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1
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.
Повний текст джерелаАнотація:
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.
2
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.
Повний текст джерелаАнотація:
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
3
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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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Niu, Beibei, Jinzheng Ren, and Xiaotao Li. "Credit Scoring Using Machine Learning by Combing Social Network Information: Evidence from Peer-to-Peer Lending." Information 10, no. 12 (December 17, 2019): 397. http://dx.doi.org/10.3390/info10120397.
Повний текст джерелаАнотація:
Financial institutions use credit scoring to evaluate potential loan default risks. However, insufficient credit information limits the peer-to-peer (P2P) lending platform’s capacity to build effective credit scoring. In recent years, many types of data are used for credit scoring to compensate for the lack of credit history data. Whether social network information can be used to strengthen financial institutions’ predictive power has received much attention in the industry and academia. The aim of this study is to test the reliability of social network information in predicting loan default. We extract borrowers’ social network information from mobile phones and then use logistic regression to test the relationship between social network information and loan default. Three machine learning algorithms—random forest, AdaBoost, and LightGBM—were constructed to demonstrate the predictive performance of social network information. The logistic regression results show that there is a statistically significant correlation between social network information and loan default. The machine learning algorithm results show that social network information can improve loan default prediction performance significantly. The experiment results suggest that social network information is valuable for credit scoring.
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Sun, Jia, and Yanrong Jiao. "Enterprise Financial Risk Analysis Based on Improved Model C-Means Clustering Algorithm." Security and Communication Networks 2022 (July 12, 2022): 1–12. http://dx.doi.org/10.1155/2022/1109813.
Повний текст джерелаАнотація:
As a provider of loans to SMEs, banks should prudently examine loan risks while ensuring that they provide loans to SMEs from the perspective of cooperating with policy implementation and controlling their own risks. The existing loan risk measurement tools include multiple discriminant analysis models, multiple regression models, and machine learning methods. Most machine learning methods have higher prediction accuracy than traditional models when using historical data for calculation, but the existence of problems such as overfitting seriously affects the robustness of machine learning methods. A similar method is introduced into the loan default risk prediction of SMEs, and the mean clustering method is used to preset penalty items to reduce overfitting and high accuracy to help banks effectively identify the default probability of SMEs during the loan period. This study will use the mean clustering method to iteratively train 900,000 SME credit records published by the US Small and Medium Business Administration, with 27 dimensions of data provided by Small Business Administration (SBA) to provide partial guarantees. A regression tree evaluates the data, combining the scores of multiple regression trees to produce a final prediction of the probability of credit default on the input data. The research results show that the mean clustering method can effectively improve the prediction accuracy of traditional machine learning methods and multiple linear regression in the scenario of SME loan default prediction and reduce the overfitting and black-box properties. As a supplementary loan default risk measurement tool, it can strengthen the ability of commercial banks to control the risk of loan business and can also promote the development of small- and medium-sized enterprises and the market economy to a certain extent.
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Foster, Benjamin P., Terry J. Ward, and Jon Woodroof. "An Analysis of the Usefulness of Debt Defaults and Going Concern Opinions in Bankruptcy Risk Assessment." Journal of Accounting, Auditing & Finance 13, no. 3 (July 1998): 351–71. http://dx.doi.org/10.1177/0148558x9801300311.
Повний текст джерелаАнотація:
This study extends the research of Hopwood et al. (1994) and Mutchler et al. (1997) by empirically investigating the relationships between loan defaults, violation of loan covenants, going-concern opinions, and bankruptcy in bankruptcy prediction models. One objective of this study is to empirically test the ability of loan defaults/accommodations and loan covenant violations to assess the risk of bankruptcy. Another objective of this study is to investigate the impact of failing to control for these two distress events on results from tests of the usefulness of going-concern opinions in assessing bankruptcy risk. Results suggest that loan default/accommodation and loan covenant violation are both significant explanatory variables of bankruptcy at the time of the last annual report before the event. While a going-concern opinion variable appears to significantly explain bankruptcy, it is not significant when included in a model with loan default/accommodation and covenant violation variables. Consequently, our results suggest that researchers should include both loan default/accommodation and covenant violation as control variables when using bankruptcy to test the usefulness of going-concern opinions.
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Wang, Qiuying. "Research on the Method of Predicting Consumer Financial Loan Default Based on the Big Data Model." Wireless Communications and Mobile Computing 2022 (March 24, 2022): 1–9. http://dx.doi.org/10.1155/2022/3786707.
Повний текст джерелаАнотація:
With the continuous advancement of Internet technology and the continuous development of big data model applications, the data model is a method of data organization and storage, which emphasizes the reasonable storage of data from the perspective of business, data access, and use. The rapid development of information technology on the Internet and the increasing consumption level of people have created conditions for the booming development of consumer finance. Compared with traditional lending methods, more and more consumers are more inclined to choose consumption channels such as the Internet and e-commerce. Consumer finance lending is more convenient and fast. The prediction of consumer financial loan default is very important, because the inaccuracy of the prediction not only leads to the loss of profits but also infringes the rights and interests of consumers. Therefore, it is very important to propose a default prediction method in the consumer finance field with good performance based on the big data model. Simulation experiment conclusions are shown as follows: (1) the pseudo R -squared value of the model is 0.3660, indicating that the control variable can better explain the change of y . (2) The chi-square test statistic is equal to 51632.31, the degree of freedom is 68, and the corresponding P < 0.0001 , which also shows that the entire model can significantly predict the change of y . (3) The regression coefficient of the number of loan performances is -0.207553, indicating that the number of consumer loans is negatively correlated with loan defaults. (4) The regression coefficient of the monthly loan frequency is 0.0500152, indicating that the customer applies the frequency of personal credit consumer loans which is positively correlated with loan defaults. (5) The accurate prediction ratio of the model is 86.11%, which further shows that the prediction model has a better effect.
