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...

This study investigates the impact of competition on credit risk in the Turkish banking system, focusing on Islamic (participation) and conventional banking under the same regulatory conditions regarding credit risk. The credit risk model is trained using CatBoost, extreme gradient boosting...

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...

Research documents the positive effects of state-required financial education in high school on downstream financial outcomes but pools different types of mandates. Do state policies embedding personal finance content into another course have the same effect as those that require a standalone...

This paper examines how accurately Swedish banks' internal probability of default (PD) models under IFRS 9 accounting rules predict changes in the borrowing firms' credit risk levels. Using a sample of matched bank lending and firm-level data, we find that PDs align well with aggregate...

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...

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...

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...

This paper explores the effectiveness of an innovative FinTech risk-scoring model to predict the risk-appropriate return for short-term credit sales. The risk score serves to mitigate the information asymmetry between the seller of receivables ("Seller") and the purchaser ("Funder"), at the same...

This paper estimates the causal effect of financial constraints on the short-term performance of Finnish SMEs using survey data from 2016-2024 and propensity score matching (PSM). We examine six outcomes: turnover, employment, investment, profitability, solvency, and innovation, and report...

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...

The fundamental issue with a credit system for manufacturers and importers of commodities is inefficient credit assessment. Traditional techniques frequently produce inaccurate risk assessments and credit scores, resulting in financial losses for lenders, missing business growth possibilities,...

Accurate corporate credit ratings are essential for financial risk assessment; yet, traditional methodologies relying on manual evaluation and basic statistical models often fall short in dynamic economic conditions. This study investigated the potential of machine-learning (ML) algorithms as a...