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Academic literature on the topic 'Prediction of loan default'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Prediction of loan default"

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Kohv, Keijo, and Oliver Lukason. "What Best Predicts Corporate Bank Loan Defaults? An Analysis of Three Different Variable Domains." Risks 9, no. 2 (January 25, 2021): 29. http://dx.doi.org/10.3390/risks9020029.

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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 longitudinal whole-population dataset from an Estonian commercial bank with 12,901 observations of defaulted and non-defaulted firms. The analysis is performed using statistical (logistic regression) and machine learning (neural networks) methods. Out of the three domains used, tax arrears show high prediction capabilities for bank loan defaults, while financial ratios and reporting delays are individually not useful for that purpose. The best default prediction accuracies were 83.5% with tax arrears only and 89.1% with all variables combined. The study contributes to the extant literature by enhancing the bank loan default prediction accuracy with the introduction of novel variables based on tax arrears, and also by indicating the pecking order of satisfying creditors’ claims in the firm failure process.
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Anand, Mayank, Arun Velu, and Pawan Whig. "Prediction of Loan Behaviour with Machine Learning Models for Secure Banking." Journal of Computer Science and Engineering (JCSE) 3, no. 1 (February 15, 2022): 1–13. http://dx.doi.org/10.36596/jcse.v3i1.237.

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Given loan default prediction has such a large impact on earnings, it is one of the most influential factor on credit score that banks and other financial organisations face. There have been several traditional methods for mining information about a loan application and some new machine learning methods of which, most of these methods appear to be failing, as the number of defaults in loans has increased. For loan default prediction, a variety of techniques such as Multiple Logistic Regression, Decision Tree, Random Forests, Gaussian Naive Bayes, Support Vector Machines, and other ensemble methods are presented in this research work. The prediction is based on loan data from multiple internet sources such as Kaggle, as well as data sets from the applicant's loan application. Significant evaluation measures including Confusion Matrix, Accuracy, Recall, Precision, F1- Score, ROC analysis area and Feature Importance has been calculated and shown in the results section. It is found that Extra Trees Classifier and Random Forest has highest Accuracy of using predictive modelling, this research concludes effectual results for loan credit disapproval on vulnerable consumers from a large number of loan applications
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Lee, Jong Wook, and So Young Sohn. "Evaluating borrowers’ default risk with a spatial probit model reflecting the distance in their relational network." PLOS ONE 16, no. 12 (December 31, 2021): e0261737. http://dx.doi.org/10.1371/journal.pone.0261737.

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Potential relationship among loan applicants can provide valuable information for evaluating default risk. However, most of the existing credit scoring models either ignore this relationship or consider a simple connection information. This study assesses the applicants’ relation in terms of their distance estimated based on their characteristics. This information is then utilized in a proposed spatial probit model to reflect the different degree of borrowers’ relation on the default prediction of loan applicant. We apply this method to peer-to-peer Lending Club Loan data. Empirical results show that the consideration of information on the spatial autocorrelation among loan applicants can provide high predictive power for defaults.
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Turiel, J. D., and T. Aste. "Peer-to-peer loan acceptance and default prediction with artificial intelligence." Royal Society Open Science 7, no. 6 (June 2020): 191649. http://dx.doi.org/10.1098/rsos.191649.

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Logistic regression (LR) and support vector machine algorithms, together with linear and nonlinear deep neural networks (DNNs), are applied to lending data in order to replicate lender acceptance of loans and predict the likelihood of default of issued loans. A two-phase model is proposed; the first phase predicts loan rejection, while the second one predicts default risk for approved loans. LR was found to be the best performer for the first phase, with test set recall macro score of 77.4 % . DNNs were applied to the second phase only, where they achieved best performance, with test set recall score of 72 % , for defaults. This shows that artificial intelligence can improve current credit risk models reducing the default risk of issued loans by as much as 70 % . The models were also applied to loans taken for small businesses alone. The first phase of the model performs significantly better when trained on the whole dataset. Instead, the second phase performs significantly better when trained on the small business subset. This suggests a potential discrepancy between how these loans are screened and how they should be analysed in terms of default prediction.
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Zhu, Qiliang, Wenhao Ding, Mingsen Xiang, Mengzhen Hu, and Ning Zhang. "Loan Default Prediction Based on Convolutional Neural Network and LightGBM." International Journal of Data Warehousing and Mining 19, no. 1 (January 1, 2023): 1–16. http://dx.doi.org/10.4018/ijdwm.315823.