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Kou, Gang, Yi Peng, and Chen Lu. "MCDM APPROACH TO EVALUATING BANK LOAN DEFAULT MODELS." Technological and Economic Development of Economy 20, no. 2 (June 27, 2014): 292–311. http://dx.doi.org/10.3846/20294913.2014.913275.
Повний текст джерелаАнотація:
Banks and financial institutions rely on loan default prediction models in credit risk management. An important yet challenging task in developing and applying default classification models is model evaluation and selection. This study proposes an evaluation approach for bank loan default classification models based on multiple criteria decision making (MCDM) methods. A large real-life Chinese bank loan dataset is used to validate the proposed approach. Specifically, a set of performance metrics is utilized to measure a selection of statistical and machine-learning default models. The technique for order preference by similarity to ideal solution (TOPSIS), a MCDM method, takes the performances of default classification models on multiple performance metrics as inputs to generate a ranking of default risk models. In addition, feature selection and sampling techniques are applied to the data pre-processing step to handle high dimensionality and class unbalancedness of bank loan default data. The results show that K-Nearest Neighbor algorithm has a good potential in bank loan default prediction.
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Lee, Jei Young. "Prediction of Default Risk in Peer-to-Peer Lending Using Structured and Unstructured Data." Journal of Financial Counseling and Planning 31, no. 1 (March 16, 2020): 115–29. http://dx.doi.org/10.1891/jfcp-18-00073.
Повний текст джерелаАнотація:
Using data from Lending Club, we analyzed funded loans between 2012 and 2013, the default status of which were mostly known in 2018. Our results showed that both the borrower characteristics and the conditions of the loan were significantly associated with the loan default rate. Results also showed that the sentiment of a user-written loan description influenced the borrower's loan interest rates. It contributes to expanding the scope of peer-to-peer (P2P) loan research by implementing unstructured data as a new model variable. Financial counselors need to consider the growth potential of the P2P loan market using data analysis: This will reveal niche market opportunities, enabling the development of services necessary for the safe supply of small loans at reasonable interest rates.
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Muslim, Much Aziz, Yosza Dasril, Muhammad Sam'an, and Yahya Nur Ifriza. "An improved light gradient boosting machine algorithm based on swarm algorithms for predicting loan default of peer-to-peer lending." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 2 (November 1, 2022): 1002. http://dx.doi.org/10.11591/ijeecs.v28.i2.pp1002-1011.
Повний текст джерелаАнотація:
Internet finance and big data technology are booming in the world. The launch of peer to peer (P2P) lending platforms is a sign and a great opportunity for entrepreneurs to easily increase their capital injection. However, this great opportunity has a high risk of impacting the sustainability and security development of the platform. One way to minimize loan risk is to predict the possibility of loan default. Hence, this study aims to find the best predictive model for predicting loan default of P2P Lending Club dataset. An improved light gradient boosting machine (LightGBM) via features selection by using swarm algorithms i.e. Ant colony optimization (ACO) and bee colony optimization (BCO) to the prediction analysis process. The best feature selection process is selected 6 out of 18 features. The synthetic minority oversampling technique (SMOTE) method is also provided to solve the unbalance class problem in the dataset, then a series of operations such as data cleaning and dimension reduction are performed. The experimental results prove that the LightGBM algorithm has been successfully improved. This success is shown by the prediction accuracy of LightGBM+ACO is 95.64%, LighGBM+BCO is 94.70% and LightGBM is 94.38%. This success also demonstrates outstanding performance in predicting loan default and strong generalizations.
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Shinde, Anant, Yash Patil, Ishan Kotian, Abhinav Shinde, and Reshma Gulwani. "Loan Prediction System Using Machine Learning." ITM Web of Conferences 44 (2022): 03019. http://dx.doi.org/10.1051/itmconf/20224403019.
Повний текст джерелаАнотація:
As the needs of people are increasing, the demand for loans in banks is also frequently getting higher every day. Banks typically process an applicant’s loan after screening and verifying the applicant’s eligibility, which is a difficult and time-consuming process. In some cases, some applicants default and banks lose capital. The machine learning approach is ideal for reducing human effort and effective decision making in the loan approval process by implementing machine learning tools that use classification algorithms to predict eligible loan applicants.
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Qu, Yanzhen, and Ihsan Said. "Improving the Performance of Loan Risk Prediction based on Machine Learning via Applying Deep Neural Networks." European Journal of Electrical Engineering and Computer Science 7, no. 1 (January 23, 2023): 31–37. http://dx.doi.org/10.24018/ejece.2023.7.1.475.
Повний текст джерелаАнотація:
Deep learning algorithms can be applied to data acquired regularly from consumers' financial activity in order to create predictive tools used to foresee credit card defaults. Methods such as Convolutional Neural Networks and Recurrent Neural networks were utilized to improve the performance of loan risk prediction, hence lowering the risk to financial institutions. To compare the two models, the same data set was used. With the goal of better modeling and moving their precise measures for gauging model performance, the same initial dataset was used. Predictions from deep learning algorithms are helpful in many different areas of study, including representation and abstraction that make sense of images, sound, and text. The study laid the groundwork for filling the void left by outdated banking tools and inadequate computer technology by applying deep learning algorithms to the problem of credit card default prediction.
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Aslam, Uzair, Hafiz Ilyas Tariq Aziz, Asim Sohail, and Nowshath Kadhar Batcha. "An Empirical Study on Loan Default Prediction Models." Journal of Computational and Theoretical Nanoscience 16, no. 8 (August 1, 2019): 3483–88. http://dx.doi.org/10.1166/jctn.2019.8312.
