I. Introduction
Loan default prediction [1], [2] plays an important role in the financial system, which predicts loan defaults for financial institutions and the banking industry. In the current financial system, human approvers are overwhelmed by the massive loan applications, lengthening the average waiting time [1]. To accelerate the reviewing procedure, machine learning techniques [3] – [6] are increasingly adopted to share the workload, which predict loan defaults from the profile of the lender such as occupation, income, and credit records. Explainable machine learning models such as GBDT [4] and Logistic Regression [3] are the standard choices for loan default prediction due to the requirement of trustworthiness in practice as the increase of relevant regulations on financial algorithms launched by different countries [7].