Relevant bibliographies by topics / Credit risk prediction

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Credit risk prediction'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Credit risk prediction"

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Aiyegbeni, Gifty, Yang Li, Joseph Annan, and Funminiyi Adebayo. "Credit Rating Prediction Using Different Machine Learning Techniques." International Journal of Data Science and Advanced Analytics 5, no. 5 (2023): 219–38. http://dx.doi.org/10.69511/ijdsaa.v5i5.193.

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Credit rating prediction is a crucial task in the banking and financial industry. Financial firms want to identify the likelihood of customers repaying loans or credit. With the advent of machine learning algorithms and big data analytics, it is now possible to automate and improve the accuracy of credit rating prediction. In this research, we aim to develop a machine learning-based approach for customer credit rating prediction. Machine learning algorithms, including decision trees, random forests, support vector machines, and logistic regression, were evaluated and compared in terms of accuracy, precision, and AUC. Feature selection was also performed to analyze the importance of different features in predicting credit ratings. Findings suggested that status, duration, credit history, amount, savings, other debtors, property, and employment duration are the most important features in predicting credit ratings. Results showed that the support vector machine algorithm did best in predicting bad credits, achieving an accuracy of 79.7%, AUROC of 0.76, and a precision of 0.88. After optimization, an AUROC of 78% was obtained. This is a 78% accuracy for properly identifying bad credits. This research demonstrates the potential of machine learning algorithms for customer credit rating prediction and could have significant implications for the banking and financial industry by enabling more accurate and efficient credit rating predictions and reducing the risk of defaults and financial losses.
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Fauser, Daniel V., and Andreas Gruener. "Corporate Social Irresponsibility and Credit Risk Prediction: A Machine Learning Approach." Credit and Capital Markets – Kredit und Kapital: Volume 53, Issue 4 53, no. 4 (2020): 513–54. http://dx.doi.org/10.3790/ccm.53.4.513.

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This paper examines the prediction accuracy of various machine learning (ML) algorithms for firm credit risk. It marks the first attempt to leverage data on corporate social irresponsibility (CSI) to better predict credit risk in an ML context. Even though the literature on default and credit risk is vast, the potential explanatory power of CSI for firm credit risk prediction remains unexplored. Previous research has shown that CSI may jeopardize firm survival and thus potentially comes into play in predicting credit risk. We find that prediction accuracy varies considerably between algorithms, with advanced machine learning algorithms (e. g. random forests) outperforming traditional ones (e. g. linear regression). Random forest regression achieves an out-of-sample prediction accuracy of 89.75% for adjusted R2 due to the ability of capturing non-linearity and complex interaction effects in the data. We further show that including information on CSI in firm credit risk prediction does not consistently increase prediction accuracy. One possible interpretation of this result is that CSI does not (yet) seem to be systematically reflected in credit ratings, despite prior literature indicating that CSI increases credit risk. Our study contributes to improving firm credit risk predictions using a machine learning design and to exploring how CSI is reflected in credit risk ratings.
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Nguyen, Quoc Giang, Linh Hoang Nguyen, Md Monir Hosen, et al. "Enhancing Credit Risk Management with Machine Learning: A Comparative Study of Predictive Models for Credit Default Prediction." American Journal of Applied Sciences 07, no. 01 (2025): 21–30. https://doi.org/10.37547/tajas/volume07issue01-04.

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This study investigates the application of machine learning algorithms for predictive analytics in credit risk management, aiming to enhance the accuracy of predicting credit defaults. The research compares multiple machine learning models, including logistic regression, decision trees, random forests, gradient boosting, XGBoost, and LightGBM, using a real-world credit risk dataset. The study focuses on evaluating the models' performance based on metrics such as accuracy, precision, recall, and F1-score. The results show that ensemble models, particularly XGBoost and LightGBM, outperform traditional algorithms in terms of predictive accuracy and computational efficiency, demonstrating their ability to effectively handle complex datasets. The comparative analysis highlights the strengths and weaknesses of each model, providing insights into the trade-offs between interpretability and predictive power. XGBoost and LightGBM are found to be highly effective for credit risk prediction, though challenges such as model interpretability and overfitting remain. The findings suggest that machine learning offers a promising approach for improving credit risk management, with implications for the financial industry to make more informed, data-driven lending decisions. The study underscores the importance of addressing interpretability concerns and data quality issues in real-world applications, paving the way for future advancements in machine learning for credit risk prediction.
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Sarder Abdulla Al Shiam, Md Mahdi Hasan, Md Jubair Pantho, et al. "Credit Risk Prediction Using Explainable AI." Journal of Business and Management Studies 6, no. 2 (2024): 61–66. http://dx.doi.org/10.32996/jbms.2024.6.2.6.

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Despite advancements in machine-learning prediction techniques, the majority of lenders continue to rely on conventional methods for predicting credit defaults, largely due to their lack of transparency and explainability. This reluctance to embrace newer approaches persists as there is a compelling need for credit default prediction models to be explainable. This study introduces credit default prediction models employing several tree-based ensemble methods, with the most effective model, XGBoost, being further utilized to enhance explainability. We implement SHapley Additive exPlanations (SHAP) in ML-based credit scoring models using data from the US-based P2P Lending Platform, Lending Club. Detailed discussions on the results, along with explanations using SHAP values, are also provided. The model explainability generated by Shapely values enables its applicability to a broad spectrum of industry applications.
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Hjouji, Zaynab, Imane Hasinat, and Amal Hjouji. "A New Method in Machine Learning Adapted for Credit Risk Prediction of Bank Loans." Statistics, Optimization & Information Computing 13, no. 3 (2024): 1209–32. https://doi.org/10.19139/soic-2310-5070-1476.

