"金融科技：预测违约的关键因素与模型构建"。

In the field of electronic finance, predicting defaults is a crucial aspect in assessing credit risk. The expected loss for a lender is determined by factors such as the probability of default (PD), exposure at default (ED), and loss given default (LGD). The formula to calculate expected loss is PD x ED x LGD. Therefore, it is essential to accurately predict default probability when extending credit. To achieve this goal, statistical models are built to predict default. The process involves developing a classification model, using the FICO score as a baseline estimate, analyzing other variables, and exploring ways to improve upon existing credit models. By utilizing these methods, lenders can better assess credit risk and make informed decisions when extending credit to individuals or businesses. The importance of predicting defaults is highlighted in the "FINTECH PREDICTING DEFAULTS" primer. This primer provides valuable information on credit risk, emphasizing the significance of default probability in the lending industry. By focusing on building statistical models to predict default, lenders can minimize potential losses and make more informed decisions when extending credit. In conclusion, predicting defaults is a critical aspect of electronic finance. By utilizing statistical models and analyzing various factors, lenders can improve their ability to assess credit risk and make informed decisions when extending credit. The "FINTECH PREDICTING DEFAULTS" primer serves as a valuable resource in understanding the significance of default probability and enhancing credit risk assessment in the electronic finance industry.