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With the change of people's consumption mode, credit consumption has gradually become a new consumption trend. Frequent loan defaults give default prediction more and more attention. This paper proposes a new comprehensive prediction method of loan default. This method combines convolutional neural network and LightGBM algorithm to establish a prediction model. Firstly, the excellent feature extraction ability of convolutional neural network is used to extract features from the original loan data and generate a new feature matrix. Secondly, the new feature matrix is used as input data, and the parameters of LightGBM algorithm are adjusted through grid search so as to build the LightGBM model. Finally, the LightGBM model is trained based on the new feature matrix, and the CNN-LightGBM loan default prediction model is obtained. To verify the effectiveness and superiority of our model, a series of experiments were conducted to compare the proposed prediction model with four classical models. The results show that CNN-LightGBM model is superior to other models in all evaluation indexes.
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Lux, Nicole, and Sotiris Tsolacos. "Loan Characteristics as Predictors of Default in Commercial Mortgage Portfolios." International Journal of Economics and Financial Research, no. 71 (February 17, 2021): 1–4. http://dx.doi.org/10.32861/ijefr.71.1.4.

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This paper examines the role of loan characteristics in mortgage default probability for different mortgage lenders in the UK. The accuracy of default prediction is tested with two statistical methods, a probit model and linear discriminant analysis, using a unique dataset of defaulted commercial loan portfolios provided by sixty-six financial institutions. Both models establish that the attributes of the underlying real estate asset and the lender are significant factors in determining default probability for commercial mortgages. In addition to traditional risk factors such as loan-to-value and debt servicing coverage ratio lenders and regulators should consider loan characteristics to assess more accurately probabilities of default.
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Ulaga Priya, K., S. Pushpa, K. Kalaivani, and A. Sartiha. "Exploratory analysis on prediction of loan privilege for customers using random forest." International Journal of Engineering & Technology 7, no. 2.21 (April 20, 2018): 339. http://dx.doi.org/10.14419/ijet.v7i2.21.12399.

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In Banking Industry loan Processing is a tedious task in identifying the default customers. Manual prediction of default customers might turn into a bad loan in future. Banks possess huge volume of behavioral data from which they are unable to make a judgement about prediction of loan defaulters. Modern techniques like Machine Learning will help to do analytical processing using Supervised Learning and Unsupervised Learning Technique. A data model for predicting default customers using Random forest Technique has been proposed. Data model Evaluation is done on training set and based on the performance parameters final prediction is done on the Test set. This is an evident that Random Forest technique will help the bank to predict the loan Defaulters with utmost accuracy.
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Dong, Xingzhe. "Loan Default Prediction based on Machine Learning (LightGBM Model)." BCP Business & Management 25 (August 30, 2022): 457–68. http://dx.doi.org/10.54691/bcpbm.v25i.1857.

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With the rapid development of Internet finance, the number of online loan platforms has been increasing, and the scale of loan business is gradually expanding. At the same time, the bad debt and non-performing loan ratios have also risen sharply, the reasons for which include incomplete information of lenders, imprudent management of capital chain, inaccurate default prediction, etc. Therefore, loan platforms need a set of efficient and accurate loan default prediction solutions to ensure the healthy operation of Internet finance and avoid huge losses for platforms and investors. Current loan default prediction models mainly contain traditional machine learning methods such as logistic regression and decision tree, as well as integrated models such as Random Forest and GBDT. In this paper, we use LightGBM machine learning model to process massive loan default information using the lender information dataset on Tianchi Platform, and the process includes data preprocessing, feature engineering, model training and evaluation. The experimental results show that the LightGBM algorithm has a strong ability to predict loan default, with a final AUC value reaching about 0.73. This method is helpful for banks and Internet loan platforms to conduct background investigation and default prediction of loan applicants, so as to strengthen the ability of loan risk management.
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Li, Baodong. "Online Loan Default Prediction Model Based on Deep Learning Neural Network." Computational Intelligence and Neuroscience 2022 (August 8, 2022): 1–9. http://dx.doi.org/10.1155/2022/4276253.

