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Journal articles on the topic 'Default Probability Prediction'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Tserng, H. Ping, Po-Cheng Chen, Wen-Haw Huang, Man Cheng Lei, and Quang Hung Tran. "PREDICTION OF DEFAULT PROBABILITY FOR CONSTRUCTION FIRMS USING THE LOGIT MODEL." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 20, no. 2 (2014): 247–55. http://dx.doi.org/10.3846/13923730.2013.801886.

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Recently, the high incidence of construction firm bankruptcies has underlined the importance of forecasting defaults in the construction industry. Early warning systems need to be developed to prevent or avert contractor default; additionally, this evaluation result could facilitate the selection of firms as collaboration or investment partners. Financial statements are considered one of the key basic evaluation tools for demonstrating firm strength. This investigation provides a framework for assessing the probability of construction contractor default based on financial ratios by using the L

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Amzile, Karim, and Mohamed Habachi. "Assessment of Support Vector Machine performance for default prediction and credit rating." Banks and Bank Systems 17, no. 1 (2022): 161–75. http://dx.doi.org/10.21511/bbs.17(1).2022.14.

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Predicting the creditworthiness of bank customers is a major concern for banking institutions, as modeling the probability of default is a key focus of the Basel regulations. Practitioners propose different default modeling techniques such as linear discriminant analysis, logistic regression, Bayesian approach, and artificial intelligence techniques. The performance of the default prediction is evaluated by the Receiver Operating Characteristic (ROC) curve using three types of kernels, namely, the polynomial kernel, the linear kernel and the Gaussian kernel. To justify the performance of the m

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3

Zhang, Zixuan. "Credit Card Default Prediction based on Machine Learning Techniques." BCP Business & Management 44 (April 27, 2023): 779–85. http://dx.doi.org/10.54691/bcpbm.v44i.4954.

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In recent years, with the development of society and economy, credit cards have been popularized due to their low interest rate and easy payment. However, with the advent of the epidemic era, the unemployment rate has increased, making the probability of credit card defaults rising. The prediction of credit card default helps banks and financial institutions balance the risk and economic interests, contributes to the stable and healthy development of the financial industry, and plays an important role in bank credit control. Therefore, this paper addresses the credit card default prediction pr

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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

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5

Ma, Yuhan. "Prediction of Default Probability of Credit-Card Bills." Open Journal of Business and Management 08, no. 01 (2020): 231–44. http://dx.doi.org/10.4236/ojbm.2020.81014.

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Pribadi, Firman, and Susanto Susanto. "Merton model as predictor of failure probability of public banks in Indonesia." Journal of Economics, Business & Accountancy Ventura 17, no. 3 (2015): 393. http://dx.doi.org/10.14414/jebav.v17i3.361.

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This research attempts to use Black-Schole-Merton (BSM) model based on market approach to predict default probability of publishing bank in Indonesia. This is done by using stock prices and financial report. In this effort, this study estimates the neutral risk and default probability for the publish bank. The result showed that option model can predict default status more with accurate event long before default information was published for public. It can be studied from the case of Bank Century that has been imposed as a failure bank, in which it is known as bailout bank by the Indonesian go

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Wang, Zhao, Cuiqing Jiang, and Huimin Zhao. "Depicting Risk Profile over Time: A Novel Multiperiod Loan Default Prediction Approach." MIS Quarterly 47, no. 4 (2023): 1455–86. http://dx.doi.org/10.25300/misq/2022/17491.

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With the rapid development of fintech, the need for dynamic credit risk evaluation is becoming increasingly important. While previous studies on credit scoring have mostly focused on single-period loan default prediction, we call for a new avenue—multiperiod default prediction (MPDP)—to depict risk profiles over time. To address the challenges raised by MPDP, such as monotonic default probability prediction and complex relationship accommodation, we propose a novel approach, hybrid and collective scoring (HACS). We design a hybrid modeling strategy to predict whether and when a borrower will d

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8

Matenda, Frank Ranganai, and Mabutho Sibanda. "Determinants of Default Probability for Audited and Unaudited SMEs Under Stressed Conditions in Zimbabwe." Economies 10, no. 11 (2022): 274. http://dx.doi.org/10.3390/economies10110274.

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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. F

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9

Sharma, Meena, and Sakshi Khurana. "Impact of Effective Generic Business Strategies on Probability of Default: A Study of NSE Listed Firms." NMIMS Management Review 33, no. 1 (2025): 29–39. https://doi.org/10.1177/09711023251323380.

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The aim of the current study is to examine the impact of generic business strategies (GBS) on default risk for Indian firms listed on the National Stock Exchange. This study employs a distance-to-default model to compute default risk and GBS, which are measured through Porter’s framework. The relationship between GBS and the probability of default is derived through panel data regression analysis for the period 2008–2009 to 2021–2022. The results support the hypothesis and show a positive relationship between cost leadership and the distance-to-default model. The findings indicate that compani

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10

GRIGOROVICH, Anastasiya V., Dmitrii V. GRIGOROVICH, and Vadim M. KOZHANOV. "Logit model for predicting the probability of default for Russian small and medium-sized enterprises." Finance and Credit 30, no. 2 (2024): 390–417. http://dx.doi.org/10.24891/fc.30.2.390.

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Subject. This article deals with the issues of determining the financial stability of small and medium-sized enterprises (SMEs) in terms of the probability of future default. Objectives. The article aims to determine the most accurate model for predicting the probability of default of Russian SMEs, as well as develop a new model based on an up-to-date database of financial indicators obtained from open sources of information. Methods. For the study, we used logistic regression and analysis. Results. The article presents a developed six-factor logit model for estimating the probability of defau

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Zhao, Yi, Yanyan Shen, and Yong Huang. "DMDP: A Dynamic Multi-source Default Probability Prediction Framework." Data Science and Engineering 4, no. 1 (2019): 3–13. http://dx.doi.org/10.1007/s41019-019-0085-9.

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12

Wu, Deming, and Suning Zhang. "Debt Market Liquidity and Corporate Default Prediction." Quarterly Journal of Finance 04, no. 04 (2014): 1550003. http://dx.doi.org/10.1142/s2010139215500032.

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Recent research on the subprime crisis and rollover risk suggests that debt market liquidity is a major factor affecting the risk of default. This implies that firms that rely heavily on short-term debt, such as commercial paper (CP), are at greater risk of default. Debt market illiquidity could reduce the value of the firm and thus impact the firm's leverage, which is a major factor in predicting default. We estimate the effect of debt market conditions on the probability of default with a discrete-time dynamic hazard model that takes into account measurement error in firm leverage. Our resul

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Gupta, Vandana. "An Empirical Analysis of Default Prediction Models: Evidence from lndian Listed Companies." Journal of Prediction Markets 8, no. 3 (2015): 1–23. http://dx.doi.org/10.5750/jpm.v8i3.946.

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This paper attempts to evaluate the predictive ability of three default prediction models: the market-based KMV model, the Z-score model using discriminant analysis (DA), and the logit model; and identifies the key default drivers. The research extends prior empirical work by modeling and testing the impact of financial ratios, macro-economic factors, corporate governance and firm-specific variables in predicting default. For the market-based model, the author has extended the works of KMV in developing a suitable algorithm for determining probability of default (PD). While for the KMV model,

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Godfrey, Ehikioya Oyamienlen. "Comparative Analysis of the Reduced form Model and the Structural Model in Credit Risk Modelling." Journal of Economics, Finance And Management Studies 07, no. 05 (2024): 3039–42. https://doi.org/10.5281/zenodo.11408866.