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The recent global financial crisis has significantly impacted the financial system, leading to major bank failures and prompting a reevaluation of credit risk management models. Given its critical role in maintaining banking stability, effective credit risk forecasting methods are essential. In light of this, various studies have introduced techniques to analyze, detect, and prevent bank credit defaults. In this paper, we present a new approach for predicting credit risk, known as the “Method of Separating the Learning Set into Two Balls.” This method involves partitioning a learning set into two distinct categories: the "Performing Ball," which contains feature vectors of customers with non-defaulting credits, and the "Non-Performing Ball," which includes vectors of customers with defaulting credits. To predict a customer’s default risk, it is sufficient to determine which ball their feature vectors belong to. If a customer’s vectors do not fall into either category, additional analysis is required for making a credit decision. We evaluated the performance of this method through extensive experimental tests and a comparative analysis. The findings suggest that our approach shows considerable promise for enhancing credit risk prediction in the banking sector.
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Xiang, Siyu, and Haowen Yan. "Credit Default Prediction Based on Random Forest." Applied and Computational Engineering 97, no. 1 (2024): 89–95. http://dx.doi.org/10.54254/2755-2721/97/20241348.

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Abstract. To help the lender to make reasonable prediction judgments on the lender in advance, to get the most appropriate way of lending amount, as well as facing the risk of a reasonable response program and the ability to cope with the need to make predictions in advance before lending for personal credit risk. This paper aims to predict credit default based on random forest, logistic regression and decision tree algorithms, by comparing and analyzing the advantages and disadvantages of these algorithms, this paper finally chooses the random forest algorithm. This paper concludes that in predicting the risk of credit default, the three characteristics of Credit amount, Duration and Job have the most significant influence in predicting the risk value of the borrower, Credit amount is the most important factor that affects the risk value, Duration is also a key factor, a more relaxed repayment period will reduce the pressure of repayment, and thus reduce the risk of default. Job, different occupations, different incomes and different income stability will lead to different repayment abilities of each borrower, according to the repayment ability and the loan amount of the comparison, you can initially arrive at the corresponding risk value and the development of the corresponding risk control strategy.
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Nooji, Pavitha, and Shounak Sugave. "Explainable ensemble technique for enhancing credit risk prediction." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 917–24. https://doi.org/10.11591/ijai.v13.i1.pp917-924.

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Credit risk prediction is a critical task in financial institutions that can impact lending decisions and financial stability. While machine learning (ML) models have shown promise in accurately predicting credit risk, the complexity of these models often makes them difficult to interpret and explain. The paper proposes the explainable ensemble method to improve credit risk prediction while maintaining interpretability. In this study, an ensemble model is built by combining multiple base models that uses different ML algorithms. In addition, the model interpretation techniques to identify the most important features and visualize the model's decisionmaking process. Experimental results demonstrate that the proposed explainable ensemble model outperforms individual base models and achieves high accuracy with low loss. Additionally, the proposed model provides insights into the factors that contribute to credit risk, which can help financial institutions make more informed lending decisions. Overall, the study highlights the potential of explainable ensemble methods in enhancing credit risk prediction and promoting transparency and trust in financial decision-making.
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Nooji, Pavitha, and Shounak Sugave. "Explainable ensemble technique for enhancing credit risk prediction." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 917. http://dx.doi.org/10.11591/ijai.v13.i1.pp917-924.

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<span>Credit risk prediction is a critical task in financial institutions that can impact lending decisions and financial stability. While machine learning (ML) models have shown promise in accurately predicting credit risk, the complexity of these models often makes them difficult to interpret and explain. The paper proposes the explainable ensemble method to improve credit risk prediction while maintaining interpretability. In this study, an ensemble model is built by combining multiple base models that uses different ML algorithms. In addition, the model interpretation techniques to identify the most important features and visualize the model's decision-making process. Experimental results demonstrate that the proposed explainable ensemble model outperforms individual base models and achieves high accuracy with low loss. Additionally, the proposed model provides insights into the factors that contribute to credit risk, which can help financial institutions make more informed lending decisions. Overall, the study highlights the potential of explainable ensemble methods in enhancing credit risk prediction and promoting transparency and trust in financial decision-making.</span>
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Liu, Xiqing. "Credit Risk Classification and Prediction Based on Deep Neural Network Algorithm." Advances in Economics, Management and Political Sciences 88, no. 1 (2024): 235–41. http://dx.doi.org/10.54254/2754-1169/88/20241007.

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This study predicts whether a user has defaulted based on correlation analysis and deep neural network algorithms. The results of the study show that the occurrence of default by a user is positively correlated with age, family, years of employment, and credit length, and negatively correlated with income, amount, rate, status, and percentage of income. After model training and testing, the prediction accuracy was 81.68% on the training set and 81.68% on the test set. Specifically, there were 2858 correct predictions and 641 incorrect predictions in the training set, of which 469 incorrectly predicted that no default had occurred as having occurred and 172 incorrectly predicted that a default had occurred as not having occurred, and there were also 2858 correct predictions and 641 incorrect predictions in the test set. The results of this study show that the established model has high reliability and accuracy in accurately predicting whether a user has defaulted or not, which provides an important reference for risk assessment and decision-making.
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Kealhofer, Stephen. "Quantifying Credit Risk I: Default Prediction." Financial Analysts Journal 59, no. 1 (2003): 30–44. http://dx.doi.org/10.2469/faj.v59.n1.2501.