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With the rapid development of Internet loans and the demand for Internet loans, Internet-based loan default prediction is particularly important. P2P online lending is based on Internet technology. With the popularization of personal PCs and mobile terminals, the borrower’s financing cost has been reduced to a large extent, and the efficiency of the borrower’s capital utilization has also been improved to a considerable level. Making full use of the existing data of the online lending platform, integrating third-party data, and predicting the default behavior of users are the major directions of future development. This paper mainly studies the network loan default prediction model based on DPNN. This paper first analyzes the problems and risks of the P2P online lending platform, then introduces the principle and characteristics of BPNN in detail, and determines the credit risk rating process for online lending based on BPNN. With the help of data analysis and processing software, after cleaning and variable selection of credit customer data provided by lending clubs, a set of corresponding online lending default risk assessment models are established through BPNN. This paper simulates the network loan default assessment model of the BPNN model and compares it with the support vector machine and regression model. The experimental results show that the highest accuracy rate of the BPNN model is 98.01% and the highest recall rate is 99.82%, which is better than the other two models; the AUC value of BPNN is 0.79, which is significantly higher than that of support vector machine and regression model. The above results show that the online loan default prediction model based on DPNN has high application value in practice. Predicting the probability of customer default risk in advance will help reduce the risk of P2P companies and lenders, improve the competitiveness of P2P lending institutions, and promote the development of domestic P2P platforms to be more stable.
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Singh, Amrik. "Predicting the Likelihood of Lodging CMBS Loan Default." Cornell Hospitality Quarterly 60, no. 1 (May 29, 2018): 52–68. http://dx.doi.org/10.1177/1938965518777222.

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This study investigates whether the traditional underwriting loan metrics, loan-to-value (LTV), debt coverage ratio (DSCR), and debt yield (DY) ratio, can predict lodging commercial mortgage-backed securities (CMBS) loan defaults. Using a data set of 5,266 fixed-rate lodging whole loans that were securitized into CMBS between 1996 and 2015, the results of the study provide evidence of significant relationships between all three metrics and the likelihood of default. The LTV varies positively with default, while the DSCR and DY are negatively related to default. These results hold for a subsample analysis of loans originated prior to the global financial crisis (GFC). Finally, the results show the DY spreads to be adequate proxies for the DY ratio.
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Dissertations / Theses on the topic "Prediction of loan default"

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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.
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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Stone, Devon. "An exploration of alternative features in micro-finance loan default prediction models." Master's thesis, Faculty of Science, 2020. http://hdl.handle.net/11427/32377.

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Despite recent developments financial inclusion remains a large issue for the World's unbanked population. Financial institutions - both larger corporations and micro-finance companies - have begun to provide solutions for financial inclusion. The solutions are delivered using a combination of machine learning and alternative data. This minor dissertation focuses on investigating whether alternative features generated from Short Messaging Service (SMS) data and Android application data contained on borrowers' devices can be used to improve the performance of loan default prediction models. The improvement gained by using alternative features is measured by comparing loan default prediction models trained using only traditional credit scoring data to models developed using a combination of traditional and alternative features. Furthermore, the paper investigates which of 4 machine learning techniques is best suited for loan default prediction. The 4 techniques investigated are logistic regression, random forests, extreme gradient boosting, and neural networks. Finally the paper identifies whether or not accurate loan default prediction models can be trained using only the alternative features developed throughout this minor dissertation. The results of the research show that alternative features improve the performance of loan default prediction across 5 performance indicators, namely overall prediction accuracy, repaid prediction accuracy, default prediction accuracy, F1 score, and AUC. Furthermore, extreme gradient boosting is identified as the most appropriate technique for loan default prediction. Finally, the research identifies that models trained using the alternative features developed throughout this project can accurately predict loan that have been repaid, the models do not accurately predict loans that have not been repaid.
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Shan, Chenyu, and 陜晨煜. "Credit default swaps (CDS) and loan financing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hub.hku.hk/bib/B5089965X.

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As evidenced by its market size, credit default swaps (CDSs) has been the cornerstone product of the credit derivatives market. The central question that I attempt to answer in this thesis is: why and how does the introduction of CDS market affect bank loan financing? Theoretical works predict some potential effects from CDS market, but empirical evidence is still rare. This dissertation empirically examines the effects of CDS trading on bank loan financing. In chapter one, I find that banks increase average loan amount and charge higher loan spread after the onset of CDS trading on the borrower’s debt. Also, credit quality of the borrower deteriorates for those with active CDS trading. These findings suggest that banks tend to take on more credit risk by issuing larger loans and by lending to riskier firms that could not obtain bank loan in the absence of CDS. The risk-taking by banks ultimately transmitted to higher bank-level risk profile. The second chapter is the first empirical study of CDS’ role in determining loan syndicate structure. I find larger lead bank share when CDS is in place. Moreover, participation of credit derivatives trading by lead banks is much larger than by the participants, suggesting that lead banks have better chance to use CDS to their own advantage. Further analysis shows that lead banks retain an even larger share when it is more experienced dealing with the borrower and when information asymmetry between the lender and the borrower is less severe. Different from conventional wisdom about moral hazard in syndicated lending, our findings suggest that the lead bank likely takes on more credit risk voluntarily due to its increased financing capacity. The third chapter focuses on the effects of CDS on debt contracting. Given that current evidence does not show CDS reduces average cost of debt, we conjecture that the diversification benefit is reflected by relaxation of restrictions imposed on borrowers. Consistent with our hypothesis, we find the marginal effect from CDS trading on covenant strictness measure is 16.8% on average. One standard deviation increase in the number of outstanding CDS contracts loosens net worth covenants by approximately 8.9%. Using various endogeneity controls, we are able to show the loosening of covenants is due to the reduced level of debtholder-shareholder conflict. Furthermore, the loosening effect is stronger when the expected renegotiation cost is larger, consistent with the view that CDS mitigates contracting friction and improves contracting efficiency. Overall, this dissertation attempts to provide first empirical evidence on how CDS affects bank loan financing. We focus the analysis on loan issuance, syndicate structure and contracting. The findings suggest that banks lend to riskier borrowers in the presence of CDS. On a positive note, banks tend to impose less restrictive covenants on its borrower, which may mitigate frictions in lending market in terms of ex ante bargaining and ex post renegotiation cost.
published_or_final_version
Economics and Finance
Doctoral
Doctor of Philosophy
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Tian, Shaonan. "Essays on Corporate Default Prediction." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1352403546.