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Credit risk models are statistical tools to infer the future default probabilities and loss distribution of values of a portfolio of debts. Credit risk modelling is prevalent in today’s financial decision-making process. It turns out that both models of modelling credit risk contribute to explaining the default risk of listed firms, however, reduce-form model outperformances the structural model. Structural models are used to calculate the probability of default for a firm based on the value of assets and liabilities. The basic idea is that a company (with limited liability) defaults if

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15

Klepáč, Václav. "Default Probability Prediction with Static Merton-D-Vine Copula Model." European Journal of Business Science and Technology 1, no. 2 (2015): 104–13. http://dx.doi.org/10.11118/ejobsat.v1i2.30.

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16

Faraj, Azhi Abdalmohammed, Didam Ahmed Mahmud, and Bilal Najmaddin Rashid. "Comparison of Different Ensemble Methods in Credit Card Default Prediction." UHD Journal of Science and Technology 5, no. 2 (2021): 20–25. http://dx.doi.org/10.21928/uhdjst.v5n2y2021.pp20-25.

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Credit card defaults pause a business-critical threat in banking systems thus prompt detection of defaulters is a crucial and challenging research problem. Machine learning algorithms must deal with a heavily skewed dataset since the ratio of defaulters to non-defaulters is very small. The purpose of this research is to apply different ensemble methods and compare their performance in detecting the probability of defaults customer’s credit card default payments in Taiwan from the UCI Machine learning repository. This is done on both the original skewed dataset and then on balanced dataset seve

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Park, Sunghwa, Hyunsok Kim, Janghan Kwon, and Taeil Kim. "Empirics of Korean Shipping Companies’ Default Predictions." Risks 9, no. 9 (2021): 159. http://dx.doi.org/10.3390/risks9090159.

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In this paper, we use a logit model to predict the probability of default for Korean shipping companies. We explore numerous financial ratios to find predictors of a shipping firm’s failure and construct four default prediction models. The results suggest that a model with industry specific indicators outperforms other models in predictive ability. This finding indicates that utilizing information about unique financial characteristics of the shipping industry may enhance the performance of default prediction models. Given the importance of the shipping industry in the Korean economy, this stu

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Krasovytskyi, Danylo, and Andriy Stavytskyy. "Predicting Mortgage Loan Defaults Using Machine Learning Techniques." Ekonomika 103, no. 2 (2024): 140–60. http://dx.doi.org/10.15388/ekon.2024.103.2.8.

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Mortgage default prediction is always on the table for financial institutions. Banks are interested in provision planning, while regulators monitor systemic risk, which this sector may possess. This research is focused on predicting defaults on a one-year horizon using data from the Ukrainian credit registry applying machine-learning methods. This research is useful for not only academia but also policymakers since it helps to assess the need for implementation of macroprudential instruments. We tested two data balancing techniques: weighting the original sample and synthetic minority oversamp

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19

Lisboa, Inês, Magali Costa, and Beatriz Vouga. "Determinants of default prediction of the tourism sector: the case of Portuguese SMEs." European Journal of Tourism Research 40 (June 7, 2025): 4001. https://doi.org/10.54055/ejtr.v40i.3484.

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This study intends to understand the determinants of default of tourism SMEs (Small and Medium Enterprises), i.e., which aspects impact the probability that the company will not comply with its financial obligations. For this purpose, a panel data composed from a sample of 3,945 Portuguese SMEs, over ten years, was analysed. An ex-ante criterion (based on a set of financial ratios) was used to classify firms in default or compliant. This criterion helps to detect financial problems early. Then, in addition to the firm’s specific characteristics, which are the most used determinants, governance

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20

Dalsania, Naman, Devang Punatar, and Deep Kothari. "Credit Card Approval Prediction using Classification Algorithms." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 507–14. http://dx.doi.org/10.22214/ijraset.2022.47369.

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Abstract: Credit risk as the boards in banks basically revolves around determining the probability of default or the creditworthiness of a customer, collapse, and the cost, assuming it happens. It is important to consider key factors and anticipate the likelihood of consumer default, given the circumstances. This is where machine learning models come into play. This allows banks and large financial institutions to predict whether their customers will default on their loans. This project uses Python to create machine-learning models with the highest possible accuracy. First, we load the dataset

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Rychnovský, Michal. "SURVIVAL ANALYSIS AS A TOOL FOR BETTER PROBABILITY OF DEFAULT PREDICTION." Acta Oeconomica Pragensia 26, no. 1 (2018): 34–46. http://dx.doi.org/10.18267/j.aop.594.