Повний текст джерелаАнотація:
Loan lending has been playing a significant role in the financial world throughout the years. Although it is quite profitable and beneficial for both the lenders and the borrowers. It does, however, carry a great risk, which in the domain of loan lending is referred to as Credit risk. Industry experts and Researchers around the world assign individuals with numerical scores known as credit scores to measure the risk and their creditworthiness. Throughout the years machine learning algorithms have been used to calculate and predict credit risk by evaluating an individual’s historical data. This study reviews the present literature on models predicting risk assessment that use machine learning algorithms.
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Hong, Zhaoyang, Wenxuan Deng, and Xiuyu Gong. "Prediction of Car Loan Default Results Based on Multi Model Fusion." Frontiers in Business, Economics and Management 5, no. 1 (September 8, 2022): 142–49. http://dx.doi.org/10.54097/fbem.v5i1.1515.
Повний текст джерелаАнотація:
With the prosperity and development of the asset management industry and various financial derivatives, many micro-loans and online loans have gradually entered the public view. How to predict the default probability of customer loans is a hot topic in the market. Therefore, in this paper, by collecting the data profile of more than 10 thousand car loan borrowers and fitting the fusion model of 4 methods: logistic model, decision model, Random Forest, and KNN model to the data, the author examines the behavioral data of borrowers to predict whether the borrowers will default in the future and find the best threshold to reach the lowest cost. The findings indicate that our final prediction can reduce costs by 38.9%. The excellent result shows that this model can be applied to the real market to help lending institutions predict default results and formulate strategies to avoid default risks in the process of borrowers' evaluation according to the model coefficient.
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Nemade, Suyog. "Loan Repayment Ability Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 4875–82. http://dx.doi.org/10.22214/ijraset.2022.45683.
Повний текст джерелаАнотація:
Abstract: Loan lending has risen quickly around the world in recent years. The fundamental goal of loan lending is to eliminate intermediaries such as banks. Loan lending is a fantastic option to apply for a loan for a small business or an individual who does not have enough credit or a credit history. However, the basic issue with loan lending is information asymmetry in this paradigm, which may not accurately predict lending default risk. Lenders solely decide whether or not to finance the loan based on the information supplied by borrowers, resulting in imbalanced datasets containing uneven completely paid and default loans. Unfortunately, the unbalanced data are hostile to traditional machine learning approaches. In our case, models with no adaptive strategies would concentrate on learning the standard payback. However, the minority class's characteristics are crucial in the lending sector. We use re-sampling and cost-sensitive procedures to analyse unbalanced datasets in this work, in addition to multiple machine learning schemes for forecasting the default risk of loan lending. Furthermore, we validate our suggested strategy using Lending Club datasets. The experiment findings suggest that our proposed technique may effectively improve default risk prediction accuracy.
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Chong, Pei Swee, Jane Labadin, and Farid Meziane. "Credit Risk Prediction for Peer-To-Peer Lending Platforms: An Explainable Machine Learning Approach." Journal of Computing and Social Informatics 1, no. 2 (September 19, 2022): 1–16. http://dx.doi.org/10.33736/jcsi.4761.2022.
Повний текст джерелаАнотація:
Small and medium enterprises face the challenge of obtaining start-up fund due to the strict rules and conditions set by banks and financial institutions. The plight yields to the growth in popularity of online peer-to-peer lending platforms which are an easier way to obtain loan as they have fewer rigid rules. However, high flexibility of loan funding in peer-to-peer lending comes with high default probability of loan funded to high-risk start-ups. An efficient model for evaluating credit risk of borrowers in peer-to-peer lending platforms is important to encourage investors to fund loans and justify the rejection of unsuccessful applications to satisfy financial regulators and increase transparency. This paper presents a supervised machine learning model with logistic regression to address this issue and predicts the probability of default of a loan funded to borrowers through peer-to-peer lending platforms. In addition, factors that affect the credit levels of borrowers are identified and discussed. The research shows that the most important features that affect probability of default are debt-to-income ratio, number of mortgage account, and Fair, Isaac and Company Score.
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Khedr, Mohamed H., Nesrine A. Azim, and Ammar M. Ammar. "A New Prediction Approach for Preventing Default Customers from Applying Personal Loans Using Machine Learning." International Journal of Computer Science and Mobile Computing 10, no. 12 (December 30, 2021): 71–82. http://dx.doi.org/10.47760/ijcsmc.2021.v10i12.009.
Повний текст джерелаАнотація:
In the Egyptian banking industry, loan officers use pure judgment to make personal loan approval decisions. In this paper, we develop a new predictive method for default customers' loans using machine learning. The new predictive method uses the available personal data and historical credit data to evaluate the credit trust-worthiness of customers to obtain loans. We used the ABE dataset for training and testing, as we used 10 features from the application form and i- score report class that could give great help to credit officers for taking the right decision through avoiding customer selection using random techniques. The collected dataset was analysed by using various machine learning classifiers based on important selected features, to obtain high accuracy. We compared the performance of several machine learning classifiers before and after feature selection. We have found that in terms of high accuracy, the most important features are (activity – income – loan) and in terms of better performance the decision tree classifier has surpassed any other machine learning classifier with significant prediction accuracy of almost 94.85%.
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Agbemava, Edinam, Israel Kofi Nyarko, Thomas Clarkson Adade, and Albert K. Bediako. "Logistic Regression Analysis Of Predictors Of Loan Defaults By Customers Of Non-Traditional Banks In Ghana." European Scientific Journal, ESJ 12, no. 1 (January 29, 2016): 175. http://dx.doi.org/10.19044/esj.2016.v12n1p175.
