Fintech and Commercial Banking

Literature Review

The risk-taking of financial technology for commercial banks is different from different perspectives. The following text will discuss how financial technology is responsible for the risk-taking of commercial banks from the perspectives of competition and cooperation.

When we focus on the perspective of competition, the development of fintech will bring challenges to the banking industry. In the asset business, Qiu et al. found that the continuous development of financial technology will lead to the development of interest rates in a market-oriented direction, which will undoubtedly change the liability structure of commercial banks, then the cost of liabilities of commercial banks will rise. To reduce losses caused by rising costs, commercial banks will invest in projects with higher risks and higher returns. Regarding debt, Berger and Houle found that in the continuous development of Internet technology, a large amount of funds does not need to pass through traditional financial institutions such as commercial banks as before and only needs to be transferred through the Internet platform.

Although this efficient method is beneficial to the supply and demand of funds, it will undoubtedly have a certain impact on the important profitable business of traditional banks–the lending business. In the intermediary business, Nie and Liu analyzed that, with the development of financial technology, the convenient three-party payment business continues to squeeze intermediate business, such as the original water, electricity, gas agent payment, and agency insurance that were handled by commercial banks. Moreover, commercial banks also provide loan services to adopt green energy technology to reduce greenhouse gas emissions and climate change.

When we focus on the perspective of cooperation, it will be beneficial to the development of commercial banking itself if fintech is actively integrated with commercial banks. In empirical research, Wu X. F. pointed out that the relationship between financial technology and commercial banks is not an alternative relationship but complementary. Financial technology can promote commercial banks to carry out products and services and other innovations to improve operational efficiency and reduce costs. Li et al. found that commercial banks can learn technical means such as the Internet, big data, and artificial intelligence by integrating with financial technology and using these technical means to obtain customers' risk-taking levels and the changing laws of risk-taking levels, which is conducive to planning asset allocation that meets the specific financial needs of customers.

Onay and Ozsoz found that, after the opening of the new business of online banking, customers' deposit and loan behaviors in commercial banks increased significantly, and the commercial banks' deposit and loan business continued to rise. The increase is reflected that the total assets of commercial banks rising, the return on assets rising, and the rate of nonperforming loans has fallen. Therefore, we know that the integration of commercial banks' business and Internet technology has promoted the development of commercial banks themselves. Guo and Shen found that if commercial banks actively use Internet financial technology, management costs and bank risks can be reduced.

Previous studies have focused on financial risk-warnings using various types of regression techniques. For instance, the mainstream models are the probability regression functions (Probit and Logit) proposed by Frankel and Rose, the cross-sectional regression model (STV) proposed by Sachs et al., the signal analysis method (KLR) proposed by Kaminsky and Zhang, and the artificial neural network (ANN) model established by Mitra and Balaji. Due to the rapid development of computer technology, a support vector machine (SVM) has become a new and effective risk-warning method in the field of artificial intelligence.

The ANN has been used in various disciplines of social science. This method solves the problem that the dimension of the training samples is too high, and the data processing is difficult. Plenty of studies have confirmed that the SVM risk-warning effect is better. For example, Andrawis et al. found that SVM has a better classification function, and its classification accuracy is significantly improved compared to a back propagation (BP) neural network. Currently, there are relatively few studies on the risk-warning of financial technology, mainly focused on the risk early warning model of P2P online lending platforms. P2P refers to peer-to-peer network lending, and it is a type of Internet financial product.