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Olsen, Hunter. "An Analysis of Post Great Recession Student Loan Default." Scholarship @ Claremont, 2018. http://scholarship.claremont.edu/cmc_theses/1960.

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With more than $1.48 trillion in outstanding student loans and nearly five million Americans in default in 2017, student loans may pose one of the greatest threats to financial stability of individuals in the coming years. Failing to pay loans on time may result in wage garnishment and the suspension of Social Security payments. The second largest form of household debt, student loans are almost never dischargeable in bankruptcy and yet are critical for millions to make investments in human capital. This thesis utilizes the October 2017 addition of administrative data in the Beginning Postsecondary Students (BPS) to analyze factors influencing likelihood of student loan default in the United States up to 20 years post-enrollment. It applies logistic regression analysis to BPS 1996 and BPS 2004 and is able to trace the evolution of contributing factors over time.
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Leow, Mindy. "Credit risk models for mortgage loan loss given default." Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/170515/.

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Arguably, the credit risk models reported in the literature for the retail lending sector have so far been less developed than those for the corporate sector, mainly due to the lack of publicly available data. Having been given access to a dataset on defaulted mortgages kindly provided by a major UK bank, this work first investigates the Loss Given Default (LGD) of mortgage loans with the development of two separate component models, the Probability of Repossession (given default) Model and the Haircut (given repossession) Model. They are then combined into an expected loss percentage. Performance-wise, this two-stage LGD model is shown to do better than a single-stage LGD model (which directly models LGD from loan and collateral characteristics), as it achieves a better Rsquare value, and it more accurately matches the distribution of observed LGD. We next investigate the possibility of including macroeconomic variables into either or both component models to improve LGD prediction. Indicators relating to net lending, gross domestic product, national default rates and interest rates are considered and the interest rate is found to be most beneficial to both component models. Finally, we develop a competing risk survival analysis model to predict the time taken for a defaulted mortgage loan to reach some outcome (i.e. repossession or non-repossession). This allows for a more accurate prediction of (discounted) loss as these periods could vary from months to years depending on the health of the economy. Besides loan- or collateral-related characteristics, we incorporate a time-dependent macroeconomic variable based on the house price index (HPI) to investigate its impact on repossession risk. We find that observations of different loan-to-value ratios at default and different security type are affected differently by the economy. This model is then used for stress test purposes by applying a Monte Carlo simulation, and by varying the HPI forecast, to get different loss distributions for different economic outlooks.
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Ngufor, Patrick. "Quantitative study of perceptions of business owners and loan officers on loan delinquency and default." Thesis, University of Phoenix, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3578584.

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This research seeks to document if differences in perceptions of small business creditworthiness and lending practices of credit union and commercial lenders exist. This study applied a quantitative method to answer five questions: (1)How do small business owners perceive commercial lenders? (2)How do small business owners perceive credit union lenders? (3)How do commercial banks perceive small businesses? (4)How do credit unions perceive small businesses? (5)What are the differences in the perceptions of small businesses, commercial banks, and credit unions? The study used a quantitative survey instrument to gather data and the data was compared and contrasted among groups (Fitzgerald & Rumrill, 2004). The chi-squared test of differences in probabilities and the goodness of fit test were applied (Figure 2A) to determine if there were differences in probabilities between answers.