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Dissertations / Theses on the topic "Credit risk prediction"

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Dalla, Fontana Silvia <1991&gt. "Credit risk modelling and valuation: testing credit rating accuracy in default prediction." Master's Degree Thesis, Università Ca' Foscari Venezia, 2017. http://hdl.handle.net/10579/9894.

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Credit risk is a forward-looking concept, focusing on the probability of facing credit difficulties in the future. Credit difficulties are represented by the risk of not being paid for goods or services sold to customers. This kind of risk involves all companies from financial services industry to consumer goods. Credit risk has acquired growing importance in recent years which have been characterized by a negative economic situation, started with the US subprime mortgage crisis and the collapse of Lehman Brothers in 2008. The financial crisis intervened before Basel II could become fully effective, and unveiled the fragilities of the financial system in general, but also emphasised the inadequacy of both credit risk management and the connected credit rating system carried out by ECAIs. In Chapter I, starting from an historical excursus, the study deals with credit risk methods and rating capability to predict firms’ probability of default, taking into account both quantitative and qualitative methods and the consequent credit rating assessment. In Chapter II we focus on the trade credit insurance case. Credit insurance allows companies of any size to protect against the risk of not being paid, and this consequently increases firm’s profitability thanks to higher client portfolio quality. This means that the analysis of creditworthiness includes a wide population, from SMEs to large corporates. In Chapter III we provide an empirical analysis on the accuracy of rating system: we start from dealing with the distribution of the Probability of Default and firms’ allocation in PD classes, we analyse the Gini coefficient’s adequacy in measuring rating accuracy and we deal with a multiple regression model based on financial indicators. Finally we conclude with reflections and final comments.
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Barton, Amanda. "Split credit ratings and the prediction of bank ratings in the Basel II environment." Thesis, University of Southampton, 2006. https://eprints.soton.ac.uk/210217/.

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This thesis investigates two aspects of credit risk measurement in the context of Basel 11: The International Convergence of Capital Measurement and Capital Standards. The first is the problem arising when two credit rating agencies disagree over the rating assigned to an issuer and a split rating arises. The second area is the determination of internal credit rating models for use under the Internal ratings-based approach. This thesis presents a variety of bank rating modes for individual and long term ratings across different agencies and regions. Using an extensive database of credit rating agencies with a sample of over 52,000 split ratings covering a four year period from 1999 - 2004 the first study shows that there is a ranking of agencies from the most to least generous that is stable over time. In most cases, the differences between the mean ratings of the agencies are significantly different from each other at the 1% level. The greatest differences arise between the US and Japanese agencies. When the split ratings are compared in terms of Basel II risk weights the differences between the US and Japanese agencies are still highly significant and the conclusion is that supervisors should alter the mapping of the Japanese agencies to the risk assessments under the provisions of Annex 2 to Basel II. Contrary to earlier research this study does not find that the highest level of split ratings arise for banks. The level of consensus between agencies appears to correspond to the average credit quality of the industry in question. Bank credit ratings are modelled from financial ratios and variables using ordinal logistic regression. Sample sizes exceeded 1,100 banks for the largest agencies.
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Wood, Anthony Paul. "The performance of insolvency prediction and credit risk models in the UK : a comparative study, development and wider application." Thesis, University of Exeter, 2012. http://hdl.handle.net/10036/4211.

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Contingent claims models have recently been applied to the field of corporate insolvency prediction in an attempt to provide the art with a theoretical methodology that has been lacking in the past. Limited studies have been carried out in order to empirically compare the performance of these “market” models with that of their accounting number-based counterparts. This thesis contributes to the literature in several ways: The thesis traces the evolution of the art of corporate insolvency prediction from its inception through to the present day, combining key developments and methodologies into a single document of reference. I use receiver operating characteristic curves and tests of economic value to assess the efficacy of sixteen models, carefully selected to represent key moments in the evolution of the art, and tested upon, for the first time, post-IFRS UK data. The variability of model efficacy is also measured for the first time, using Monte Carlo simulation upon 10,000 randomly generated training and validation samples from a dataset consisting of over 12,000 firmyear observations. The results provide insights into the distribution of model accuracy as a result of sample selection, which is something which has not appeared in the literature prior to this study. I find overall that the efficacy of the models is generally less than that reported in the prior literature; but that the theoretically driven, market-based models outperform models which use accounting numbers; the latter showing a relatively larger efficacy distribution. Furthermore, I obtain the counter-intuitive finding that predictions based on a single ratio can be as efficient as those which are based on models which are far more complicated – in terms of variable variety and mathematical construction. Finally, I develop and test a naïve version of the down-and-out-call barrier option model for insolvency prediction and find that, despite its simple formulation, it performs favourably compared alongside other market-based models.
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Zhiyong, Li. "Predicting financial distress using corporate efficiency and corporate governance measures." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/9934.

