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.