The results of this study are significant to small business and banking leaders by helping to define how lenders’ and small businesses’ perceptions affect the differences in loan delinquency rates between commercial lenders and credit unions lenders and by offering new insight into how loan delinquency rates can be reduced. The results also pointed to inherent perceptions of small business owners and lenders that might contribute to the root causes of loan defaults and delinquencies. The results provided information upon which small business owners and financial institution loan officers might act in order to understand how to better manage loans and to reduce the rate of loan delinquency.

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Aguilera, Nelson A. "Credit rationing and loan default in formal rural credit markets." Connect to resource, 1990. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1232115721.

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Mphaka, Patrick. "Strategies for Reducing Microfinance Loan Default in Low-Income Markets." ScholarWorks, 2017. https://scholarworks.waldenu.edu/dissertations/4391.

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Poor loan repayment causes the decline and failure of some microfinance institutions. The purpose of this qualitative multiple case study was to explore strategies that microfinance (MFI) leaders use to reduce loan default in the base of the pyramid market. The study population included 6 MFI leaders, 12 borrower community-based groups, and 4 staff members of the Adventist Development and Relief Agency (ADRA Rwanda) who reduced MFI loan default in Rwanda. Data were collected through semistructured interviews with 3 MFI leaders, 3 ADRA Rwanda staff members, and 3 members of borrower groups. Data were also collected through focus groups with 3 borrower community-based groups comprising 6 to 8 members. Additional data were collected through the analysis of MFI and ADRA Rwanda organizational documents. The Varian group lending model was the conceptual framework for the study. Data analysis involved methodological triangulation and the Gadamerian hermeneutics framework of interpretation. Four major themes emerged: intrapreneurship and environmental business opportunities, favorable loan repayment conditions, strategies for choosing borrower groups, and loan use monitoring. A sustainable microfinance institution can produce social change by providing microfinance loans that clients can use to start and grow microenterprises that can become the source of income for improving the lives of clients and their family members. Findings may also be used to create economic growth through the participation of more people in economic activities in the base of the pyramid market.
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Wang, Yangzhengxuan. "Corporate default prediction : models, drivers and measurements." Thesis, University of Exeter, 2011. http://hdl.handle.net/10036/3457.

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This thesis identifies the optimal set of corporate default drivers and examines the prediction performance of corporate default measurement tools, using a sample of companies in the United States from 1970 to 2009. In the discussion of optimal default drivers, feature selection techniques including the t-test and stepwise methods are used to filter relevant default information collected from previous empirical studies. The optimal default driver information set consists of quantitative parameters from accounting ratios, market indices, macroeconomic indicators, default history, and firm age. While both accounting ratios and market information dominate the explanatory ability, followed by default history, macroeconomic indicators contribute additional explanation for default risk. Moreover, industry effects show significance across alternative models, with the retail industry presenting as the sector with highest risk. The results are robust in both traditional and advanced random models. In investigating the optimal prediction method, two newly developed random models, mixed logit and frailty model, are tested for their theoretical superiority in capturing default clusters and unobservable information for default risk. The prediction ability of both models has been improved upon using the extended optimal set of default drivers. While the mixed logit model provides better prediction accuracy and shows stability in robustness checks, the frailty model benefits from computational efficiency and explains default clusters more thoroughly. This thesis further compares the prediction performance of large dimensional models across five categories based on the default probabilities transferred from alternative results in different models. Besides the traditional assessment criteria - covering the receiver operating characteristic curve, accuracy ratios, and classification error rates – this thesis thoroughly evaluates forecasting performance using innovative proxies including model stability under financial crisis, profitability and misclassification costs for creditors using alternative risk measurements. The practical superiority of the two advanced random models has been verified further in the comparative study.
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Books on the topic "Prediction of loan default"

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Cavazos, Lauro. Student loan default reduction initiatives. Washington, D.C: U.S. Dept. of Education, 1989.

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Babbel, David F. Insuring sovereign debt against default. Washington, D.C: World Bank, 1996.

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Hoque, Mohammad Ziaul. Industrial loan default: The case of Bangladesh. Dhaka: University Press, 2004.

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Duffie, Darrell. Multi-period corporate default prediction with stochastic covariates. Cambridge, Mass: National Bureau of Economic Research, 2006.

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Office, General Accounting. Student loans: Direct loan default rates : report to Congressional Requesters. Washington, D.C: GAO, 2000.

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Min, Qi. Loss given default of high loan-to-value residential mortgages. Washington, DC: Office of the Comptroller of the Currency, 2007.

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Donough, Kevin F. Mc. Predicting company failure: A study based on companies who have defaulted on loans. Dublin: University College Dublin, 1997.

