Fintech model performance analysis in credit loans: evidence from China city commercial banks

The analysis of bank loan performance has consistently been a central issue in banking research. With the impact of the COVID-19 pandemic, we have witnessed a significant increase in online loans. Our study employs fintech models to develop a big data credit analysis system, evaluating model efficiency and the effect of 'data organization' on predictive performance for city commercial bank loans. Our results show that training data size, timeliness, and inter-bank 'data sharing' significantly affect model predictions. We also find that traditional econometric models (Logit and Probit) have the weakest predictive ability. However, 'most advanced' deep learning models perform worse than machine learning models with lower algorithmic complexity, such as Random Forest. Besides, 'sharing training data' generally increases model efficiency, but there are exceptions for larger city commercial banks. This paper provides ample empirical evidence for the application of the fintech model in bank loan credit. It highlights several characteristics of training data, such as size and timeliness, which greatly affect model efficiencies. We also provide policy recommendations for fintech transition and collaboration among municipal banks.