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Credit models are essential to control credit risk and accurately predicting bankruptcy and financial distress is even more necessary after the recent global financial crisis. Although accounting and financial information have been the main variables in corporate credit models for decades, academics continue searching for new attributes to model the probability of default. This thesis investigates the use of corporate efficiency and corporate governance measures in standard statistical credit models using cross-sectional and hazard models. Relative efficiency as calculated by Data Envelopment Analysis (DEA) can be used in prediction but most previous literature that has used such variables has failed to follow the assumptions of Variable Returns to Scale and sample homogeneity and hence the efficiency may not be correctly measured. This research has built industry specific models to successfully incorporate DEA efficiency scores for different industries and it is the first to decompose overall Technical Efficiency into Pure Technical Efficiency and Scale Efficiency in the context of modelling financial distress. It has been found that efficiency measures can improve the predictive accuracy and Scale Efficiency is a more important measure of efficiency than others. Furthermore, as no literature has attempted a panel analysis of DEA scores to predict distress, this research has extended the cross sectional analysis to a survival analysis by using Malmquist DEA and discrete hazard models. Results show that dynamic efficiency scores calculated with reference to the global efficiency frontier have the best discriminant power to classify distressed and non-distressed companies. Four groups of corporate governance measures, board composition, ownership structure, management compensation and director and manager characteristics, are incorporated in the hazard models to predict financial distress. It has been found that state control, institutional ownership, salaries to independent directors, the Chair’s age, the CEO’s education, the work location of independent directors and the concurrent position of the CEO have significant associations with the risk of financial distress. The best predictive accuracy is made from the model of governance measures, financial ratios and macroeconomic variables. Policy implications are advised to the regulatory commission.
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Karas, Michal. "Měření úvěrového rizika podniků zpracovatelského průmyslu v České republice." Doctoral thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2013. http://www.nusl.cz/ntk/nusl-233756.

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The purpose of this doctoral thesis is to create a new bankruptcy prediction model and also to design how to use this model for the purposes of credit risk measuring. The starting-point of this work is the analysis of traditional bankruptcy models. It was found out that the traditional bankruptcy model are not enough effective in the current economic conditions and it is necessary to create a new ones. Based on the identified deficiencies of the traditional models a set of two new model series was created. The first series of the created models is based on the use of parametric methods, and the second one is based on the use of newer nonparametric approach. Moreover, a set of factors which are able to identify an imminent bankruptcy was analyzed. It was found, that significant signs of imminent bankruptcy can be identified even five years before the bankruptcy occurs. Based on these findings a new model was created. This model incorporates variables of static and even dynamic character for bankruptcy prediction purposes. The overall classification accuracy of this model is 92.27% of correctly classified active companies and 95.65% of correctly classified bankrupt companies.
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Granström, Daria, and Johan Abrahamsson. "Loan Default Prediction using Supervised Machine Learning Algorithms." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252312.

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It is essential for a bank to estimate the credit risk it carries and the magnitude of exposure it has in case of non-performing customers. Estimation of this kind of risk has been done by statistical methods through decades and with respect to recent development in the field of machine learning, there has been an interest in investigating if machine learning techniques can perform better quantification of the risk. The aim of this thesis is to examine which method from a chosen set of machine learning techniques exhibits the best performance in default prediction with regards to chosen model evaluation parameters. The investigated techniques were Logistic Regression, Random Forest, Decision Tree, AdaBoost, XGBoost, Artificial Neural Network and Support Vector Machine. An oversampling technique called SMOTE was implemented in order to treat the imbalance between classes for the response variable. The results showed that XGBoost without implementation of SMOTE obtained the best result with respect to the chosen model evaluation metric.<br>Det är nödvändigt för en bank att ha en bra uppskattning på hur stor risk den bär med avseende på kunders fallissemang. Olika statistiska metoder har använts för att estimera denna risk, men med den nuvarande utvecklingen inom maskininlärningsområdet har det väckt ett intesse att utforska om maskininlärningsmetoder kan förbättra kvaliteten på riskuppskattningen. Syftet med denna avhandling är att undersöka vilken metod av de implementerade maskininlärningsmetoderna presterar bäst för modellering av fallissemangprediktion med avseende på valda modelvaldieringsparametrar. De implementerade metoderna var Logistisk Regression, Random Forest, Decision Tree, AdaBoost, XGBoost, Artificiella neurala nätverk och Stödvektormaskin. En översamplingsteknik, SMOTE, användes för att behandla obalansen i klassfördelningen för svarsvariabeln. Resultatet blev följande: XGBoost utan implementering av SMOTE visade bäst resultat med avseende på den valda metriken.
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Sayeh, Wafa. "Le financement bancaire des Petites et Moyennes Entreprises : rationnement de crédit, conditions d'emprunt et notation." Thesis, Cergy-Pontoise, 2014. http://www.theses.fr/2014CERG0713.

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Les travaux de recherche menés dans cette thèse répondent à plusieurs problématiques concernant les Petites et Moyennes Entreprises (PME). Après un état de l'art et une proposition de classification des types de rationnement de crédit, les deux premières études s'interrogent sur l'accès des PME aux crédits bancaires : la première question concerne la prédiction du rationnement de crédit à partir des caractéristiques des PME, la deuxième est relative aux déterminants des conditions de crédit. Enfin, la troisième étude teste l'existence et les causes de la divergence des notations de crédit des PME. Les travaux économétriques menés dans ces trois études se sont appuyés sur deux échantillons différents : l'un construit à partir d'un questionnaire sur le rationnement du crédit envoyé à un panel de PME, l'autre contenant les PME clientes d'un établissement bancaire, ayant obtenu au moins un crédit sur la période d'étude de quatre ans<br>This dissertation addresses several issues facing Small and Medium Enterprises (SMEs). The first three articles are focusing on SMEs' access to bank loans. This issue contains two areas for intervention. The first is the prediction of credit rationing decision based upon SMEs characteristics. The second relates to the determinants of credit terms. The fourth article approaches the issue of the existence and causes of split rating. Researches covered in this thesis are based on two different samples. The first sample was constructed from a credit rationing survey sent to an SMEs panel. The second sample was supplied by one French mutual bank and relates to information on its credit reports and credit history over the period from 2007 to 2010
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GRASSELLI, FRANCESCA. "L'Analisi e la Previsione delle Insolvenze: Lo Studio del Caso Italiano." Doctoral thesis, Università Cattolica del Sacro Cuore, 2007. http://hdl.handle.net/10280/132.