Повний текст джерелаАнотація:
The objective of this research is to identify the risk factors that influence loan defaults by customers in the microfinance sector and to develop a model that links these factors to credit default by customers in the sector. Data from a microfinance institution based in Accra Ghana was used. A binomial logistic regression analysis was fitted to a data of 548 customers who were granted credit from January 2013 to December 2014. The results of the study revealed that six factors: X3 (Marital Status); X7 (Dependents); X11 (Type of Collateral or Security); X13(Assessment); X15 (Duration); and X16 (Loan Type) were statistically significant in the prediction of loan default payment with a predicted default rate of 86.67%. It is therefore suggested that microfinance institutions adopt among others, the default risk model to ascertain the level of risk since it’s relatively efficient and cost effective. There should also be up to date training for loan officers of microfinance institutions in order to improve on their assessment skills and methodology. The supervising body of microfinance institutions (Bank of Ghana) should also consider enacting laws that will ensure that all such institutions in Ghana are roped into centralized database to check multiple borrowing and also serve as an internal control measure for the sustainability of these institutions.
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Nureni, Azeez A., and O. E. Adekola. "LOAN APPROVAL PREDICTION BASED ON MACHINE LEARNING APPROACH." FUDMA JOURNAL OF SCIENCES 6, no. 3 (June 24, 2022): 41–50. http://dx.doi.org/10.33003/fjs-2022-0603-830.
Повний текст джерелаАнотація:
Banks have various goods to sell in the banking system. The major source of income and profit, however, is their credit lines. As a result, they can profit from the interest on the loans they credit. The profit or loss of a bank is mostly determined by loans, that is, whether consumers repay the loan or default. The bank can lower its Non-Performing Assets by forecasting loan defaulters. Previous research in this age has revealed that there are numerous techniques for studying the subject of loan default control. However, because accurate forecasts are critical for profit maximization, it is critical to investigate the nature of the various methodologies and compare them. In this research, the datasets used were gathered from Kaggle for training and testing. The results gotten from both datasets were compared to ascertain which algorithm could best be used for predicting loan approval and also to determine which features are most important in predicting loan approval. The different metrics of performance that were used to define the results are: Accuracy, Precision, Recall and F1-Score. Eight different algorithms were used to train the models, these are: the Logistic Regression algorithm, Random forest, Decision trees, Linear Regression, Support Vector Machine (SVM), Naïve Bayes, K-means and K Nearest Neighbors (KNN) algorithms. The final results revealed that the models generated varied outcomes. From the results shown across both datasets, Logistic regression had - 83.24% and 78.13% of accuracy, followed by Naïve Bayes with 82.16% and 77.34% accuracy level, Random Forest
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Matenda, Frank Ranganai, and Mabutho Sibanda. "Determinants of Default Probability for Audited and Unaudited SMEs Under Stressed Conditions in Zimbabwe." Economies 10, no. 11 (November 4, 2022): 274. http://dx.doi.org/10.3390/economies10110274.
Повний текст джерелаАнотація:
Using stepwise logistic regression models, the study aims to separately detect and explain the determinants of default probability for unaudited and audited small-to-medium enterprises (SMEs) under stressed conditions in Zimbabwe. For effectiveness purposes, we use two separate datasets for unaudited and audited SMEs from an anonymous Zimbabwean commercial bank. The results of the paper indicate that the determinants of default probability for unaudited and audited SMEs are not identical. These determinants include financial ratios, firm and loan characteristics, and macroeconomic variables. Furthermore, we discover that the classification rates of SME default prediction models are enhanced by fusing financial ratios and firm and loan features with macroeconomic factors. The study highlights the vital contribution of macroeconomic factors in the prediction of SME default probability. We recommend that financial institutions model separately the default probability for audited and unaudited SMEs. Further, it is recommended that financial institutions should combine financial ratios and firm and loan characteristics with macroeconomic variables when designing default probability models for SMEs in order to augment their classification rates.
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Li, Xingyun, Daji Ergu, Di Zhang, Dafeng Qiu, Ying Cai, and Bo Ma. "Prediction of loan default based on multi-model fusion." Procedia Computer Science 199 (2022): 757–64. http://dx.doi.org/10.1016/j.procs.2022.01.094.
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Eweoya, I. O., A. A. Adebiyi, A. A. Azeta, and Olufunmilola Amosu. "Fraud prediction in loan default using support vector machine." Journal of Physics: Conference Series 1299 (August 2019): 012039. http://dx.doi.org/10.1088/1742-6596/1299/1/012039.
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Al-qerem, Ahmad, Ghazi Al-Naymat, Mays Alhasan, and Mutaz Al-Debei. "Default Prediction Model: The Significant Role of Data Engineering in the Quality of Outcomes." International Arab Journal of Information Technology 17, no. 4A (July 31, 2020): 635–44. http://dx.doi.org/10.34028/iajit/17/4a/8.
