Evaluating the Impact of Telecom Payment Behavior and Equb Participation on Credit Approval Decisions
| dc.contributor.advisor | Rosa Tsegay | |
| dc.contributor.author | Selamawit Getasew | |
| dc.date.accessioned | 2026-08-04T21:22:13Z | |
| dc.date.available | 2026-08-04T21:22:13Z | |
| dc.date.issued | 2026-06 | |
| dc.description.abstract | Availability of credit helps in the development of small-scale businesses, and in most cases, traditional credit scoring is not effective for people without adequate financial history. This study aims to determine whether alternative data helps in improving the accuracy of microfinance institutions in predicting the success of loans. This study aims to determine the effect of using alternative data in improving the accuracy of microfinance institutions in predicting the success of loans. This is done by comparing the performance of models using traditional data and models using traditional and alternative data. The data preprocessing steps involve handling missing values, limiting the number of outliers, scaling, and encoding. Stratified sampling and 5-fold cross-validation are used to perform the study. The performance is measured by accuracy, precision, recall, and F1 score. The results obtained from the study show marginal improvements in the accuracy of microfinance institutions in predicting the success of loans. LightGBM secures the highest result, with an average F1 score of 0.857 that is realized when conventional features are employed. Other models’ (for instance, Random Forest and XGBoost) effectiveness is at the par with LightGBM. Incorporating alternative data hardly changes the models’ effectiveness. The addition of alternative data to traditional data helps in enhancing the accuracy of microfinance institutions in predicting the success of loans, although the improvements are marginal and context-dependent | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/8804 | |
| dc.language.iso | en | |
| dc.publisher | Addis Ababa University | |
| dc.subject | Artificial intelligence | |
| dc.subject | alternative data | |
| dc.subject | inclusive lending | |
| dc.subject | credit evaluation | |
| dc.subject | small business finance | |
| dc.subject | predictive modelling | |
| dc.subject | machine learning | |
| dc.subject | economic empowerment | |
| dc.subject | equitable finance | |
| dc.title | Evaluating the Impact of Telecom Payment Behavior and Equb Participation on Credit Approval Decisions | |
| dc.type | Thesis |