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A causa delle conseguenze che il fenomeno comporta, sia sul piano finanziario sia sul fronte dell'economia reale, l'analisi e la previsione delle insolvenze societarie continua a rappresentare un argomento attuale nell'ambito della ricerca economica. I recenti sforzi condotti dal Comitato di Basilea verso la diffusione di criteri di valutazione del rischio di credito più precisi ed oggettivi, hanno ulteriormente accresciuto l'importanza della materia. L'obiettivo del presente studio è l'analisi del fenomeno del fallimento sul territorio italiano, al fine di valutare quali variabili sono più efficaci nell'individuazione di una situazione di dissesto dell'impresa. Per l'analisi si sono sviluppati dei modelli di previsione delle insolvenze in grado di individuare i segnali early warning di dissesto finanziario. L'analisi econometrica è basata su un campione ampio ed originale di fallimenti rilevati negli anni 2003 e 2004: a tal fine sono stati costituiti dei campioni comparabili di imprese fallite e non fallite ed è stato verificato, mediante l'applicazione di una metodologia logit, il potere previsivo di diversi indici di bilancio e di variabili di tipo non finanziario. I risultati ottenuti sono stati validati su un campione hold-out. L'analisi si evidenzia l'importanza delle caratteristiche del settore di attività nel determinare la forma del processo di fallimento: i modelli sector specific ottengono risultati migliori rispetto ai modelli generali stimati. Inoltre, alcuni fattori comuni ai diversi settori di attività si dimostrano particolarmente efficaci nella previsione dei dissesti aziendali: l'età, il livello di leverage e la composizione del debito d'impresa, così come la sua redditività.<br>Due to the consequences that the phenomenon entails both on the financial and real sides of the economy, the analysis and prediction of corporate failures continue to be a current topic in economic research. The recent efforts laid by the Basel Committee towards the diffusion of more precise and objective ways of assessing credit risk have further increased the importance of this matter. The purpose of the study is to analyse the bankruptcy phenomenon among Italian firms, in order to assess what firm-specific and industry variables are more important in determining corporate failure events. We develop a bankruptcy prediction model that aims at detecting early signals of financial distress. The econometric analysis is based on a wide and unique sample of recent failure events: comparable sets of bankrupt and non-bankrupt firms are identified and several prior balance-sheet and economic indicators are tested for their power in predicting failure probabilities in a logit modelling framework; model performances are cross-validated on hold-out samples. The analyses provide evidence of the importance of industry membership in determining and shaping corporate failure processes: sector-specific models produce a better assessment of financial distress than general ones. Also, some common factors emerge as important predictors of corporate collapse across different industries: age, gearing and the composition of a firm's debt, as well as its capability of generating profits.
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GRASSELLI, FRANCESCA. "L'Analisi e la Previsione delle Insolvenze: Lo Studio del Caso Italiano." Doctoral thesis, Università Cattolica del Sacro Cuore, 2007. http://hdl.handle.net/10280/132.

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A causa delle conseguenze che il fenomeno comporta, sia sul piano finanziario sia sul fronte dell'economia reale, l'analisi e la previsione delle insolvenze societarie continua a rappresentare un argomento attuale nell'ambito della ricerca economica. I recenti sforzi condotti dal Comitato di Basilea verso la diffusione di criteri di valutazione del rischio di credito più precisi ed oggettivi, hanno ulteriormente accresciuto l'importanza della materia. L'obiettivo del presente studio è l'analisi del fenomeno del fallimento sul territorio italiano, al fine di valutare quali variabili sono più efficaci nell'individuazione di una situazione di dissesto dell'impresa. Per l'analisi si sono sviluppati dei modelli di previsione delle insolvenze in grado di individuare i segnali early warning di dissesto finanziario. L'analisi econometrica è basata su un campione ampio ed originale di fallimenti rilevati negli anni 2003 e 2004: a tal fine sono stati costituiti dei campioni comparabili di imprese fallite e non fallite ed è stato verificato, mediante l'applicazione di una metodologia logit, il potere previsivo di diversi indici di bilancio e di variabili di tipo non finanziario. I risultati ottenuti sono stati validati su un campione hold-out. L'analisi si evidenzia l'importanza delle caratteristiche del settore di attività nel determinare la forma del processo di fallimento: i modelli sector specific ottengono risultati migliori rispetto ai modelli generali stimati. Inoltre, alcuni fattori comuni ai diversi settori di attività si dimostrano particolarmente efficaci nella previsione dei dissesti aziendali: l'età, il livello di leverage e la composizione del debito d'impresa, così come la sua redditività.<br>Due to the consequences that the phenomenon entails both on the financial and real sides of the economy, the analysis and prediction of corporate failures continue to be a current topic in economic research. The recent efforts laid by the Basel Committee towards the diffusion of more precise and objective ways of assessing credit risk have further increased the importance of this matter. The purpose of the study is to analyse the bankruptcy phenomenon among Italian firms, in order to assess what firm-specific and industry variables are more important in determining corporate failure events. We develop a bankruptcy prediction model that aims at detecting early signals of financial distress. The econometric analysis is based on a wide and unique sample of recent failure events: comparable sets of bankrupt and non-bankrupt firms are identified and several prior balance-sheet and economic indicators are tested for their power in predicting failure probabilities in a logit modelling framework; model performances are cross-validated on hold-out samples. The analyses provide evidence of the importance of industry membership in determining and shaping corporate failure processes: sector-specific models produce a better assessment of financial distress than general ones. Also, some common factors emerge as important predictors of corporate collapse across different industries: age, gearing and the composition of a firm's debt, as well as its capability of generating profits.
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Bär, Tobias. "Predicting and hedging credit portfolio risk with macroeconomic factors /." Hamburg : Kovac, 2002. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=009735176&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Books on the topic "Credit risk prediction"

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Jones, Stewart, and David A. Hensher, eds. Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction. Cambridge University Press, 2008. http://dx.doi.org/10.1017/cbo9780511754197.

