Credit risk database for SME financial inclusion - Lan H. Nguyen; Megumi Sagara (Asian Development Bank Institute (ADBI), 2020)
We introduce the Credit Risk Database (CRD) and its contribution to financial inclusion efforts in Japan. By collecting financial data about small and medium-sized enterprises (SMEs), the CRD contributes to the overall understanding of the SME sector, to the adaptation of risk-based lending and to a fairer loan guarantee system. In addition to financial data, the CRD also includes alternative data, bank account transaction data, when assessing SME credit. A machine learning model is adopted to process the extremely large body of transaction data. The best performing predictors of default include cash balance and cash outflow related to repayments. The machine learning model outperforms the logistic model and is highly accurate in predicting the probability of short-term default. The alternative data and model can serve as both an enabling short-term monitoring instrument and a credit assessment tool for SMEs without financial statements.
Keywords: G21; G28; G32; 330; SME credit assessment; machine learning; transaction data