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22

Ogundimu, Emmanuel O. "Prediction of default probability by using statistical models for rare events." Journal of the Royal Statistical Society: Series A (Statistics in Society) 182, no. 4 (2019): 1143–62. http://dx.doi.org/10.1111/rssa.12467.

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23

Xiao, Yaoxin. "The Predictive Power of Credit Scores: Examining Default Probability in Taiwanese Credit Card Clients." Advances in Economics, Management and Political Sciences 42, no. 1 (2023): 139–47. http://dx.doi.org/10.54254/2754-1169/42/20232097.

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The concept of a scorecard originated from the need to establish a standardized and objective approach to evaluate credit applicants. Various techniques have been utilized to build scoring model. This research chooses Logistic regression to construct a scorecard using SPSS modeler. In this way, the study enhances the understanding of the relationship between credit scores and default behavior. By using a scorecard constructed through logistic regression, the study provides a comprehensive and interpretable model for evaluating creditworthiness. The study also employs linear probability models

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Costa, Magali, Inês Lisboa, and Ana Gameiro. "Is the Financial Report Quality Important in the Default Prediction? SME Portuguese Construction Sector Evidence." Risks 10, no. 5 (2022): 98. http://dx.doi.org/10.3390/risks10050098.

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This work analyses whether financial information quality is relevant to explaining firms’ probability of default. A financial default prediction model for SMEs (Small and Medium Enterprises) is presented, which includes not only traditional measures but also financial reporting quality (FRQ) measures. FRQ influences the decision-making due to its impact on financial information, which has repercussions on the accounting ratios’ informativeness. A panel data of 1560 Portuguese SMEs in the construction sector, from 2012 to 2018, is analysed. First, firms are classified as default or compliant us

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Shinde, Gaurav, Shreyash Pawar, Rohit Albhar, Avaneesh Yadav, and Mrs Priyanka Patil. "Home-Credit Risk Analysis and Prediction Modelling using Python." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 1614–23. http://dx.doi.org/10.22214/ijraset.2022.42610.

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Abstract: Financial Companies or firms try to determine if an individual or organization is worth lending specified amount of credit without any risk to its investors. If deemed eligible for it, they try to determine the risk associated (Probability of Default) with it. This Study compares Extreme gradient Boosting, Support Vector Machine, Naïve Bayesian, and Random Forest techniques for predicting the target variable efficiently with different strategies. This study tries to determine the risk using the person's assets, income, and various other parameters. Here, we are trying to calculate th

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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.

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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 meth

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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

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Glushenko, A. A., and A. V. Kulikov. "MARKOV REGIME-SWITCHING MODEL DEVELOPMENT FOR NUMBER OF DEFAULTS OF INVESTMENT AND SPECULATIVE GRADE ANALYSIS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 215 (May 2022): 26–35. http://dx.doi.org/10.14489/vkit.2022.05.pp.026-035.

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In this paper the Markov regime-switching model was constructed for the main classes of credit ratings – speculative and investment. Estimates of the model parameters were obtained based on statistics on credit ratings and the number of defaults from the annual reports of S&P Global. The VIX volatility index was taken into account for a more accurate prediction of the probability of default. A similar model was constructed for the average annual value of VIX. Also a combined regime-switching model was considered, taking into account both statistics on the number of defaults of the speculat

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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 (2022): 1–16. http://dx.doi.org/10.33736/jcsi.4761.2022.

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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

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Ciampi, Francesco. "The Need for Specific Modelling of Small Enterprise Default Prediction: Empirical Evidence from Italian Small Manufacturing Firms." International Journal of Business and Management 12, no. 12 (2017): 251. http://dx.doi.org/10.5539/ijbm.v12n12p251.

