As China’s economy transitions from high-speed growth to high-quality development，the traditional credit business of commercial banks，which heavily relies on real estate mortgages to create credit，is becoming unsustainable. There is an urgent need to adopt new scientific methods to assist banks in establishing credit assessment and pricing capabilities，with a focus on the primary source of repayment. Thanks to the comprehensive implementation of the National Golden Tax Project，obtaining compliant enterprise tax-related data，particularly real transaction data，has become increasingly convenient. This opens the door to using causal relationship data instead of merely correlational relationship data to characterize the operational conditions and credit risks of enterprises. By modeling with real transaction data，we can address the issue of information asymmetry among banks and external stakeholders such as enterprises，tax authorities，regulatory agencies，as well as within various internal bank processes. This approach helps in preventing potential moral hazards and effectively aids banks in identifying and assessing various credit risks，including issues such as “inflated revenue”，“off-balance-sheet operations”，“borrowing under others’ names”，“insufficient debt-servicing capacity”，and more. It also allows banks to implement comprehensive，dynamic，and intelligent risk control measures，thus enabling them to maintain stable operations in the credit business and guard against systemic risks. Therefore，it is imperative for commercial banks to establish a recognized，verifiable，traceable，real，objective，and complete foundation of enterprise transaction data to empower credit asset management.