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New Jersey. Legislature. Senate. Committee on Education. Committee meeting of Senate Education Committee: Student loan default--proprietary schools. Trenton, N.J. (State House Annex, CN 068, Trenton 08625): The Committee, 1994.

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Hua, Yu. The valuation of loan commitments with default risk: A discrete time model. Reading: University of Reading, Departmentof Economics, 1990.

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Force, New Jersey Default Task. Toward the reduction of student loan defaults in New Jersey: Report of the New Jersey Default Task Force, a joint committee of the New Jersey Association of Student Financial Aid Administrators and the New Jersey Department of Higher Education. Trenton, N.J. (20 W. State St., Trenton 08625): The Department, 1988.

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Book chapters on the topic "Prediction of loan default"

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Aditya Sai Srinivas, T., Somula Ramasubbareddy, and K. Govinda. "Loan Default Prediction Using Machine Learning Techniques." In Innovations in Computer Science and Engineering, 529–35. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8987-1_56.

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Luu, Ngo Tien, and Phan Duy Hung. "Loan Default Prediction Using Artificial Intelligence for the Borrow – Lend Collaboration." In Lecture Notes in Computer Science, 256–70. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88207-5_26.

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He, Yanghuai, Yuzhe Jian, Tianyuan Liu, and Huaijin Xue. "Optimization of Machine Learning Models for Prediction of Personal Loan Default Rate." In Atlantis Highlights in Intelligent Systems, 270–82. Dordrecht: Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-030-5_29.

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Pang, Sulin, and Jinmeng Yuan. "WT Model & Applications in Loan Platform Customer Default Prediction Based on Decision Tree Algorithms." In Intelligent Computing Theories and Application, 359–71. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95930-6_33.

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Bhandari, Amit Kumar. "Predicting the Likelihood of Loan Default Among Marginalised Population: A Case Study on Rural Bengal." In Microfinance to Combat Global Recession and Social Exclusion, 277–94. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-4329-3_19.

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Kandappan, V. A., and A. G. Rekha. "Machine Learning in Finance: Towards Online Prediction of Loan Defaults Using Sequential Data with LSTMs." In Advances in Intelligent Systems and Computing, 53–62. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1696-9_5.

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Wright, Sue. "Representations, Undertakings, and Events of Default." In International Loan Documentation, 117–84. London: Palgrave Macmillan UK, 2006. http://dx.doi.org/10.1057/9780230514799_9.

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Wright, Sue. "Representations, Undertakings and Events of Default." In The Handbook of International Loan Documentation, 152–246. London: Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1007/978-1-137-38337-2_9.

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Bing, Janet. "Default features in contour tones." In Principles and Prediction, 327. Amsterdam: John Benjamins Publishing Company, 1993. http://dx.doi.org/10.1075/cilt.98.26bin.

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Dostál, Petr, and Stanislav Škapa. "Fuzzy Clustering and Loan Risk Prediction." In Nostradamus 2014: Prediction, Modeling and Analysis of Complex Systems, 377–84. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07401-6_37.

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Conference papers on the topic "Prediction of loan default"

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Owusu, Ebenezer, Richard Quainoo, Justice Kwame Appati, and Solomon Mensah. "Loan Default Predictive Analytics." In 2022 IEEE World Conference on Applied Intelligence and Computing (AIC). IEEE, 2022. http://dx.doi.org/10.1109/aic55036.2022.9848906.

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Chen, Ya-Qi, Jianjun Zhang, and Wing W. Y. Ng. "Loan Default Prediction Using Diversified Sensitivity Undersampling." In 2018 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2018. http://dx.doi.org/10.1109/icmlc.2018.8526936.

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Lai, Lili. "Loan Default Prediction with Machine Learning Techniques." In 2020 International Conference on Computer Communication and Network Security (CCNS). IEEE, 2020. http://dx.doi.org/10.1109/ccns50731.2020.00009.

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Alam, Md Nurul, and Muhammad Masroor Ali. "Loan Default Risk Prediction Using Knowledge Graph." In 2022 14th International Conference on Knowledge and Smart Technology (KST). IEEE, 2022. http://dx.doi.org/10.1109/kst53302.2022.9729073.

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Hassan, Amira Kamil Ibrahim, and Ajith Abraham. "Modeling consumer loan default prediction using neural netware." In 2013 International Conference on Computing, Electrical and Electronics Engineering (ICCEEE). IEEE, 2013. http://dx.doi.org/10.1109/icceee.2013.6633940.

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Hassan, Amira Kamil Ibrahim, and Ajith Abraham. "Modeling consumer loan default prediction using ensemble neural networks." In 2013 International Conference on Computing, Electrical and Electronics Engineering (ICCEEE). IEEE, 2013. http://dx.doi.org/10.1109/icceee.2013.6634029.