Повний текст джерелаАнотація:
For financial institutions and the banking industry, it is very crucial to have predictive models for their core financial activities, and especially those activities which play major roles in risk management. Predicting loan default is one of the critical issues that banks and financial institutions focus on, as huge revenue loss could be prevented by predicting customer’s ability not only to pay back, but also to be able to do that on time. Customer loan default prediction is a task of proactively identifying customers who are most probably to stop paying back their loans. This is usually done by dynamically analyzing customers’ relevant information and behaviors. This is significant so as the bank or the financial institution can estimate the borrowers’ risk. Many different machine learning classification models and algorithms have been used to predict customers’ ability to pay back loans. In this paper, three different classification methods (Naïve Bayes, Decision Tree, and Random Forest) are used for prediction, comprehensive different pre-processing techniques are being applied on the dataset in order to gain better data through fixing some of the main data issues like missing values and imbalanced data, and three different feature extractions algorithms are used to enhance the accuracy and the performance. Results of the competing models were varied after applying data preprocessing techniques and features selections. The results were compared using F1 accuracy measure. The best model achieved an improvement of about 40%, whilst the least performing model achieved an improvement of 3% only. This implies the significance and importance of data engineering (e.g., data preprocessing techniques and features selections) course of action in machine learning exercises
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Attigeri, Girija, Manohara Pai M M, and Radhika M. Pai. "Framework to predict NPA/Willful defaults in corporate loans: a big data approach." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (October 1, 2019): 3786. http://dx.doi.org/10.11591/ijece.v9i5.pp3786-3797.
Повний текст джерелаАнотація:
Growth and development of the economy is dependent on the banking system. Bad loans which are Non-Performing Assets (NPA) are the measure for assessing the financial health of the bank. It is very important to control NPA as it affects the profitability, and deteriorates the quality of assets of the bank. It is observed that there is a significant rise in the number of willful defaulters. Hence systematic identification, awareness and assessment of parameters is essential for early prediction of willful default behavior. The main objective of the paper is to identify exhaustive list of parameters essential for predicting whether the loan will become NPA and thereby willful default. This process includes understanding of existing system to check NPAs and identifying the critical parameters. Also propose a framework for NPA/Willful default identification. The framework classifies the data comprising of structured and unstructured parameters as NPA/Willful default or not. In order to select the best classification model in the framework an experimentation is conducted on loan dataset on big data platform. Since the loan data is structured, unstructured component is incorporated by generating synthetic data. The results indicate that neural network model gives best accuracy and hence considered in the framework.
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Tariq, Hafiz Ilyas, Asim Sohail, Uzair Aslam, and Nowshath Kadhar Batcha. "Loan Default Prediction Model Using Sample, Explore, Modify, Model, and Assess (SEMMA)." Journal of Computational and Theoretical Nanoscience 16, no. 8 (August 1, 2019): 3489–503. http://dx.doi.org/10.1166/jctn.2019.8313.
Повний текст джерелаАнотація:
The purpose of this study is to provide a comprehensive research and to develop a model to predict the loan defaults. This kind of models becomes inevitable as the issue of bad loans are very much critical in the financial sector especially in micro financing banks of various underdeveloped and developed countries. To cope up with this problem a comprehensive literature review was done to study the significant factors that leads to this issue. Moreover, these reviewed studies were critically focused towards applying data mining techniques for the prediction and classification of the loan defaults. This study used methodologies named KDD, CRISP-DM and SEMMA. While in the experimentation phase, three different data mining techniques were applied for the proposed model and their performances were evaluated on various parameters. Based on these parameters, the best method was selected, explained and suggested because of its significant characteristics regarding the prediction of the loan defaults in the financial sector.
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Zurada, Jozef, and Martin Zurada. "How Secure Are Good Loans: Validating Loan-Granting Decisions And Predicting Default Rates On Consumer Loans." Review of Business Information Systems (RBIS) 6, no. 3 (July 1, 2002): 65–84. http://dx.doi.org/10.19030/rbis.v6i3.4563.
Повний текст джерелаАнотація:
The failure or success of the banking industry depends largely on the industrys ability to properly evaluate credit risk. In the consumer-lending context, the banks goal is to maximize income by issuing as many good loans to consumers as possible while avoiding losses associated with bad loans. Mistakes could severely affect profits because the losses associated with one bad loan may undermine the income earned on many good loans. Therefore banks carefully evaluate the financial status of each customer as well as their credit worthiness and weigh them against the banks internal loan-granting policies. Recognizing that even a small improvement in credit scoring accuracy translates into significant future savings, the banking industry and the scientific community have been employing various machine learning and traditional statistical techniques to improve credit risk prediction accuracy.This paper examines historical data from consumer loans issued by a financial institution to individuals that the financial institution deemed to be qualified customers. The data consists of the financial attributes of each customer and includes a mixture of loans that the customers paid off and defaulted upon. The paper uses three different data mining techniques (decision trees, neural networks, logit regression) and the ensemble model, which combines the three techniques, to predict whether a particular customer defaulted or paid off his/her loan. The paper then compares the effectiveness of each technique and analyzes the risk of default inherent in each loan and group of loans. The data mining classification techniques and analysis can enable banks to more precisely classify consumers into various credit risk groups. Knowing what risk group a consumer falls into would allow a bank to fine tune its lending policies by recognizing high risk groups of consumers to whom loans should not be issued, and identifying safer loans that should be issued, on terms commensurate with the risk of default.
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Krichene, Aida. "Using a naive Bayesian classifier methodology for loan risk assessment." Journal of Economics, Finance and Administrative Science 22, no. 42 (June 12, 2017): 3–24. http://dx.doi.org/10.1108/jefas-02-2017-0039.
Повний текст джерелаАнотація:
Purpose Loan default risk or credit risk evaluation is important to financial institutions which provide loans to businesses and individuals. Loans carry the risk of being defaulted. To understand the risk levels of credit users (corporations and individuals), credit providers (bankers) normally collect vast amounts of information on borrowers. Statistical predictive analytic techniques can be used to analyse or to determine the risk levels involved in loans. This paper aims to address the question of default prediction of short-term loans for a Tunisian commercial bank. Design/methodology/approach The authors have used a database of 924 files of credits granted to industrial Tunisian companies by a commercial bank in the years 2003, 2004, 2005 and 2006. The naive Bayesian classifier algorithm was used, and the results show that the good classification rate is of the order of 63.85 per cent. The default probability is explained by the variables measuring working capital, leverage, solvency, profitability and cash flow indicators. Findings The results of the validation test show that the good classification rate is of the order of 58.66 per cent; nevertheless, the error types I and II remain relatively high at 42.42 and 40.47 per cent, respectively. A receiver operating characteristic curve is plotted to evaluate the performance of the model. The result shows that the area under the curve criterion is of the order of 69 per cent. Originality/value The paper highlights the fact that the Tunisian central bank obliged all commercial banks to conduct a survey study to collect qualitative data for better credit notation of the borrowers.
