Market structure, screening activity and bank lending behaviour

In this paper we construct a theoretical model of spatial banking competition that considers the differential information among banks and potential borrowers in order to investigate how market structure affects the lending behavior of banks and their incentives to invest in screening technology. Consistent with the prevailing view in the relevant literature, our results reveal that competition reduces lending cost, which, in turn, encourages the entry of new customers in the loan market. Also, that the transportation cost that potential borrowers have to pay in order to reach the bank of their interest is decreased with the degree of competitiveness. Importantly, we demonstrate that market structure exerts a considerable positive effect on banks’ incentives to screen their loan applicants since banks are found to invest more in screening as competition in the market becomes higher. This is to say, banks resort to screening that serves as a buffer mechanism against bad credit which entails higher risk and which is more likely under competitive conditions. Overall, our findings provide support to a rather close link between the degree of competition, bank lending activity, and the investment of banks in screening technology

What lies behind the "Too-Small-To-Survive" banks

It is a common place that during financial crises, like the one started in 2007, authorities provide substantial financial support to some problem banks, whilst at the same time let several others to go bankrupt. Is this happening because some particular banks are considered important and big enough to save, whereas some others are perceived as being ‘Too-Small-To-Survive’? Is the size of banks the fundamental factor that makes authorities to treat them differently, or it is also that some banks perform poorly and are not capable of withstanding some considerable shocks whatsoever? Our study provides concrete answers to these questions thus filling part of the void in the existing literature. A short- and a long-run positive relationship between size and performance is documented regardless of the level of bank soundness (healthy vs. failed and assisted banks) under scrutiny. Importantly, we pose and lend support to the ‘Too-Small-To-Survive’ hypothesis according to which the impact of bank performance on failure probability strongly depends on size. Evidence shows that authorities tend not to save banks whose size is below some specific threshold

Determinants of bank efficiency: Evidence from a semi-parametric methodology

In this paper, we use a semi-parametric two-stage model to examine the effect of bank-specific, industry-specific and macroeconomic determinants of bank efficiency. This method, proposed by Simar and Wilson (2007), relaxes several deficiencies of previous two-stage analyses, which regress non-parametric estimates of bank efficiency on exogenous determinants. In particular, we propose a bootstrap procedure to be used in the second stage and we compare the results obtained to the equivalents of a Tobit model. We suggest that the Tobit regressions inaccurately provide insignificant estimates for the effect of bank size, industry concentration and economic investment on bank efficiency, a fact that illustrates the power of the new method. Since the effect of these determinants has been ambiguous in previous literature, this may be a desideratum for future research.Bank efficiency; semi-parametric models

Does the CAMELS bank ratings system follow a procyclical pattern?

The financial crisis which erupted in 2007-8 has illustrated the disruptive effects of procyclicality. The phenomenon of procyclicality refers to the mutually reinforcing interactions between the financial system and the real economy that tend to amplify business cycle fluctuations. In this study, we empirically investigate the sensitivity of the CAMELS ratings system, which is used by the U.S. authorities to monitor the conditions in the banking market, to the fluctuations of the economic cycle. Our results suggest that the overall state of the U.S. economy and bank regulatory ratings are positively linked to each other: CAMELS increase during economic upturns and decrease during downturns. This is to say that the performance and risk-taking behaviour of banks is rated higher when the conditions in the economy are favourable and lower when the economic environment is weak. Along these lines, we document a positive relationship between CAMELS and the conditions in financial markets. This very important and rather unknown source of procyclicality should be taken into serious consideration by authorities

Exploring the Nexus between Banking Sector Reform and Performance: Evidence from Newly Acceded EU Countries

The aim of this study is to examine the relationship between banking sector reform and bank performance – measured in terms of efficiency, total factor productivity growth and net interest margin – accounting for the effects through competition and bank risk-taking. To this end, we develop an empirical model of bank performance and draw on recent econometric advances to consistently estimate it. The model is applied to bank panel data from ten newly acceded EU countries. The results indicate that both banking sector reform and competition exert a positive impact on bank efficiency, while the effect of reform on total factor productivity growth is significant only toward the end of the reform process. Finally, the effect of capital and credit risk on bank performance is in most cases negative, while it seems that higher liquid assets reduce the efficiency and productivity of banks.Bank performance; Banking sector reform; Competition; Risk-taking

Determinants of bank efficiency: evidence from a semi-parametric mathodology

Purpose 13 This paper aims to analyze bank efficiency into a number of bank-specific, industryspecific and macroeconomic determinants. Design/methodology/approach 13 The authors follow a semi-parametric two-stage methodology, where productive efficiency is derived via a non-parametric technique in the first stage and then the scores obtained are linked to a series of determinants of bank efficiency, using a double bootstrapping procedure. Findings 13 Overall, it is found that the banking sectors of almost all the sample countries show a gradual improvement in their efficiency levels. The model used shows that a number of determinants like bank size, industry concentration and the investment environment have a positive impact on bank efficiency, which is not the case when standard Tobit models are employed. Research limitations/implications 13 The findings have important implications for the relevance of well-known hypotheses that refer to the performance of the banking sectors, like the structure conduct-performance and the efficient structure hypotheses. These implications are not necessarily verified when past conventional econometric methodologies are used. Practical implications 13 The paper offers new insights to policy makers, bank managers and practitioners on the relevance of a number of driving factors of bank efficiency that might help them to improve the performance of the banking system and enhance the quality of services provided. Originality/value 13 This is the first paper in the bank efficiency literature that employs a semiparametric two-stage model, which relaxes several deficiencies of previous two-stage empirical approaches thus, offering a solution to the many problematic features of standard censored regression

Determinants of bank efficiency: Evidence from a semi-parametric methodology

In this paper, we use a semi-parametric two-stage model to examine the effect of bank-specific, industry-specific and macroeconomic determinants of bank efficiency. This method, proposed by Simar and Wilson (2007), relaxes several deficiencies of previous two-stage analyses, which regress non-parametric estimates of bank efficiency on exogenous determinants. In particular, we propose a bootstrap procedure to be used in the second stage and we compare the results obtained to the equivalents of a Tobit model. We suggest that the Tobit regressions inaccurately provide insignificant estimates for the effect of bank size, industry concentration and economic investment on bank efficiency, a fact that illustrates the power of the new method. Since the effect of these determinants has been ambiguous in previous literature, this may be a desideratum for future research

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI