Banks often assume that higher credit limits increase customer default risk because greater exposure appears to imply greater vulnerability. This reasoning, however, conflates correlation with causation. Whether increasing a customer's credit limit truly raises the likelihood of default remains...

This paper investigates credit risk management as a dynamic system. Panel Vector Autoregression (PVAR) is employed to model interrelationships among four key components: Non-Performing Loans (NPLs), Loan Loss Provision (LLP), loan charge-off (LCO) and capital. The Cost-to-Income ratio (CIR) and...

We develop a general equilibrium framework in which a commercial banker, constrained by capital adequacy requirements, creates a special purpose vehicle (SPV) to hold securitized assets off its balance sheet. By operating the bank and SPV as separate entities, the banker circumvents regulation,...

This paper investigates the macroeconomic and bank-specific determinants of nonperforming loans in Romania and selected Central and Eastern European countries. Using a combination of econometric approaches, the study employs fixed-effects panel regressions for 18 Romanian banks over the period...

To mitigate credit risk, banks are required to set aside a specific amount as a safety net to absorb the expected loss on a banks' loan portfolio called loan loss provisions (LLPs) or provisions for bad debts. All banks worldwide had to adopt International Financial Reporting Standard 9 (IFRS 9)...

This study aims to analyze the impact of environmental, social, and governance (ESG) disclosure quality on banking risk. Data were collected from the 100 largest commercial banks in the Middle East and Africa over ten years and examined using econometric analysis to measure the influence of ESG...

This study investigates the probability of consumer default across both secured and unsecured assets, with a particular focus on borrower behavior and the role of moral hazard in shaping individual credit risk. It examines how different borrower decisions, such as investing in secured and...

The circular economy (CE) is increasingly recognized as a financially material dimension of corporate sustainability, complementing and in some respects surpassing traditional Environmental, Social, and Governance (ESG) metrics. Building on prior evidence of a negative relationship between...

Machine learning-based credit risk models are commonly assessed using discrimination metrics alone. Such evaluation, however, does not fully capture economic consequences, temporal deployment conditions, or changes in the underlying risk environment. This study develops a decision-aligned,...

Internal ratings-based models play a central role in bank risk management and regulatory capital determination, yet their validation remains methodologically challenging and operationally resourceintensive. In this paper, we contribute to the quantitative validation of probability of default...

This paper explores how the low-carbon transition affects firms' credit ratings and market-implied distance-to-default. We develop a novel dataset covering firms' greenhouse gas emissions alongside climate disclosure and forward-looking emission reduction targets. Panel regression analysis...

This study addresses the quantification of credit risk in solidarity economy entities, proposing a new methodology to redefine the concept of a "default" in the frequent situations of extreme class imbalances. The objective is to develop and evaluate credit scoring models that enhance risk...

How do banks respond to geopolitical risk, and is this response distinct from other macroeconomic risks? Using U.S. supervisory data and new geopolitical risk indices, we show that banks reduce cross-border lending to countries with elevated geopolitical risk but continue lending to those...

How do banks respond to geopolitical risk, and is this response distinct from other macroeconomic risks? Using U.S. supervisory data and new geopolitical risk indices, we show that banks reduce cross-border lending to countries with elevated geopolitical risk but continue lending to those...

The paper analyses the association between the reporting of the Sustainable Development Goals (SDGs) and the behaviour of Romanian banks, focusing on the relationship between SDG disclosure scores (both total and across the four sustainability dimensions) and credit risk management practices,...

This study aims to combine deep and recurrent neural networks with a reduced-form portfolio model to predict future default rates across economic sectors. The industry-specific forecasts for Italian default rates produced with the proposed approach demonstrate its effectiveness, achieving...

How do banks respond to geopolitical risk, and is this response distinct from other macroeconomic risks? Using U.S. supervisory data and new geopolitical risk indices, we show that banks reduce cross-border lending to countries with elevated geopolitical risk but continue lending to those...

Climate change is expected to lead to more frequent and intense extreme weather events, such as floods and droughts, which in turn increase physical risks. In this paper, we assess the direct exposure of Slovak banks' corporate loan portfolios to riverine flood risk. We propose several...

This study provides a comparative analysis of European banking systems, focusing on how macroeconomic factors influence non-performing loans (NPLs) to evaluate bank credit risk from 2008 to 2022. Using the Cross-Sectional Autoregressive Distributed Lag (CS-ARDL) model, the research examines both...

This study examines how dismantling Mafia-connected firms affects banks' lending practices. Using a unique dataset of 667 such firms and loan-level data from the European Central Bank, our analysis shows that anti-Mafia operations precede an increase in bank loans to businesses that operate in...

Climate risk is the negative effect of climate change on several aspects of the environment, business, and society. There are two categories of climate risks: physical risks include direct impacts due to extreme events and chronic changes due to climate modifications that have become commonplace;...

Traditionally, business risk management models have not taken into consideration household composition for the purposes of credit granting or project financing in order to manage the risk of default. In this research, an improvement in the risk management model was obtained by introducing...

Over the past decade, the debate on the microeconomic effects of financial inclusion has intensified, with a growing body of research exploring how access to financial services impacts banks' behaviors. Studying the effect of financial inclusion on bank risk is crucial because it helps...

In the aftermath of global financial crises and amid increasing complexity in banking operations, understanding and managing various types of risk - especially liquidity, credit, and solvency risks - has become a global concern for financial stability. This study addresses a critical gap in the...

We develop a novel stress testing framework to quantify the risks to the German banking sector from the green transition. Our methodology combines a macro-level and a micro-level approach to calculate scenario-dependent probabilities of default and losses. The macro approach leverages...

This study examines the short-run dynamics between interest rates and non-performing loans (NPLs) in Ghana's banking sector, with particular emphasis on how fluctuations in lending rates influence credit risk and financial stability. Using quarterly data spanning 2008 to 2023, the study applies...

From an Internal Audit perspective, the integration of Artificial Intelligence (AI) into credit risk modelling through Machine Learning (ML) algorithms presents significant challenges due to the complexity and multidimensional nature of these models. While AI enhances predictive performance and...

This study examines the impact of corruption on credit risk in Southeast Asian commercial banks by using machine learning models to predict non-performing loans (NPLs) based on the Corruption Perception Index (CPI). Analyzing data from 70 banks over a decade, it employs Decision Tree, Random...

Using Credit Default Swap spreads, we construct and validate a forward-looking, market-implied carbon risk (CR) factor that captures how lenders price firms’ exposure to carbon regulation. The credit-risk impact of carbon regulation depends on its scope (breadth of coverage), stringency (share...