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Bär, Tobias. Predicting and hedging credit portfolio risk with macroeconomic factors. Kovac, 2002.

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Kazakova, Nataliya. Financial security of the company. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1908969.

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The textbook provides theoretical and practical training of business analysts on the financial security of companies. Considers the regulatory legal and methodological basis for the diagnosis of bankruptcy of organizations, as well as corporate fraud as a type of economic crimes; analytical tools for assessing the level of financial security based on a risk-oriented approach, the basics of building an internal financial security control system, including monitoring of the company's business processes affecting its financial security, as well as methods for assessing the risks of corporate fraud. The methods of diagnostics of the processes of companies' activities that contribute to improving their financial security through the introduction of a comprehensive digital environment, predictive analytics and big data technology into the control and diagnostic processes of business management are considered. Each chapter includes knowledge assessment questions, tests and situational tasks.&#x0D; It complies with the federal state educational standards of higher education of the latest generation, is focused on the competence model of the main professional educational programs, and also provides the functionality (requirements for labor functions) of employees laid down in the state professional standard "Business Analyst".&#x0D; For master's degree students studying in the areas of 38.04.01 "Economics", 38.04.02 "Management", 38.04.08 "Finance and Credit".
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Jones, Stewart, and David A. Hensher. Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction. Cambridge University Press, 2008.

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Jones, Stewart, and David A. Hensher. Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction. Cambridge University Press, 2008.

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Jones, Stewart, and David A. Hensher. Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction. Cambridge University Press, 2008.

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Advances in credit risk modelling and corporate bankruptcy prediction. Cambridge University Press, 2008.

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Jones, Stewart, and David A. Hensher. Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction. Cambridge University Press, 2010.

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Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction. Cambridge University Press, 2008.

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(Editor), Stewart Jones, and David A. Hensher (Editor), eds. Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction (Quantitative Methods for Applied Economics and Business Research). Cambridge University Press, 2008.

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Book chapters on the topic "Credit risk prediction"

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Kumar, Ashish, Roheet Bhatnagar, and Sumit Srivastava. "Analysis of Credit Risk Prediction Using ARSkNN." In The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74690-6_63.

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Wang, Liping, and Fanglin An. "Machine Learning Algorithm Credit Risk Prediction Model." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53980-1_15.

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Samanvitha, G. S., K. Aditya Shastry, N. Vybhavi, N. Nidhi, and R. Namratha. "Machine Learning Based Consumer Credit Risk Prediction." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9012-9_10.

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Zhang, Juncheng. "Personal Credit Loans Risk Prediction Based on NS3-LightGBM." In Advances in Economics, Business and Management Research. Atlantis Press International BV, 2024. http://dx.doi.org/10.2991/978-94-6463-546-1_7.

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Cong, Yuyue. "Bank Credit Risk Prediction Based on MCDM and CNN." In Innovative Computing Vol 1 - Emerging Topics in Artificial Intelligence. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2092-1_39.

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Wang, Daixin, Zhiqiang Zhang, Jun Zhou, et al. "Temporal-Aware Graph Neural Network for Credit Risk Prediction." In Proceedings of the 2021 SIAM International Conference on Data Mining (SDM). Society for Industrial and Applied Mathematics, 2021. http://dx.doi.org/10.1137/1.9781611976700.79.

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Chen, Zhaoyan. "The Role of Alternative Data in Credit Risk Prediction." In Advances in Economics, Business and Management Research. Atlantis Press International BV, 2025. https://doi.org/10.2991/978-94-6463-652-9_76.

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Zhou, Hong, Jingyi Wang, and Yilin Qiu. "Empirical Study on Firm Credit Risk Prediction Based on Default Distance." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28466-3_93.

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Chen, Shin-Fu, Goutam Chakraborty, and Li-Hua Li. "Feature Selection on Credit Risk Prediction for Peer-to-Peer Lending." In New Frontiers in Artificial Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31605-1_1.

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Nejjar, Chaymae, Mohammed Kaicer, Sara El Haimer, Azzeddine Idhmad, and Loubna Essairh. "Credit Risk Management in Microfinance: Application of Non-repayment Prediction Models." In International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD'2023). Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54318-0_26.

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Conference papers on the topic "Credit risk prediction"

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Palaiokrassas, Georgios, Sandro Scherrers, Eftychia Makri, and Leandros Tassiulas. "Machine Learning in DeFi: Credit Risk Assessment and Liquidation Prediction." In 2024 IEEE International Conference on Blockchain and Cryptocurrency (ICBC). IEEE, 2024. http://dx.doi.org/10.1109/icbc59979.2024.10634435.

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Li, Cong, and Jin Zhang. "Research on Credit Risk Prediction Models Based on Machine Learning." In 2024 6th International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI). IEEE, 2024. https://doi.org/10.1109/mlbdbi63974.2024.10824021.

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Chandna, Manisha, Brajesh Kumar Umrao, Madhur Grover, Jamuna K. V, Trapty Agarwal, and Anitha D. Souza J. "Artificial Intelligence in Banking: Regression Analysis for Credit Risk Prediction." In 2025 International Conference on Automation and Computation (AUTOCOM). IEEE, 2025. https://doi.org/10.1109/autocom64127.2025.10957362.