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Soni, Aman, and K. C. Prabu Shankar. "Bank Loan Default Prediction Using Ensemble Machine Learning Algorithm." In 2022 Second International Conference on Interdisciplinary Cyber Physical Systems (ICPS). IEEE, 2022. http://dx.doi.org/10.1109/icps55917.2022.00039.

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Deng, Shizhe, Rui Li, Yaohui Jin, and Hao He. "CNN-based feature cross and classifier for loan default prediction." In Third International Conference on Image, Video Processing and Artificial Intelligence, edited by Ruidan Su. SPIE, 2020. http://dx.doi.org/10.1117/12.2579457.

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Lux, Nicole. "Relevance of loan characteristics in probability of default prediction for commercial mortgage loans." In 26th Annual European Real Estate Society Conference. European Real Estate Society, 2019. http://dx.doi.org/10.15396/eres2019_86.

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Al-qerem, Ahmad, Ghazi Al-Naymat, and Mays Alhasan. "Loan Default Prediction Model Improvement through Comprehensive Preprocessing and Features Selection." In 2019 International Arab Conference on Information Technology (ACIT). IEEE, 2019. http://dx.doi.org/10.1109/acit47987.2019.8991084.

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Reports on the topic "Prediction of loan default"

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Bhardwaj, Geetesh, and Rajdeep Sengupta. Credit Scoring and Loan Default. Federal Reserve Bank of St. Louis, 2011. http://dx.doi.org/10.20955/wp.2011.040.

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Duffie, Darrell, Leandro Siata, and Ke Wang. Multi-Period Corporate Default Prediction With Stochastic Covariates. Cambridge, MA: National Bureau of Economic Research, January 2006. http://dx.doi.org/10.3386/w11962.

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Mueller, Holger, and Constantine Yannelis. Students in Distress: Labor Market Shocks, Student Loan Default, and Federal Insurance Programs. Cambridge, MA: National Bureau of Economic Research, March 2017. http://dx.doi.org/10.3386/w23284.

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Cox, James, Daniel Kreisman, and Susan Dynarski. Designed to Fail: Effects of the Default Option and Information Complexity on Student Loan Repayment. Cambridge, MA: National Bureau of Economic Research, November 2018. http://dx.doi.org/10.3386/w25258.

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II, Dennis A. Kramer, Christina Lamb, and Lindsay Page. The Effects of Default Choice on Student Loan Borrowing: Experimental Evidence from a Public Research University. Cambridge, MA: National Bureau of Economic Research, April 2021. http://dx.doi.org/10.3386/w28703.

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Yaroshchuk, Svitlana O., Nonna N. Shapovalova, Andrii M. Striuk, Olena H. Rybalchenko, Iryna O. Dotsenko, and Svitlana V. Bilashenko. Credit scoring model for microfinance organizations. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3683.

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Abstract:
The purpose of the work is the development and application of models for scoring assessment of microfinance institution borrowers. This model allows to increase the efficiency of work in the field of credit. The object of research is lending. The subject of the study is a direct scoring model for improving the quality of lending using machine learning methods. The objective of the study: to determine the criteria for choosing a solvent borrower, to develop a model for an early assessment, to create software based on neural networks to determine the probability of a loan default risk. Used research methods such as analysis of the literature on banking scoring; artificial intelligence methods for scoring; modeling of scoring estimation algorithm using neural networks, empirical method for determining the optimal parameters of the training model; method of object-oriented design and programming. The result of the work is a neural network scoring model with high accuracy of calculations, an implemented system of automatic customer lending.
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Cabrera, Wilmar, Santiago Gamba, Camilo Gómez, and Mauricio Villamizar-Villegas. Examining Macroprudential Policy through a Microprudential Lens. Banco de la República, October 2022. http://dx.doi.org/10.32468/be.1212.

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In this paper, we examine the financial and real effects of macroprudential policies with a new identifying strategy that exploits borrower-specific provisioning levels for each bank. Locally, we compare similar firms just below and above regulatory thresholds established in Colombia during 2008--2018 for the corporate credit portfolio. Our results indicate that the scheme induces banks to increase the provisioning cost of downgraded loans. This implies that, for loans with similar risk but with a discontinuously lower rating, banks offer a lower amount of credit, demand higher quality guarantees, and impose a higher level of provision coverage through the loan-loss given default. To illustrate, a 1 percentage point (pp) increase in the provision-to-credit ratio leads to a reduction in credit growth of up to 15pp and lowers the probability of receiving new credit by up to 11pp. When mapping our results to the real sector, we find that downgraded firms are constrained in their investment decisions and experience a contraction in liabilities, equity, and total assets.
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Financial Stability Report - September 2015. Banco de la República, August 2021. http://dx.doi.org/10.32468/rept-estab-fin.sem2.eng-2015.

