The availability of formal credit is crucial for the development of the agricultural sector as it can enhance farmers’ purchasing power to acquire inputs and agricultural technology. This, in turn, can increase productivity and resilience throughout the sector. Therefore, the analysis of bank client and loan data in the agricultural sector in a developing country is of interest. We explore the question of who the clients of agricultural credit are and whether they can be clustered into different groups by using an unsupervised machine learning technique. We also investigate whether the loan repayment performance of these clusters differs based on various logit regressions. According to our results, there are 3 different clusters of farmers in Mali that differ by personal characteristics (such as age or gender) as well as credit demand characteristics (e.g., loan amount, interest rates, credit duration, number of credits). Each cluster that differs in their characteristics demonstrates a dissimilar repayment performance. Hence, different instruments as well as communication designs are needed to meet the financial needs of the different clusters and to strengthen the resilience of different groups of farmers in Mali. Our findings provide an important foundation for the design of future agricultural policies and financial products for the agricultural sector as they emphasise the heterogeneity of agricultural lenders in general.