This paper aims to compare the accuracy of financial ratios, tax arrears and annual report submission delays for the prediction of bank loan defaults. To achieve this, 12 variables from these three domains are used, while the study applies a longitud...
The aim of this study is to investigate the impact of delegated monitoring by a group leader and peer monitoring by group members on loan defaults in a group-based lending program in Vietnam. The data used in the study were collected from a questionn...
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, Ra...
This paper considers a hypothetical case in which a bank wants to build a routine climate stress test exercise on residential mortgage loans. The bank has regularly updated the probability of default (PD) and loss given default (LGD) on each resident...
This paper studies the joint distribution of the default and prepayment losses for a large portfolio of loans, based on a bottom-up approach. The repayment behaviors of loans in the portfolio are determined by both systematic and idiosyncratic risk f...
Loan default prediction is a critical task for financial institutions, directly influencing risk management, loan approval decisions, and profitability. This study evaluates the effectiveness of machine learning models, specifically XGBoost, Gradient...
The topic of bank default risk in connection with government bailouts has recently attracted a great deal of attention. In this paper, the question of how a bank’s default risk is affected by a distress acquisition is investigated. Specifically...
Peer-to-peer lending, a novel element of Internet finance that links lenders and borrowers via online platforms, has generated large profits for investors. However, borrowers’ missed payments have negatively impacted the industry’s sustai...
Credit risk refers to the possibility that a borrower fails to meet contractual repayment obligations, posing potential losses to lenders. This study aims to enhance post-loan default prediction in credit risk management by constructing a time-series...
Loan defaults have become an increasing concern for lending institutions, presenting significant challenges to profitability and operational stability. However, with the advent of advanced data processing capabilities, greater data availability, and...
Small family-owned companies are the most common type of European business structure and are characterised by their orientation to long-term goals. Therefore, they can play an important role in the launching of businesses related to sustainable growt...
We utilize the data of a very large UK automobile loan firm to study the interaction of the characteristics of borrowers and loans in predicting the subsequent loan performance. Our broader findings confirm the earlier research on the issue of subpri...
A mortgage borrower has several options once a foreclosure proceedings is initiated, mainly default and prepayment. Using a sample of FHA mortgage loans, we develop a dependent competing risks framework to examine the determinants of time to default...
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 est...
We study the determinants of borrowers’ default in P2P lending with a new data set consisting of 70,673 loan observations from the Lending Club. Previous research identified a number of default determining variables but did not distinguish betw...
An accurate prediction of loan default is crucial in credit risk evaluation. A slight deviation from true accuracy can often cause financial losses to lending institutes. This study describes the non-parametric approach that compares five different m...
Bayesian inference has gained popularity in the last half of the twentieth century thanks to the wider applications in numerous fields such as economics, finance, physics, engineering, life sciences, environmental studies, and so forth. In this paper...
Predicting credit default risk is important to financial institutions, as accurately predicting the likelihood of a borrower defaulting on their loans will help to reduce financial losses, thereby maintaining profitability and stability. Although mac...
This work aims to illustrate an advanced quantitative methodology for measuring the credit risk of a loan portfolio allowing for diversification effects. Also, this methodology can allocate the credit capital coherently to each counterparty in the po...
Within bank activities, which is normally defined as the joint exercise of savings collection and credit supply, risk-taking is natural, as in many human activities. Among risks related to credit intermediation, credit risk assumes particular importa...
The number of non-payments is an indicator of delinquent behaviour in credit scoring, hence its estimation and prediction are of interest. The modelling of the number of non-payments, as count data, can be examined as a renewal process. In a renewal...
In order to stress test loan portfolios for the impacts of climate change, historical events need to be analyzed to create templates to stress test for future events. Using the 2012 Midwestern US drought as an example, this work creates a stress-test...
Background: the credit scoring model is an effective tool for banks and other financial institutions to distinguish potential default borrowers. The credit scoring model represented by machine learning methods such as deep learning performs well in t...
This study seeks to answer the question of whether we could use a bank’s past financial data to predict the bank failure in 2009 and proposes three new empirical proxies for loan quality (LQ), interest margins (IntMag), and earnings efficiency...
We research the response of the proportion of student borrowers with ninety or more days of delinquency or in default to variables such as unemployment and the average debt per borrower after the financial crisis of 2007–2008, in the United Sta...