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The existing literature has proved the effectiveness of financial ratios for company default prediction modelling. However, such researches rarely focus on small enterprises (SEs) as specific units of analysis. The aim of this paper is to demonstrate that SE default prediction should be modelled separately from that of large and medium-sized firms. In fact, a multivariate discriminant analysis was applied to a sample of 2,200 small manufacturing firms located in Central Italy and a SE default prediction model was developed based on a selected group of financial ratios and specifically construc

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Aniska, Meiliawati, Di Asih I Maruddani, and Suparti Suparti. "Valuasi One Period Coupon Bond dengan Aset Mengikuti Model Geometric Brownian Motion with Jump Diffusion." Indonesian Journal of Applied Statistics 3, no. 2 (2021): 94. http://dx.doi.org/10.13057/ijas.v3i2.43149.

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<p>One period coupon bond gives coupon once a bond life together with the principal debt. If the firm’s asset value on maturity date is insufficient to meet the debtholder’s claim, then the firm is stated as default. The well-known model for predicting default probability is KMV-Merton model. Under this model, it is assumed that the return on the firm’s assets is distributed normally and their behaviour can be described with the Geometric Brownian Motion (GBM) formula. In practice, most of the financial data tend to have heavy-tailed distribution. It indicates that the data contain some

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Zhanjiang Li, Xueting Ren, and Hua Tao. "Optimal Prediction Model of Default Probability Based on Multiple Machine Learning Methods." Automatic Control and Computer Sciences 59, no. 1 (2025): 116–25. https://doi.org/10.3103/s0146411625700105.

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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 (2022): 142–49. http://dx.doi.org/10.54097/fbem.v5i1.1515.

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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

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Abid, Amira, and Fathi Abid. "Sovereign Credit Risk in Saudi Arabia, Morocco and Egypt." Journal of Risk and Financial Management 17, no. 7 (2024): 283. http://dx.doi.org/10.3390/jrfm17070283.

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The purpose of this paper is to assess and predict sovereign credit risk for Egypt, Morroco and Saudi Arabia using credit default swap (CDS) spreads obtained from the DataStream database for the period from 2009 to 2022. Our approach consists of generating the implied default probability and the corresponding credit rating in order to estimate the term structure of the implied default probability using the Nelson–Siegel model. In order to validate the prediction from the probability term structure, we calculate the transition matrices based on the implied rating using the homogeneous Markov mo

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Ding, A. Adam, Shaonan Tian, Yan Yu, and Hui Guo. "A Class of Discrete Transformation Survival Models With Application to Default Probability Prediction." Journal of the American Statistical Association 107, no. 499 (2012): 990–1003. http://dx.doi.org/10.1080/01621459.2012.682806.

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Chang, Wen-Tien, Chao-Hung Yu, and Chau-Jung Kuo. "A Prediction of the Probability of Default of SMEs on the Credit Guarantee Schemes." Journal of Statistics and Management Systems 16, no. 2-03 (2013): 109–35. http://dx.doi.org/10.1080/09720510.2013.777573.

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Hong, Bo, Xingsheng Xie, and Haoming Guo. "Research on probability of default prediction based on loan company's credit fund trading behaviours." International Journal of Simulation and Process Modelling 9, no. 4 (2014): 240. http://dx.doi.org/10.1504/ijspm.2014.066368.

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Huang, Yu‐Lin. "Prediction of contractor default probability using structural models of credit risk: an empirical investigation." Construction Management and Economics 27, no. 6 (2009): 581–96. http://dx.doi.org/10.1080/01446190902960474.

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Gabbi, Giampaolo, Daniele Tonini, and Michele Russo. "A Novel Supervised-Unsupervised Approach for Past-Due Prediction." Risk Management Magazine 19, no. 2 (2024): 4–21. http://dx.doi.org/10.47473/2020rmm0141.

