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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Lengua, Miguel Angel Cano
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Computer Science & IT Electrical & Electronics Engineering

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Design of a machine learning model for predicting credit risk in microfinance using environmental data Alvarez, Eladio Alfredo Soto; Lengua, Miguel Angel Cano
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.9848

Abstract

Agricultural microfinance is a sector that is significantly impacted by climate-related risks, such as temperature fluctuation, soil degradation, and irregular rainfall. These environmental factors have not only impact on crop yield but also results in influencing borrowers’ ability to repay agricultural loans. Traditional credit scoring models lack in predicting due to the complex interplay between environmental and borrower-specific variables. This research study proposes a new predictive machine learning model based on XGBoost for assessing the credit risks in agricultural microfinance. This model utilizes environmental indicators, borrower characteristics, and loan attributes for computing the continuous credit risk score. The model was trained utilizing a real-world dataset of 142,017 loan applications with a 70/30 split. When compared with other traditional models, the results of the model showcases an accuracy of 99%, a recall of 84%, a precision of 89%, and an F1-score of 86%, outperforming traditional algorithms such as logistic regression and decision tree. This model has substantial implications for microfinance organizations. With this model, borrowers can evaluate risk accurately during the loan application stage by utilizing environmental data, resulting in better loan targeting, enhanced financial inclusion, and better risk mitigation for vulnerable farming communities in climate-sensitive regions.
Systematic review of a business model using blockchain technology for the use of digital money in mass centers Medina, Julio César Rojas; Lengua, Miguel Ángel Cano
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp342-356

Abstract

In recent years, commercial transactions have experienced a radical change in the way goods and services are purchased. Payments with electronic and digital money are increasing dramatically compared to payments with physical money. Likewise, money using blockchain technology is marking disruptive milestones in transactions, especially in cross-border payments, showing many benefits, such as speed, lower costs, and security. The COVID-19 pandemic has shown the entire world the potential and possible development horizon of digital money, especially cryptoassets, in commercial transactions, as well as the risks associated with this technology. This has exposed the problem and need for a commercial model with blockchain technology for use in mass centers, which allows for the widespread and democratization of blockchain technology in mass commercial transactions. The methodology used is PRISMA. The objective of this article is to conduct a systematic review of the literature on digital money with blockchain technology for use in mass marketing centers. Finally, the results are presented, where the commercial model based on blockchain must consider security criteria, technology, legal aspects, and sociocultural barriers. Incorporate the interaction between electronic money, central bank digital currencies (CBDCs), and cryptoassets, as well as a decentralized technological platform for direct digital commerce. This implies that the model must consider these criteria in its design, implementation process, and the platform it supports.
Co-Authors Alvarez, Eladio Alfredo Soto Medina, Julio César Rojas