Abstract This study applies machine-learning techniques and rulebased methods to on struct nonlinear nonparametric models to forecast retail consumer and medium-sized enterprises
(SMEs) credit risk. By combining customer transactions and
enterprise data from 2018 to 2020 sampled from a major
business district in the People’s Republic of China, forecasts
were constructed that significantly improved the classification rates of customer and enterprise delinquencies and
defaults. Moreover, the time-series patterns of the estimated
delinquency rates and credit scores over multiple dimensions
produced by this model suggest that aggregated credit risk
analytics may have important applications in forecasting
systemic risk, which might shed some light on obtaining
prospective insights regarding consumer credit that can be
gleaned from historical data especially pandemic period.
Keywords: Credit risk model, Machine Learning, Rule-based,Credit Score