Credit risk prediction identifies default risk by establishing a nonlinear relationship between feature data and default status. The feature system is the crucial factor affecting the accuracy of credit risk prediction. This paper addresses how to identify customers within a certain range of cost budgets. A case study on farmers in China shows that the characteristics of incomplete loan information, dispersed residences and strong liquidity indicate that bank lending should consider not only the identification of default customers but also the actual cost budget. A modified support vector machine (C+SVM), which considers the cost of obtaining credit information and the budgets of decision makers, is established to optimize the credit evaluation results. This paper performs feature selection and weighting by optimizing the minimum total misjudgment cost as the objective function while constraining the prediction deviation, feature weights and cost budget. The empirical results for Chinese farmers as customers of a large commercial bank demonstrate that the proposed C+SVM model is effective in selecting credit information costs and that decision makers can achieve the necessary prediction accuracy at lower costs. The best features and weights for credit risk prediction can be obtained for different cost budgets.