu, Z. E., Weinberger, K. Q., & Cunningham, J. P. (2014). Bayesian optimization with inequality constraints. ICML, 2014, 937–945.
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., … Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585(7825), 357–362. https://doi.org/10.1038/s41586-020-2649-2
Hjelholt, M., & Damsgaard, J. (2012). The genesis and evolution of digital payment platforms - A framework for understanding payment transition routes. ECIS 2012.
Ismail, Q. F., Al-Sobh, E. S., Al-Omari, S. S., Bani Yaseen, T. M., & Abdullah, M. A. (2021). Using Machine Learning Algorithms to Predict the State of Financial Inclusion in Africa. 12th International Conference on Information and Communication Systems, ICICS 2021. https://doi.org/10.1109/ICICS52457.2021.9464590
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T. Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 2017-December.
Klapper, L., & Singer, D. (2017). The opportunities and challenges of digitizing government-to- person payments. World Bank Research Observer, 32(2), 211–226. https://doi.org/10.1093/wbro/lkx003
Lee, H. S., Kuang, K. S., Liew, P. X., Har, W. M., & Low, C. W. (2023). Supporting Financial Inclusion with Digital Financial Services: Comparative Analysis of Machine Learning Models. 5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023. https://doi.org/10.1109/IICAIET59451.2023.10291296
Maehara, R., Benites, L., Talavera, A., Aybar-Flores, A., & Muñoz, M. (2024). Predicting Financial Inclusion in Peru: Application of Machine Learning Algorithms. Journal of Risk and Financial Management, 17(1). https://doi.org/10.3390/jrfm17010034
Maherali, A. (2017). Financial Inclusion, Digital Payments and Their Impact on Income and Tax Revenue Around the World [Master’s thesis, Harvard University]. https://dash.harvard.edu/handle/1/33826588
McKinney, W. (2010). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference, 56–61. https://doi.org/10.25080/Majora-92bf1922-00a
Mollaahmetoǧlu, E., & Akcąli, B. Y. (2019). The missing-link between financial development and economic growth: financial innovation. Procedia Computer Science, 158, 696–704. https://doi.org/10.1016/j.procs.2019.09.105
Obidat, A., Almahameed, M., & Alalwan, M. (2022). An empirical examination of factors affecting the post-adoption stage of mobile wallets by consumers: A perspective from a developing country. Decision Science Letters, 11(3). https://doi.org/10.5267/j.dsl.2022.2.005
Patil, P. P., Dwivedi, Y. K., & Rana, N. P. (2017). Digital payments adoption: An analysis of literature. In Springer (Ed.), Lecture Notes in Computer Science: Vol. 10595 LNCS (pp. 61–70). https://doi.org/10.1007/978-3-319-68557-1_7
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12(85), 2825–2830. https://www.jmlr.org/papers/v12/pedregosa11a.html
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). Catboost: Unbiased boosting with categorical features. Advances in Neural Information Processing Systems, 2018-December.
Qamruzzaman, M., & Jianguo, W. (2017). Financial innovation and economic growth in Bangladesh. Financial Innovation, 3(1). https://doi.org/10.1186/s40854-017-0070-0
Qamruzzaman, M., & Wei, J. (2018). Financial innovation, stock market development, and economic growth: An application of ARDL model. International Journal of Financial Studies, 6(3). https://doi.org/10.3390/ijfs6030069
Rakipi, A., Shurdi, O., & Imami, J. (2023). Utilization of data mining and machine learning in digital and electronic payments in banks. Corporate and Business Strategy Review, 4(4 Special Issue). https://doi.org/10.22495/cbsrv4i4siart5
Sahay, R., Cihák, M., N’Diaye, P., & Barajas, A. (2015). Rethinking financial deepening: stability and growth in emerging markets. Revista de Economia Institucional, 17(33), 73–108. https://doi.org/10.18601/01245996.v17n33.04
Setor, T. K., Senyo, P. K., & Addo, A. (2021). Do digital payment transactions reduce corruption? Evidence from developing countries. Telematics and Informatics, 60(101577). https://doi.org/10.1016/j.tele.2021.101577