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Ying, Li. "Research on bank credit default prediction based on data mining algorithm." International Journal of Social Sciences and Humanities Invention 5, no. 6 (June 28, 2018): 4820–23. http://dx.doi.org/10.18535/ijsshi/v5i6.09.
Повний текст джерелаАнотація:
It is of great importance to identify the potential risks to the bank's loan customers. Based on data mining technology, it is an effective method to classify loan customers by classification algorithm. In this paper, we use Random Forest method, Logistic Regression method, SVM method and other suitable classification algorithms by python to study and analyze the bank credit data set, and compared these models on five model effect evaluation statistics of Accuracy, Recall, precision, F1-score and ROC area. This paper use the data mining classification algorithm to identify the risk customers from a large number of customers to provide an effective basis for the bank's loan approval.
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陈, 旭岚. "Corporate Loan Default Risk Prediction Based on Machine Learning Method." Modeling and Simulation 10, no. 03 (2021): 890–97. http://dx.doi.org/10.12677/mos.2021.103088.
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Eweoya, I. O., A. A. Adebiyi, A. A. Azeta, F. Chidozie, F. O. Agono, and B. Guembe. "A Naive Bayes approach to fraud prediction in loan default." Journal of Physics: Conference Series 1299 (August 2019): 012038. http://dx.doi.org/10.1088/1742-6596/1299/1/012038.
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Alejandrino, Jovanne C., Jovito Jr P. Bolacoy, and John Vianne Bauya Murcia. "Supervised and unsupervised data mining approaches in loan default prediction." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (April 1, 2023): 1837. http://dx.doi.org/10.11591/ijece.v13i2.pp1837-1847.
Повний текст джерелаАнотація:
Given the paramount importance of data mining in organizations and the possible contribution of a data-driven customer classification recommender systems for loan-extending financial institutions, the study applied supervised and supervised data mining approaches to derive the best classifier of loan default. A total of 900 instances with determined attributes and class labels were used for the training and cross-validation processes while prediction used 100 new instances without class labels. In the training phase, J48 with confidence factor of 50% attained the highest classification accuracy (76.85%), <em>k</em>-nearest neighbors (<em>k</em>-NN) 3 the highest (78.38%) in IBk variants, naïve Bayes has a classification accuracy of 76.65%, and logistic has 77.31% classification accuracy. <em>k</em>-NN 3 and logistic have the highest classification accuracy, F-measures, and kappa statistics. Implementation of these algorithms to the test set yielded 48 non-defaulters and 52 defaulters for <em>k</em> -NN 3 while 44 non-defaulters and 56 defaulters under logistic. Implications were discussed in the paper.
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Nguyen, Cuong, and Liang Chen. "Comparing Data Mining Models in Loan Default Prediction: A Framework and a Demonstration." Journal of Information Technology and Computer Science 7, no. 1 (April 7, 2022): 1–8. http://dx.doi.org/10.25126/jitecs.202271352.
Повний текст джерелаАнотація:
In the banking sector, credit risk assessment is an important process to ensure that loans could be paid on time, and that banks could maintain their credit performance effectively. Despite restless business efforts allocated to credit scoring yearly, high percentage of loan defaulting remains a major issue. With the availability of tremendous banking data and advanced analytics tools, data mining algorithms can be applied to develop a platform of credit scoring, and to resolve the loan defaulting problem. This paper puts forward a framework to compare four classification algorithms, including logistic regression, decision tree, neural network, and Xgboost, using a public dataset. Confusion matrix and Monte Carlo simulation benchmarks are used to evaluate their performance. We find that the XGboost outperforms the other three traditional models. We also offer practial recommendation and future research.
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Liao, Jingxian, Wei Wang, Jason Xue, Anthony Lei, Xue Han, and Kun Lu. "Combating Sampling Bias: A Self-Training Method in Credit Risk Models." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 12566–72. http://dx.doi.org/10.1609/aaai.v36i11.21528.
Повний текст джерелаАнотація:
A significant challenge in credit risk models for underwriting is the presence of bias in model training data. When most credit risk models are built using only applicants who had been funded for credit, such non-random sampling predominantly influenced by credit policymakers and previous loan performances may introduce sampling bias to the models, and thus alter their prediction of default on loan repayment when screening applications from prospective borrowers. In this paper, we propose a novel data augmentation method that aims to identify and pseudo-label parts of the historically declined loan applications to mitigate sampling bias in the training data. We also introduce a new measure to assess the performance from the business perspective, loan application approval rates at various loan default rate levels. Our proposed methods were compared to the original supervised learning model and the traditional sampling issue remedy techniques in the industry. The experiment and early production results from deployed model show that self-training method with calibrated probability as data augmentation selection criteria improved the ability of credit scoring to differentiate default loan applications and, more importantly, can increase loan approval rate up to 8.8\%, while keeping similar default rate comparing to baselines. The results demonstrate practical implications on how future underwriting model development processes should follow.
