Tech in Fin before FinTech: The importance of technology in banking during a crisis
The emergence of financial technology (FinTech) has triggered a debate on the effect of information technology on financial stability (FSB 2019, Claessens et al. 2018). The recent literature on FinTech has mostly focused on how the latest technological developments have been changing the way information is processed and the relative consequences for credit allocation and performance (e.g. Bartlett et al. 2019, Berg et al. 2019, Buchak et al. 2018, Di Maggio and Yao 2018, Fuster et al. 2019). However, the era of FinTech has not yet been exposed to large shocks testing its resilience. Therefore, the FinTech literature cannot directly speak to the link between technology and financial stability, as predictive systems which are accurate in good times may fail to predict default during a crisis (Rajan et al. 2015).
In our recent paper (Pierri and Timmer 2020), we shed light on how banks with a varying degree of pre-Global Crisis IT adoption fared when the crisis hit in order to understand the potential impact of technology adoption in lending on financial stability. To evaluate banks’ resilience, we study the evolution of their non-performing loans (NPLs) which are considered an important indicator of banking sector distress. The direction of the impact of IT adoption on banks’ resilience to a crisis is a priori ambiguous. On one hand, IT can improve monitoring and screening by enhancing the collection, storage, communication, and processing of information. On the other hand, banks with more IT might rely too much on ‘hard’ information, which are easier to report and communicate, inducing them to neglect ‘soft’ information and to take on too much risk (Rajan 2006).
Figure 1 illustrates the evolution of the ratio of NPLs to assets from 1996 to 2014 for banks in the bottom and top quartiles of the distribution of IT adoption. The two series are virtually indistinguishable until 2007. However, in 2008 – as NPLs start to surge – the two lines diverge. The growth in NPLs is considerably more pronounced for banks with low IT adoption. For example, in 2010, banks in the top 25% of IT adoption had about half of the NPLs compared to those in the bottom 25% of IT adoption. After 2010 the two series start converging again. The lack of correlation between IT adoption and non-performing loans outside the crisis reinforces the argument that it is important to study the effects of technology adoption in finance when the economy faces a system-wide shock.
Figure 1 NPLs over assets by pre-Global Crisis IT adoption
Note: Figure 1: NPLs over Assets by pre-GFC IT adoption. This Figure plots the median share of NPLs over assets for high and low IT adopters. “High IT adoption” is the median share of NPLs over assets for banks with…