Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Financial Distress Prediction in Kenyan Saccos: A Comparative Analysis of Machine Learning Models

Domaine:

socioeconomic

Type de record:

papersoftware
Créateur:
Dr.Pro
Éditeur:
Journal of Economics, Finance And Management Studies
Hôte:avatar

Financial distress prediction is crucial for assessing the economic health of any organization or nation; as a whole. This research focuses on developing a robust machine learning model to predict financial distress in Kenyan Savings and Credit Cooperative Organizations (SACCOs). Notably, this is one of the first studies to explore financial distress prediction specifically within the context of Kenyan SACCOs. The specific objectives included the assessment of the performance of machine learning predictive models using both financial and non-financial variables, the identification of predictors that significantly affect financial distress of Kenyan SACCOs, and determining which features are the most predictive. The study evaluates the effectiveness of various machine learning algorithms, including Decision Tree, K-Nearest Neighbors, Logistic Regression, Artificial Neural Networks, Naive Bayes Classifier, Random Forest, and Support Vector Machine, through nine performance evaluation metrics, with the ROC-AUC score as the primary measure. Results indicate that while K-Nearest Neighbors achieved an ROC-AUC score of 0.78, an ensemble model using Stochastic Gradient Boosting reached 0.81.

Visit

doi.org

Tags

Financial distress, Artificial Intelligence, machine learning, predictive modelling, ensemble models, SACCOs, Kenya

Licenses

info:eu-repo/semantics/openAccessCreative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

Similaires

Prediction of SACCOS Failure in Tanzania using Machine Learning ModelsA Comparative Analysis of Machine Learning Models for Prediction of Insurance Uptake in KenyaA Comparative Analysis of Machine Learning Models for the Prediction of Insurance Uptake in KenyaMachine Learning Models in Climate Prediction and Adaptation Planning in Mozambique: A Comparative AnalysisComparative analysis of stochastic models and machine learning algorithms for inflation rate prediction in NigeriaMachine Learning Approach for Crime Prediction: A Comparative Analysis

Prediction of SACCOS Failure in Tanzania using Machine Learning Models

Savings and Credit Co-Operative Societies (SACCOS) are seen as viable opportunities to promote finan

A Comparative Analysis of Machine Learning Models for Prediction of Insurance Uptake in Kenya

The role of insurance in financial inclusion as well as in economic growth is immense. However, low

A Comparative Analysis of Machine Learning Models for the Prediction of Insurance Uptake in Kenya

The role of insurance in financial inclusion and economic growth, in general, is immense and is incr

Machine Learning Models in Climate Prediction and Adaptation Planning in Mozambique: A Comparative Analysis

Mozambique is a country heavily impacted by climate variability, necessitating robust predi

Comparative analysis of stochastic models and machine learning algorithms for inflation rate prediction in Nigeria

Inflation forecasting is critical to economic planning, particularly in developing economies like Ni

Machine Learning Approach for Crime Prediction: A Comparative Analysis

Abstract Crime is a global issue that affects countries at all stages of developme