Credit scoring models are usually formulated by fitting the probability of loan default as a function of individual evaluation attributes. Typically, these attributes are measured using a Likert-type scale, but are treated as interval scale explanato...
We investigate the default probability, recovery rates and loss distribution of a portfolio of securitised loans granted to Italian small and medium enterprises (SMEs). To this end, we use loan level data information provided by the European DataWare...
This study selected factors influencing the default risk of micro- and small-sized enterprises (MSEs) from the perspective of both financial and non-financial indicators and constructed an identification model of the influencing factors for the defau...
The transfer of rural land contractual management rights belongs to the recessive transition of land use. The mortgage of rural land management rights is a way of rural land circulation, and has an important impact on the transformation of land use....
In this study, we design stepwise ordinary least squares regression models using various amalgamations of firm features, loan characteristics and macroeconomic variables to forecast workout recovery rates for defaulted bank loans for private non-fina...
Economic disruptions can alter the likelihood of defaults on peer-to-peer loans, causing those impacted to adjust. The option to declare economic hardship and temporarily reduce the payment burden can provide some relief. When this occurs, the borrow...
Portfolio credit risk is often concerned with the tail distribution of the total loss, defined to be the sum of default losses incurred from a collection of individual loans made out to the obligors. The default for an individual loan occurs when the...
Financial institutions use credit scoring to evaluate potential loan default risks. However, insufficient credit information limits the peer-to-peer (P2P) lending platform’s capacity to build effective credit scoring. In recent years, many type...
Using stepwise logistic regression models, the study aims to separately detect and explain the determinants of default probability for unaudited and audited small-to-medium enterprises (SMEs) under stressed conditions in Zimbabwe. For effectiveness p...
Online consumer credit services play a vital role in the contemporary consumer market. To foster their sustainable development, it is essential to establish and strengthen the relevant risk management mechanism. This study proposes an intelligent man...
Recommending loan products to applicants would benefit many financial businesses and individuals. Nevertheless, many loan products suffer from the cold start problem; i.e., there are no available historical data for training the recommendation model....
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.,...
European Union legislation, particularly Council Directive 2004/113/EC, mandates gender neutrality in credit scoring to prevent discrimination. However, this creates a regulatory paradox if gender is a statistically relevant predictor of default risk...
Airtime lending default rates are typically lower than those experienced by banks and microfinance institutions (MFIs) but are likely to grow as the service is offered more widely. In this paper, credit scoring techniques are reviewed, and that knowl...
Previous research indicates that small-loan financing is a highly complex process, particularly when public sources provide financial support. This study applies propensity score matching to improve the effectiveness of closer inspection systems. Spe...
This paper seeks to identify computationally efficient importance sampling (IS) algorithms for estimating large deviation probabilities for the loss on a portfolio of loans. Related literature typically assumes that realised losses on defaulted loans...
Macroeconomic adverse selection is computed as a time series of forecast residuals via the vintage origination model for an industry dataset of auto loans. The adverse selection time series are computed separately as model residuals using logistic re...
In this article, a risk-adjusted return on capital (RAROC) valuation scheme for loans is derived. The critical assumption throughout the article is that no market information on a borrower’s credit quality like bond or CDS (Credit Default Swap)...
Predicting if a client is worth giving a loan—credit scoring—is one of the most essential and popular problems in banking. Predictive models for this goal are built on the assumption that there is a dependency between the client’s profile before the...
This paper determined the predefining factors of loan repayment behavior based on psychological and behavioral economics theories. The purpose of this research is to identify whether an individual’s credit risk can be predicted based on psychometric...
This paper analyzes different government debt relief programs in the European Monetary Union. I build a model and study different options ranging from debt relief to the European Stability Mechanism (ESM). The analysis reveals the following: First, p...
Psychometric-based credit scores measure important personality traits that are characteristic of good borrowers’ behaviors. While such data can potentially improve credit models for underbanked consumers, the utility of psychometric data in con...
A significant share of many commercial banks’ portfolios consists of residential mortgage loans provided to individuals and families. This paper examines the default and rational prepayment risk of single-borrower (residential) mortgage loans b...
The main objective of this paper is to investigate the determining factors of loan delinquencies from the perspective of borrower attributes and loan characteristics. Empirical results indicated that the borrower-lender distance factor, collateral, e...
Various types of government credit guarantee programs exist for small- and medium-sized enterprises (SMEs). The SMEs guaranteed by these programs can resolve their financial difficulties by obtaining loans from banks or being included in a pool for t...
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