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In the current landscape of banking and financial services, a primary concern for industry practitioners revolves around predicting the probability of default (PD) and categorizing raw data into risk classes. This study addresses the challenge of predicting payment past-due for customers of Residential Mortgage-Based Securities (RMBS) and Small and Medium Enterprises (SMEs) within the Italian banking sector, employing an innovative approach that integrates a classification model (Random Forest) with an anomalies detection technique (Isolation Forest). The models are trained on a substantial da

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Rahman, Mahfuzur, Cheong Li Sa, and Md Abdul Kaium Masud. "Predicting Firms’ Financial Distress: An Empirical Analysis Using the F-Score Model." Journal of Risk and Financial Management 14, no. 5 (2021): 199. http://dx.doi.org/10.3390/jrfm14050199.

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Financial performance of firms is very important to bankers, shareholders, potential investors, and creditors. The inability of firms to meet their liabilities will affect all its stakeholders and will result in negative consequences in the wider economy. The objective of the study is to explore the applicability of a distress prediction model which uses the F-Score and its components to identify firms which are at high risk of going into default. The study incorporates a prediction model and vast literature to address the research questions. The sample of the study is collected from publicly

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Cenciarelli, Velia Gabriella, Giulio Greco, and Marco Allegrini. "Does intellectual capital help predict bankruptcy?" Journal of Intellectual Capital 19, no. 2 (2018): 321–37. http://dx.doi.org/10.1108/jic-03-2017-0047.

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Purpose The purpose of this paper is to explore whether intellectual capital affects the probability that a particular firm will default. The authors also test whether including intellectual capital performance in bankruptcy prediction models improves their predictive ability. Design/methodology/approach Using a sample of US public companies from the period stretching from 1985 to 2015, the authors test whether intellectual capital performance reduces the probability of bankruptcy. The authors use the VAIC as an aggregate measure of corporate intellectual capital performance. Findings The find

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Apte, Mohit. "Refining Credit Risk Analysis- Integrating Bayesian MCMC with Hamiltonian Monte Carlo." International Journal of Innovative Research in Computer Science and Technology 12, no. 4 (2024): 88–91. http://dx.doi.org/10.55524/ijircst.2024.12.4.14.

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The accurate prediction of loan defaults is paramount for financial institutions to enhance decision-making processes, optimize loan approval rates, and mitigate associated risks. This study develops a predictive model utilizing Bayesian Markov Chain Monte Carlo (MCMC) techniques to forecast potential loan defaults. Employing a comprehensive dataset of 255,000 borrower profiles, which include detailed borrower characteristics and loan information, the model integrates advanced statistical methods to assess and interpret the factors influencing loan defaults. The Bayesian framework allows for r

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Yin, Menglin, and Gushuo Li. "Supply Chain Financial Default Risk Early Warning System Based on Particle Swarm Optimization Algorithm." Mathematical Problems in Engineering 2022 (April 20, 2022): 1–7. http://dx.doi.org/10.1155/2022/7255967.

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With the advancement of the linkage between financial markets, the probability of credit risk infection is also increasing. Traditional financing methods, which mostly relied on corporate credit to give credit to the whole supply chain, have been replaced by supply chain finance. This paper studies the supply chain financial credit risk through the logistic model and chooses the financial data and supply chain financial operation indicators of relevant listed companies from 2014 to 2016 for analysis. Because not all of companies can find the bad debt rate of accounts receivable from 2014 to 20

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Podhorska, Ivana, and Maria Misankova. "Success of Prediction Models in Slovak Companies." GATR Global Journal of Business Social Sciences Review 4, no. 4 (2016): 54–59. http://dx.doi.org/10.35609/gjbssr.2016.4.4(6).

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Objective The issue of bankrupt of company is very actual topic not only in Slovakia but also in abroad. The reason is that many companies have problem with the question of their probability of default or bankrupt and also with their financial health as a whole. This paper deals with the issue of prediction models and captures the applicability of these models in the Slovak conditions. Methodology/Technique In this paper are applied eight selected prediction models in the sample of 74 companies from Slovak Republic. In addition, this paper calculated one financial ratio from the category of co

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Stefano, Olgiati, and Danovi Alessandro. "ZETA™ Methodology and Variation in the Systemic Risk of Default: Accounting for the Effects of Type II (False Negative) Errors Variation on Lending." Journal of Corporate Finance Research / Корпоративные Финансы | ISSN: 2073-0438 9, no. 1 (2015): 71–81. http://dx.doi.org/10.17323/j.jcfr.2073-0438.9.1.2015.71-81.