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From this edition, the Financial Stability Report will have fewer pages with some changes in its structure. The purpose of this change is to present the most relevant facts of the financial system and their implications on the financial stability. This allows displaying the analysis more concisely and clearly, as it will focus on describing the evolution of the variables that have the greatest impact on the performance of the financial system, for estimating then the effect of a possible materialization of these risks on the financial health of the institutions. The changing dynamics of the risks faced by the financial system implies that the content of the Report adopts this new structure; therefore, some analyses and series that were regularly included will not necessarily be in each issue. However, the statistical annex that accompanies the publication of the Report will continue to present the series that were traditionally included, regardless of whether or not they are part of the content of the Report. In this way we expect to contribute in a more comprehensive way to the study and analysis of the stability of the Colombian financial system. Executive Summary During the first half of 2015, the main advanced economies showed a slow recovery on their growth, while emerging economies continued with their slowdown trend. Domestic demand in the United States allowed for stabilization on its average growth for the first half of the year, while other developed economies such as the United Kingdom, the euro zone, and Japan showed a more gradual recovery. On the other hand, the Chinese economy exhibited the lowest growth rate in five years, which has resulted in lower global dynamism. This has led to a fall in prices of the main export goods of some Latin American economies, especially oil, whose price has also responded to a larger global supply. The decrease in the terms of trade of the Latin American economies has had an impact on national income, domestic demand, and growth. This scenario has been reflected in increases in sovereign risk spreads, devaluations of stock indices, and depreciation of the exchange rates of most countries in the region. For Colombia, the fall in oil prices has also led to a decline in the terms of trade, resulting in pressure on the dynamics of national income. Additionally, the lower demand for exports helped to widen the current account deficit. This affected the prospects and economic growth of the country during the first half of 2015. This economic context could have an impact on the payment capacity of debtors and on the valuation of investments, affecting the soundness of the financial system. However, the results of the analysis featured in this edition of the Report show that, facing an adverse scenario, the vulnerability of the financial system in terms of solvency and liquidity is low. The analysis of the current situation of credit institutions (CI) shows that growth of the gross loan portfolio remained relatively stable, as well as the loan portfolio quality indicators, except for microcredit, which showed a decrease in these indicators. Regarding liabilities, traditional sources of funding have lost market share versus non-traditional ones (bonds, money market operations and in the interbank market), but still represent more than 70%. Moreover, the solvency indicator remained relatively stable. As for non-banking financial institutions (NBFI), the slowdown observed during the first six months of 2015 in the real annual growth of the assets total, both in the proprietary and third party position, stands out. The analysis of the main debtors of the financial system shows that indebtedness of the private corporate sector has increased in the last year, mostly driven by an increase in the debt balance with domestic and foreign financial institutions. However, the increase in this latter source of funding has been influenced by the depreciation of the Colombian peso vis-à-vis the US dollar since mid-2014. The financial indicators reflected a favorable behavior with respect to the historical average, except for the profitability indicators; although they were below the average, they have shown improvement in the last year. By economic sector, it is noted that the firms focused on farming, mining and transportation activities recorded the highest levels of risk perception by credit institutions, and the largest increases in default levels with respect to those observed in December 2014. Meanwhile, households have shown an increase in the financial burden, mainly due to growth in the consumer loan portfolio, in which the modalities of credit card, payroll deductible loan, revolving and vehicle loan are those that have reported greater increases in risk indicators. On the side of investments that could be affected by the devaluation in the portfolio of credit institutions and non-banking financial institutions (NBFI), the largest share of public debt securities, variable-yield securities and domestic private debt securities is highlighted. The value of these portfolios fell between February and August 2015, driven by the devaluation in the market of these investments throughout the year. Furthermore, the analysis of the liquidity risk indicator (LRI) shows that all intermediaries showed adequate levels and exhibit a stable behavior. Likewise, the fragility analysis of the financial system associated with the increase in the use of non-traditional funding sources does not evidence a greater exposure to liquidity risk. Stress tests assess the impact of the possible joint materialization of credit and market risks, and reveal that neither the aggregate solvency indicator, nor the liquidity risk indicator (LRI) of the system would be below the established legal limits. The entities that result more individually affected have a low share in the total assets of the credit institutions; therefore, a risk to the financial system as a whole is not observed. José Darío Uribe Governor
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