2024/10/17 by Cedric H. A. Koffi, Koffi, Cedric H. A., Viani Biatat Djeundje +3
Economics, Econometrics and Finance · #FOS: Economics and business #Microfinance and Financial Inclusion #Risk Management (q-fin.RM)
paper · pdf · doi:10.48550/arxiv.2410.13100
openalex publication_date 2024/10/17 · openalex created_date 2024/10/20 · openalex updated_date 2026/07/28
We develop and evaluate a family of discrete-time logit-link (LLink) models (including fixed-effects and frailty extensions) to capture latent heterogeneity in repayment behaviour and quantify the effects of socio-temporal factors in microfinance. Our findings highlight the importance of unobserved borrower risk, revealing that simple random intercept structures are sufficient to model latent heterogeneity in this context. Additionally, socio-temporal variables--such as festive seasons and long school breaks--consistently associate with delinquency transitions, offering key insights into repayment dynamics. While LLink models provide clear interpretability, tree-based methods outperform them in predictive accuracy, making them suitable for multistate classification tasks. Building on this, we propose an optimised classification strategy based on the Matthews Correlation Coefficient to enhance next-state prediction. Overall, our results highlight the benefit of combining interpretable risk modeling with advanced machine learning to support robust, data-driven decision-making in microfinance operations.