This paper examines the combination of traditional banking credit assessment techniques with contemporary internal sales accounting systems in Kazakhstan, aiming to augment the precision and resilience of financial assessments pertaining to SMEs. The proposed model consists of two discrete...

Bankruptcy prediction is currently a widely researched topic, as it typically results from a chain of negative events. Logistic Regression (LR) is one of the standard prediction tools; however, with advances in technology, machine learning (ML) methods are gaining prominence and demonstrating...

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...

Islamic Microfinance Institutions (IMFIs) encounter distinct difficulties with credit scoring because they need to follow Shariah principles that combine riba bans with fair financial dealings regulations. Conventional credit scoring models exhibit two shortcomings: a poor capability to...

How borrowers respond to future changes in the interest rate on their debt matters for the transmission of monetary policy and for household financial stability. Combining bank data, a letter RCT, and a survey, we study this question in the context of the German mortgage market, where since 2022...

How borrowers respond to future changes in the interest rate on their debt matters for the transmission of monetary policy and for household financial stability. Combining bank data, a letter RCT, and a survey, we study this question in the context of the German mortgage market, where since 2022...

In dynamic models of asset markets with asymmetric information and endogenous screening, the anticipation of signaling through delayed sales incentivizes originators to exert greater effort ex ante. A central prediction in those models is a positive relationship between screening effort and the...

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...

In the peer-to-peer (P2P) lending market, current studies focus on two categories of approaches to evaluate the loans, thus providing investment suggestions to the investors: credit scoring (i.e., predicting the credit risk) and profit scoring (i.e., predicting the profitability). However,...

Research background: Financial exclusion is a very important phenomenon due to its socio-economic nature. It is linked to social exclusion and means limited access to financial services and products provided, among others, by cooperative banks. In the paper, the author presents the reasons for...

The significant increase in non-performing loans (NPLs) during the escalating recession of the Greek economy motivates us to study the predictive power of credit rating models in periods of economic shocks. In parallel, we examined the responsibilities of bank management in the expansion of NPLs...

This paper uses survival analysis as a tool to assess credit risk in loan portfolios within the framework of the Basel Internal Ratings-Based (IRB) approach. By modeling the time to default using survival functions, the methodology allows for the estimation of default probabilities and the...

This study presents a novel Early Warning System for monitoring the credit risk of commercial customers at a large international bank headquartered in the Netherlands. Traditional early warning methods often rely on backward-looking indicators such as probability of default or loss given...

Based on data from Chinese corporate bonds issued between 2014 and 2023, this study examines how ESG rating disagreement affects credit spreads. The results indicate that such disagreement significantly increases spreads through financial risk and information asymmetry channels, though this...

This study investigates the interplay between bank lending standards and credit scoring behavior of Turkish banks to SMEs. A Logistic Regression Model has been used by employing CBRT bank and firm-level micro data for the 2017Q1 - 2024Q2 period. First, one of the primary findings of the study...

This study develops a machine learning framework to improve the prediction of automobile loan defaults by integrating explainable feature selection with advanced resampling techniques. Using publicly available data, we compare Logistic Regression, Random Forest, eXtreme Gradient Boosting...

Research shows that foreign asset expropriation narrows long-term bond spreads, resulting in lower borrowing costs. However, no empirical studies have investigated the effects of expropriation on sovereign bond ratings. Bondholders and sovereign bond issuers track ratings by credit rating...