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Olgiati Stefano - University of Bergamo, Department of Management, Economics and Quantitative Methods.  Danovi Alessandro - University of Bergamo The loan manager - dealing with one single borrower at a time and being responsible for that single decision to lend - is exposed to the idiosyncratic risk of default of his customer just like the physician is exposed to the risk of a wrong diagnosis with our strep throat. At the same time – if we do not expect the strep throat diagnostic test kit to change - we would still expect that physician reading that test to become more careful –

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Peela, Harsha Vardhan, Tanuj Gupta, Nishit Rathod, Tushar Bose, and Neha Sharma. "Prediction of Credit Card Approval." International Journal of Soft Computing and Engineering 11, no. 2 (2022): 1–6. http://dx.doi.org/10.35940/ijsce.b3535.0111222.

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Credit risk as the board in banks basically centers around deciding the probability of a customer's default or credit decay and how expensive it will end up being assuming it happens. It is important to consider major factors and predict beforehand the probability of consumers defaulting given their conditions. Which is where a machine learning model comes in handy and allows the banks and major financial institutions to predict whether the customer, they are giving the loan to, will default or not. This project builds a machine learning model with the best accuracy possible using python. Firs

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Harsha, Vardhan Peela, Gupta Tanuj, Rathod Nishit, Bose Tushar, and Sharma Neha. "Prediction of Credit Card Approval." International Journal of Soft Computing and Engineering (IJSCE) 11, no. 2 (2022): 1–6. https://doi.org/10.35940/ijsce.B3535.0111222.

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Credit risk as the board in banks basically centers around deciding the probability of a customer's default or credit decay and how expensive it will end up being assuming it happens. It is important to consider major factors and predict beforehand the probability of consumers defaulting given their conditions. Which is where a machine learning model comes in handy and allows the banks and major financial institutions to predict whether the customer, they are giving the loan to, will default or not. This project builds a machine learning model with the best accuracy possible using python.

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Karas, Michal. "The Hazard Model for European SMEs: Combining Accounting and Macroeconomic Variables." Journal of Competitiveness 14, no. 3 (2022): 76–92. http://dx.doi.org/10.7441/joc.2022.03.05.

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Predicting the default of small and medium-sized businesses (SMEs) using the hazard model approach represents an area relatively neglected by mainstream literature. On the one hand, SMEs are regarded as the backbone of the economy; on the other hand, their specific features pose a challenge to the modelling process. This issue is further complicated by the fact that many modern structural approaches to default modelling are simply unsuitable for SMEs due to their limited size. Therefore, researchers only rely on accounting, non-financial, or macroeconomic data. The gap is especially noticeable

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Watson-Daniels, Jamelle, David C. Parkes, and Berk Ustun. "Predictive Multiplicity in Probabilistic Classification." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 10306–14. http://dx.doi.org/10.1609/aaai.v37i9.26227.

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Machine learning models are often used to inform real world risk assessment tasks: predicting consumer default risk, predicting whether a person suffers from a serious illness, or predicting a person's risk to appear in court. Given multiple models that perform almost equally well for a prediction task, to what extent do predictions vary across these models? If predictions are relatively consistent for similar models, then the standard approach of choosing the model that optimizes a penalized loss suffices. But what if predictions vary significantly for similar models? In machine learning, thi

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万, 婷婷. "Analysis of Credit Card Customer Default Probability Prediction Using Least Squares Ramp Loss Geometric NHSVM." E-Commerce Letters 13, no. 04 (2024): 2756–66. http://dx.doi.org/10.12677/ecl.2024.1341454.

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