Acceso usuarios
Dejan Mirč, Marina Ignjatović, Dragan Milic, Vladimir Crnojević, Nedeljko Tica
Our study aimed to determine factors influencing timely loan repayment of smallholder farmers. We used data from 1735 liquidated loans, collecting a set of 36 feasible determinant variables. The study was two-folded. In the first step, with a 64% accuracy, a Logit model revealed 18 significant predictors of timely repayment. Previously credited clients, special agricultural account, average monthly inflow, loan amount, age when applying for a loan, clean credit history, and no credit in the past have a positive influence, while number of transactions, profiling, owned farm area, past due records over five days, tax debt status, and livestock had a negative influence on timely repayment.
We used machine learning algorithms in the second step to enhance model prediction performance. XGBoost model that envisioned the timely repayment with 92% accuracy. As significant predictors, Shapley’s additive explanations identified clean credit history, average monthly inflow, time of owning the account, age when applying for a loan, and horticulture. The study’s findings provide insight into the critical factors in substantially achieving a high repayment rate on borrowed funds.