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Chen, Herui. "Prediction and Analysis of Financial Default Loan Behavior Based on Machine Learning Model." Computational Intelligence and Neuroscience 2022 (September 20, 2022): 1–10. http://dx.doi.org/10.1155/2022/7907210.
Повний текст джерелаАнотація:
In recent years, the increase of customer loan risk and the aggravation of the epidemic have led to the increase of customer default risk. Therefore, identifying high-risk customers has become an important research hotspot for banks. The customer’s credit is the standard to evaluate the loan amount and interest rate, and the ability to quickly identify customer information has become a research hotspot. Based on the bank credit application scenario, this paper realizes function extraction and data processing for customer basic attribute data and download transaction data. Then, a linear regression model with penalty and a neural network prediction model are proposed to improve the accuracy of bankruptcy assessment and achieve local optimization. In this way, the implicit risk prediction and control of customer credit are improved, and the default risk of bank loans is significantly reduced. According to the characteristics of the collected sample data, the most appropriate penalty linear regression prediction algorithm is selected and the experimental analysis is carried out to improve the risk management level of banks. The experimental results show that the improved logistic regression and neural network model has obvious advantages in the prediction effect for four models.
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Matenda, Frank Ranganai, Mabutho Sibanda, Eriyoti Chikodza, and Victor Gumbo. "Corporate Loan Recovery Rates under Downturn Conditions in a Developing Economy: Evidence from Zimbabwe." Risks 10, no. 10 (October 17, 2022): 198. http://dx.doi.org/10.3390/risks10100198.
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In this study, we design stepwise ordinary least squares regression models using various amalgamations of firm features, loan characteristics and macroeconomic variables to forecast workout recovery rates for defaulted bank loans for private non-financial corporates under downturn conditions in Zimbabwe. Our principal aim is to identify and interpret the determinants of recovery rates for private firm defaulted bank loans. For suitability and efficacy purposes, we adopt a unique real-life data set of defaulted bank loans for private non-financial firms pooled from a major anonymous Zimbabwean commercial bank. Our empirical results show that the firm size, the collateral value, the exposure at default, the earnings before interest and tax/total assets ratio, the length of the workout process, the total debt/total assets ratio, the ratio of (current assets–current liabilities)/total assets, the inflation rate, the interest rate and the real gross domestic product growth rate are the significant determinants of RRs for Zimbabwean private non-financial firm bank loans. We reveal that accounting information is useful in examining recovery rates for defaulted bank loans for private corporations under distressed financial and economic conditions. Moreover, we discover that the prediction results of recovery rate models are augmented by fusing firm features and loan characteristics with macroeconomic factors.
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Ganbat, Mandukhai, Erdenebileg Batbaatar, Ganzul Bazarragchaa, Togtuunaa Ider, Enkhjargalan Gantumur, Lkhamsuren Dashkhorol, Khosgarig Altantsatsralt, Mandakhbayar Nemekh, Erdenebaatar Dashdondog, and Oyun-Erdene Namsrai. "Effect of Psychological Factors on Credit Risk: A Case Study of the Microlending Service in Mongolia." Behavioral Sciences 11, no. 4 (April 5, 2021): 47. http://dx.doi.org/10.3390/bs11040047.
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This paper determined the predefining factors of loan repayment behavior based on psychological and behavioral economics theories. The purpose of this research is to identify whether an individual’s credit risk can be predicted based on psychometric tests measuring areas of psychological factors such as effective economic decision-making, self-control, conscientiousness, selflessness and a giving attitude, neuroticism, and attitude toward money. In addition, we compared the psychological indicators to the financial indicators, and different age and gender groups, to assess whether the former can predict loan default prospects. This research covered the psychometric test results, financial information, and loan default information of 1118 borrowers from loan-issuing applications on mobile phones. We validated the questionnaire using confirmatory factor analysis (CFA) and achieved an overall Cronbach’s alpha reliability coefficient greater than 0.90 (α = 0.937). We applied the empirical data to construct prediction models using logistic regression. Logistic regression was employed to estimate the parameters of a logistic model. The outcome indicates that positive results from the psychometric testing of effective financial decision-making, self-control, conscientiousness, selflessness and a giving attitude, and attitude toward money enable individuals’ debt access possibilities. On the other hand, one of the variables—neuroticism—was determined to be insignificant. Finally, the model only used psychological variables proven to have significant default predictability, and psychological variables and psychometric credit scoring offer the best prediction capacities.
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Netzer, Oded, Alain Lemaire, and Michal Herzenstein. "When Words Sweat: Identifying Signals for Loan Default in the Text of Loan Applications." Journal of Marketing Research 56, no. 6 (August 15, 2019): 960–80. http://dx.doi.org/10.1177/0022243719852959.
Повний текст джерелаАнотація:
The authors present empirical evidence that borrowers, consciously or not, leave traces of their intentions, circumstances, and personality traits in the text they write when applying for a loan. This textual information has a substantial and significant ability to predict whether borrowers will pay back the loan above and beyond the financial and demographic variables commonly used in models predicting default. The authors use text-mining and machine learning tools to automatically process and analyze the raw text in over 120,000 loan requests from Prosper, an online crowdfunding platform. Including in the predictive model the textual information in the loan significantly helps predict loan default and can have substantial financial implications. The authors find that loan requests written by defaulting borrowers are more likely to include words related to their family, mentions of God, the borrower’s financial and general hardship, pleading lenders for help, and short-term-focused words. The authors further observe that defaulting loan requests are written in a manner consistent with the writing styles of extroverts and liars.
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Madaan, Mehul, Aniket Kumar, Chirag Keshri, Rachna Jain, and Preeti Nagrath. "Loan default prediction using decision trees and random forest: A comparative study." IOP Conference Series: Materials Science and Engineering 1022 (January 19, 2021): 012042. http://dx.doi.org/10.1088/1757-899x/1022/1/012042.
