The problem of credit assessment and approval is generally considered a data-driven issue. While the evaluation of individual credit applications is well-suited to a data-oriented approach, the assessment of applications submitted by corporations involves a more complex process. This complexity stems from the influence of factors such as prevailing economic conditions and sector-specific issues. The effectiveness of a purely data-driven approach in corporate credit assessment is limited, as many influential factors cannot be adequately represented by existing data. In this study, a decision support system is proposed that combines a data-driven approach with expert opinions. A decision model involving three criteria and 10 sub criteria is constructed. A machine learning model is also developed to provide an assessment based on previous data. As a result, the proposed assessment approach enables both objective and subjective evaluations. The result of the case study shows that the proposed approach provides an applicable, flexible and adaptable method for credit assessment.