Research on the Construction of Financial Supervision Information System Based on Machine Learning

Abstract

In order to fully implement systematic, continuous and effective supervision of financial institutions and promote the safe, steady, and efficient operation of China’s financial system, this research needs to develop a fully intelligent financial supervision information system, so as to take measures to effectively prevent and resolve financial risks. In this paper, based on ML (machine learning), an LSTM (long short-term memory) model with good comprehensive performance is built. This model is different from the existing scorecard model which relies on statistical learning. It not only further reduces the dependence on financial experts, but also has the ability of rapid iteration. The post-loan risk early warning model based on RF (random forest) algorithm is designed. The model parameters are optimized, which makes the risk early warning model have higher accuracy. The results show that when the data amount is 10,000 to 50,000, the accuracy of the model is relatively high when it is input into the model. When the amount of data is low, the overall throughput of the model is still very high. It shows that the pre-warning model of post-lending risk constructed in this paper has a strong risk prediction ability.

Business model contributions to bank profit performance: A machine learning approach

“…2 Duttagupta and Cashin (2011), as well as Manasse et al (2013), apply them to the study of banking downturns in emerging and developing countries. In a similar line of research, Ioannides et al (2010), Alessi andDetken (2018), andLin et al (2022) assess the early warning signs of banking crises. Emrouznejad and Anouze (2010) combine data envelopment analysis and decision trees to carry out a bank efficiency study, whereas the focus of Bijak and Thomas (2012), Kao et al (2012), andPai et al (2015) is on customer credit scoring in financial intermediation.…”