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Sierra, Evelyn, Erick Delenia, Eric Saputra Lays, Riyanarto Sarno, and Agus Tri Haryono. "A Comparative Analysis of Macroeconomic Indicators in Optimising Credit Risk Prediction." In 2024 2nd International Conference on Technology Innovation and Its Applications (ICTIIA). IEEE, 2024. https://doi.org/10.1109/ictiia61827.2024.10761138.

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Sanyal, Parambrata, Mukund Kuthe, Sudhanshu Maurya, Kashish Mirza, Pradnya Borkar, and Rachit Garg. "A Voting Ensemble Learning Model for Improved Credit Default Risk Prediction." In 2024 Global Conference on Communications and Information Technologies (GCCIT). IEEE, 2024. https://doi.org/10.1109/gccit63234.2024.10862547.

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Yu, Guanfang. "Intelligent Credit Assessment and Risk Prediction Model based on Machine Learning." In 2025 3rd International Conference on Data Science and Information System (ICDSIS). IEEE, 2025. https://doi.org/10.1109/icdsis65355.2025.11070653.

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Zhang, Yue, and Xuechun Liang. "Personal Credit Risk Prediction Based on Minimum Weight Value Error Combination Model." In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025. https://doi.org/10.1109/icaace65325.2025.11020278.

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Kalpana, G., Bhoomika S S, Nagella Venkata Ramana, N. Shilpa, and Ramy Riad Hussein. "Dynamic Ensemble Machine Learning Classifier Based Credit Card Financial Risk Management and Prediction." In 2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC). IEEE, 2024. https://doi.org/10.1109/icdscnc62492.2024.10939444.

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Yu, Chang, Yixin Jin, Qianwen Xing, Ye Zhang, Shaobo Guo, and Shuchen Meng. "Advanced User Credit Risk Prediction Model Using LightGBM, XGBoost and Tabnet with SMOTEENN." In 2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS). IEEE, 2024. https://doi.org/10.1109/icpics62053.2024.10796247.

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Satheeshkumar, S., M. Dakshana, K. Gunalan, P. Anandan, R. Saveetha, and M. Nithya. "Leveraging Machine Learning and Forecasting Techniques to Enhance Credit Risk Analysis and Prediction." In 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS). IEEE, 2024. https://doi.org/10.1109/icssas64001.2024.10760746.

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Reports on the topic "Credit risk prediction"

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Blanco, Roberto, Elena Fernández, Miguel García-Posada, and Sergio Mayordomo. An estimation of the default probabilities of Spanish non-financial corporations and their application to evaluate public policies. Banco de España, 2023. http://dx.doi.org/10.53479/33512.

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We model the one-year ahead probability for default of Spanish non-financial corporations using data for the period 1996-2019. While most previous literature considers that a firm is in default if it files for bankruptcy, we define default as having non-performing loans during at least three months of a given year. This broader definition allows us to predict firms’ financial distress at an earlier stage that cannot generally be observed by researchers, before their financial conditions become too severe and they have to file for bankruptcy or engage in private workouts with their creditors. We estimate, by means of logistic regressions, both a general model that uses all the firms in the sample and six models for different size-sector combinations. The selected explanatory variables are five accounting ratios, which summarise firms’ creditworthiness, and the growth rate of aggregate credit to non-financial corporations, to take into account the role of credit availability in mitigating the risk of default. Finally, we carry out two applications of our prediction models: we construct credit rating transition matrices and evaluate a programme implemented by the Spanish government to provide direct aid to firms severely affected by the COVID-19 crisis.
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Boyarchenko, Nina, and Leonardo Elias. The Global Credit Cycle. Federal Reserve Bank of New York, 2024. http://dx.doi.org/10.59576/sr.1094.

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Do global credit conditions affect local credit and business cycles? Using a large cross-section of equity and corporate bond market returns around the world, we construct a novel global credit factor and a global risk factor that jointly price the international equity and bond cross-section. We uncover a global credit cycle in risky asset returns, which is distinct from the global risk cycle. We document that the global credit cycle in asset returns translates into a global credit cycle in credit quantities, with a tightening in global credit conditions predicting extreme capital flow episodes and declines in the stock of country-level private debt. Furthermore, global credit conditions predict the mean and left tail of real GDP growth outcomes at the country level. Thus, the global pricing of corporate credit is a fundamental factor in driving local credit conditions and real outcomes.
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Álvarez-Román, Laura, Sergio Mayordomo, Carles Vergara-Alert, and Xavier Vives. Climate risk, soft information and credit supply. Banco de España, 2024. http://dx.doi.org/10.53479/36112.

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We study a model of the impact of climate risk on credit supply and test its predictions using data on all wildfires and corporate loans in Spain. Our findings reveal a significant decrease in credit following climate-driven events. This result is driven by outsider banks (large and diversified), which reduce lending significantly to firms in affected areas. By contrast, due to their access to soft information, local banks (geographically concentrated) reduce their loans to opaque affected firms to a lesser extent without increasing their risk. We also find that employment decreases in affected areas where local banks are not present.
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Morales, Paola, Daniel Osorio-Rodíguez, Juan S. Lemus-Esquivel, and Miguel Sarmiento. The internationalization of domestic banks and the credit channel of monetary policy. Banco de la República, 2021. http://dx.doi.org/10.32468/be.1181.

