Assessment of SME Credit Risk using Advanced Machine Learning and Big Data Methods
PI: Prof. Chen Lin, HKU
Co-Is: Dr. Luo Ye, HKU & Dr. Mingzhu Tai, HKU
Nowadays policymakers are promoting lending to small and medium enterprises (SMEs) given their important roles in creating jobs and driving economic growth. However, the limited of information availability to creditors has largely constrained SME credit access. By incorporating high-frequency, high-dimensional, and high-volume big data and by utilizing the cutting-edge dynamic machine learning models into the business credit risk assessment, the information asymmetry problem can be substantially alleviated.
The improvement in SME credit risk assessment is valuable to financial institutions, to SMEs, as well as to policy makers. For the financial institutions that originate SME loans, being able to better assess the credit risk can help them lower the risk exposure and reduce potential losses. For SMEs, a better credit risk management by lenders can increase credit supply and alleviate their credit constraints. For policy makers, the improvement in credit risk assessment can help them better monitor risk in the financial system.