This paper uses loan application-level data from a peer-to-peer lending platform to study the risk-taking channel of monetary policy. By employing a direct ex-ante measure of risk-taking and estimating the simultaneous equations of loan approval and loan amount, we provide evidence of monetary policy's impact on a nonbank financial institution's risk-taking. We find that the search-for-yield is the main driving force of the risk-taking effect, while we do not observe consistent findings of risk-shifting from the liquidity change. Monetary policy easing is associated with a higher probability of granting loans to risky borrowers and greater riskiness of credit allocation. However, these changes do not necessarily relate to a larger loan amount on average.

This study resolves a puzzle in the banking literature: why do an increasing number of countries adopt a deposit insurance scheme (DIS) while prior studies have shown that it increases the likelihood of banking crises? Using a dataset of 64 countries over the period 1970–2009, our study shows that the adoption of a DIS is associated with a 2.0–4.7 percentage points higher likelihood of banking crises (the “direct effect”), while it is associated with a 10.1–11.1 percentage points lower likelihood of non-banking financial crises (the “spillover effect”). Since the “spillover effect” is larger than the “direct effect”, a DIS actually increases overall financial stability. Additionally, we analyze the mechanisms through which a DIS affects financial crises. First, we highlight the existence of the implicit guarantee and examine its interaction with an explicit DIS. Second, we investigate the substitution effect between banking crises and non-banking crises.

This paper compares the predictive power of credit scoring models based on machine learning techniques with that of traditional loss and default models. Using proprietary transaction-level data from a leading fintech company in China, we test the performance of different models to predict losses and defaults both in normal times and when the economy is subject to a shock. In particular, we analyse the case of an (exogenous) change in regulation policy on shadow banking in China that caused credit conditions to deteriorate. We find that the model based on machine learning and non-traditional data is better able to predict losses and defaults than traditional models in the presence of a negative shock to the aggregate credit supply. This result reflects a higher capacity of non-traditional data to capture relevant borrower characteristics and of machine learning techniques to better mine the non-linear relationship between variables in a period of stress.

Using a unique dataset of more than 2 million Chinese firms that received credit from both an important big tech firm (Ant Group) and traditional commercial banks, this paper investigates how different forms of credit correlate with local economic activity, house prices, and firm characteristics. We find that big tech credit does not correlate with local business conditions and house prices when controlling for demand factors, but reacts strongly to changes in firm characteristics, such as transaction volumes and network scores used to calculate firm credit ratings. By contrast, both secured and unsecured bank credit react significantly to local house prices, which incorporate useful information on the environment in which clients operate and on their creditworthiness. This evidence implies that the wider use of big tech credit could reduce the importance of the collateral channel but, at the same time, make lending more reactive to changes in firms’ business activity.