Towards Small-Scale Farmers Fair Credit Scoring Technique

Abstract

In consumer loans, where lenders deal with masses, use of algorithms to classify borrowers is fast catching up. Classification based on predictive models tend, to adversely affect borrowers. In this paper, we study the extent to which various algorithms disenfranchise borrowers lying on the boundaries of decision making. In the study, the data used for loan appraisal, and decisions made by the lenders are subjected to a set of select algorithms. The bias suffered by borrowers in each case is determined using mean absolute error (MAE) and relative absolute error (RAE). The results show that FURIA has the least bias with the MAE of 0.2662 and 0.1501 and RAE of 64.19% and 30.31% for the German and Australian data sets respectively. Consequently, FURIA is modified to remove the hard boundaries which results in even lower MAE of 0.2535 and 0.1264 and RAE of 64.14% and 27.73% for the German and Australian data sets respectively.