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Adewusi, Amos Olaolu, Tunbosun Biodun Oyedokun, and Mustapha Oyewole Bello. "Application of artificial neural network to loan recovery prediction." International Journal of Housing Markets and Analysis 9, no. 2 (June 6, 2016): 222–38. http://dx.doi.org/10.1108/ijhma-01-2015-0003.
Повний текст джерелаАнотація:
Purpose This study assesses the classification accuracy of an artificial neural network (ANN) model. It examines the application of loan recovery probability rather than odds of default as the case with traditional credit evaluation models. Design/methodology/approach Data on 2,300 loans granted over the period 2001-2012 was obtained from the databases of Nigerian commercial banks and primary mortgage institutions. A multilayer feed-forward ANN model with back-propagation learning algorithm was developed having classified the sample into training (38 per cent), testing (41 per cent) and validation (21 per cent) sub-samples. Findings The model exhibits a high overall percentage classification accuracy of 92.6 per cent. It also achieves relatively low misclassification Type I and Type II errors at 6.5 per cent and 8.2 per cent, respectively. Macroeconomic variables such as gross domestic product, inflation and interest rates have the strongest influence on the ANN model classification power. The result of the analysis shows that adopting odds of recovery in ANN classification models can lead to improved loan evaluation. Originality/value The paper is distinct from extant studies in that it presents a new dimension to loan evaluation in Nigerian lending market. To the best knowledge of the authors, the paper is among the first to explore probability of loan recovery as the basis for credit evaluation in the country.
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M.I., Omogbhemhe, and Momodu I.B.A. "Model for Predicting Bank Loan Default using XGBoost." International Journal of Computer Applications 183, no. 32 (October 16, 2021): 1–4. http://dx.doi.org/10.5120/ijca2021921705.
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Aslam, Mohammad, Senthil Kumar, and Shahryar Sorooshian. "Predicting Likelihood for Loan Default Among Bank Borrowers." International Journal of Financial Research 11, no. 1 (October 10, 2019): 318. http://dx.doi.org/10.5430/ijfr.v11n1p318.
Повний текст джерелаАнотація:
Poverty is a threat to the world. In its extreme form at any part of the world, it will make endanger rest of the world. In fact, it is the source of crime and the worst form of violence. The poor people do not commit any crime but they get punishment out of being born as a poor that is not controllable in their hand. Microfinance has been designed to eliminate poverty and help marginal and poor people through small income generating activities. The borrowers need capital to materialize their dream, may be in a small amount and microfinance can play important role in this scenario. Through microfinance, small entrepreneurs may acquire necessary inputs to start their business. Both local governments and international agencies are trying to eliminate poverty through microfinance programs, services and guidelines. With this concept, Microfinance has been hosted primarily in Bangladesh. Grameen Bank (GB) has been serving large number of people below poverty level in Bangladesh. However, impact of microfinance is still questionable in several studies. Microfinance used properly and returned back to the lender with stipulated amount and time shows its working effectively for poverty alleviation. Otherwise, there must be loan default and the whole system may be in question. We survey with questionnaire to find out factors contributing to loan default among GB borrowers using binomial logistic regression. The results showed that some factors were crucial for loan default and should be treated properly at the start of lending.
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Choudhary, Ali M., and Anil K. Jain. "Corporate stress and bank nonperforming loans: Evidence from Pakistan." International Finance Discussion Paper 2021, no. 1327 (August 20, 2021): 1–32. http://dx.doi.org/10.17016/ifdp.2021.1327.
Повний текст джерелаАнотація:
Using detailed administrative Pakistani credit registry data, we show that banks with low leverage ratios are both significantly slower and less likely to recognize a loan as nonperforming than other banks that lend to the same firm. Moreover, we find suggestive evidence that this lack of recognition impedes loan curing, with banks with low leverage ratios reporting significantly higher final default rates than other banks for the same borrower (even after controlling for differences in loan terms). Our empirical findings are consistent with the theoretical prediction that classifying a nonperforming loan is more expensive for banks with less capital.
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Baklouti, Ibtissem. "On the role of loan officers’ psychological traits in predicting microcredit default accuracy." Qualitative Research in Financial Markets 7, no. 3 (August 3, 2015): 264–89. http://dx.doi.org/10.1108/qrfm-04-2014-0011.
Повний текст джерелаАнотація:
Purpose – The present paper’s aim lies in providing an empirical analysis of whether the loan officers’ psychological traits display an explanation of their subjective prediction accuracy. Design/methodology/approach – A qualitative and qualitative analysis has also been applied. Findings – The reached results reveal that, with respect to microfinance institutions, the loan officers’ accurate subjective judgment crucially relies on the principle of learning-through-experience so as to construct a special type of relevant skills and competences. Learning is both an intellectual and an emotional process, whereby loan officers acquire certain specific experience likely to enhance their cognitive skills and shape their emotional intelligence, which would, in turn, sharpen their forecasting accuracy. In fact, the higher emotional intelligence is, the easier it makes it for loan officer to adjust or reduce their judgmental errors and make a more effective application of the pertinent heuristics. Conversely, however, the lack or absence of emotions and feelings of novice loan officer is likely to hinder and inhibit the cognitive as well as the learning processes. Originality/value – The paper considers the role of individual psychological traits on the decisions of experienced and inexperienced individuals when deciding on the default risk in the context of loan decisions. Learning is both an intellectual and an emotional process, whereby loan officers acquire certain specific experience likely to enhance their cognitive skills and shape their emotional intelligence, which would, in turn, sharpen their forecasting accuracy.