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How does the expansion of domestic banks in international markets affect the bank lending channel of monetary policy? Using bank-firm loan-level data, we find that loan growth and loan rates from international banks respond less to monetary policy changes than domestic banks and that internationalization partially mitigates the risk-taking channel of monetary policy. Banks with a large international presence tend to tolerate more their credit risk exposition relative to domestic banks. Moreover, international banks tend to rely more on foreign funding when policy rates change, allowing them to insulate better the monetary policy changes from their credit supply than domestic banks. This result is consistent with the predictions of the internal capital markets hypothesis. We also show that macroprudential FX regulation reduces banks with high FX exposition access to foreign funding, ultimately contributing to monetary policy transmission. Overall, our results suggest that the internationalization of banks lowers the potency of the bank lending channel. Furthermore, it diminishes the risk-taking channel of monetary policy within the limit established by macroprudential FX regulations.
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Lozano-Espitia, Ignacio, and Fernando Arias-Rodríguez. The Relationship between Fiscal and Monetary Policies in Colombia: An Empirical Exploration of the Credit Risk Channel. Banco de la República, 2022. http://dx.doi.org/10.32468/be.1196.

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This paper aims to provide evidence on the relationship between fiscal and monetary policy in Colombia through an empirical exploration of the credit risk channel. Under this approach, fiscal policy plays an important explanatory role in the sovereign risk premium, which, in turn, could affect the exchange rate and inflation expectations. The Central Bank reacts to inflation expectations using the policy interest rate; consequently, such reaction could be indirectly influenced by fiscal behavior. Using monthly data from January 2003 to December 2019, we estimate both jointly and independently the reduced-form core equations of a system that describes the credit risk channel in a small open economy. Our findings are in line with the model predictions. Fiscal policy affected the country’s sovereign risk during this period, but only slightly. Hence, there is insufcient evidence to sustain the idea that monetary policy has been signifcantly influenced by government fiscal management.
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Boyarchenko, Nina, and Leonardo Elias. Corporate Debt Structure over the Global Credit Cycle. Federal Reserve Bank of New York, 2024. https://doi.org/10.59576/sr.1139.

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We study the determinants of active debt management through issuance and refinancing decisions for firms around the world. We leverage instrument-level data to create a comprehensive picture of the maturity, currency, and security type composition of firms' debt for a large cross-section of countries. At the instrument level, we estimate a predictive model of prepayment as a function of interest costs savings and maturity lengthening motives. We document that there is substantial heterogeneity in prepayment across bonds and loans and across firms, depending on their reliance on bank lending. While debt prepayment is generally successful at extending average maturities and lowering interest rate costs at the firm level, these benefits appear smaller for issuers in emerging market economies. Tight global credit conditions reduce both the ability to prepay debt early and the effectiveness of debt refinancing in reducing interest costs and rollover risk. Put together, our results show that the impact of global credit conditions on firms' debt structure can be traced back to how instrument-level prepayment incentives change over the global credit cycle.
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Fernandez, Andres, Julián Caballero, and Jongho Park. On Corporate Borrowing, Credit Spreads and Economic Activity in Emerging Economies: An Empirical Investigation. Inter-American Development Bank, 2016. http://dx.doi.org/10.18235/0011756.

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This paper studies the influence of external financial factors on economic activity in emerging economies (EMEs) motivated by a considerable increase in foreign financing by the corporate sector in EMEs since the early 2000s, mainly in the form of bond issuance. A quarterly external financial indicator for several EMEs is built using bond-level data on spreads of corporate bonds issued in foreign capital markets, and its relationship with economic activity is examined. Results show that the indicator has considerable predictive power on future economic activity. Furthermore, an identified adverse shock to the financial indicator generates a large and protracted fall in real output growth, and about a third of its forecast error variance is associated with this shock. These findings are robust to controlling for possible spillovers from sovereign to corporate risk, among other considerations.
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Micco, Alejandro, Andrew Powell, and Arturo Galindo. Loyal Lenders or Fickle Financiers: Foreign Banks in Latin America. Inter-American Development Bank, 2005. http://dx.doi.org/10.18235/0010962.

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We suggest that foreign banks may represent a trade-off for their developing country hosts. A portfolio model is developed to show that a more diversified international bank may be one of lower, overall risk and less susceptible to funding shocks but may react more to shocks that affect expected returns in a particular host country. Foreign banks have become particularly important in Latin America where we find strong support for these theoretical predictions using a dataset of individual Latin American banks in 11 countries. Moreover, we find no significant difference between the size of the response of foreign banks to a negative liquidity shock and a positive opportunity shock: in both cases the market share of foreign banks in credit increases.
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Cattaneo, Matias D., Richard K. Crump, and Weining Wang. Beta-Sorted Portfolios. Federal Reserve Bank of New York, 2023. http://dx.doi.org/10.59576/sr.1068.

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Beta-sorted portfolios—portfolios comprised of assets with similar covariation to selected risk factors—are a popular tool in empirical finance to analyze models of (conditional) expected returns. Despite their widespread use, little is known of their statistical properties in contrast to comparable procedures such as two-pass regressions. We formally investigate the properties of beta-sorted portfolio returns by casting the procedure as a two-step nonparametric estimator with a nonparametric first step and a beta-adaptive portfolios construction. Our framework rationalizes the well-known estimation algorithm with precise economic and statistical assumptions on the general data generating process. We provide conditions that ensure consistency and asymptotic normality along with new uniform inference procedures allowing for uncertainty quantification and general hypothesis testing for financial applications. We show that the rate of convergence of the estimator is non-uniform and depends on the beta value of interest. We also show that the widely used Fama-MacBeth variance estimator is asymptotically valid but is conservative in general and can be very conservative in empirically relevant settings. We propose a new variance estimator, which is always consistent and provide an empirical implementation which produces valid inference. In our empirical application we introduce a novel risk factor—a measure of the business credit cycle—and show that it is strongly predictive of both the cross-section and time-series behavior of U.S. stock returns.
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