To see the other types of publications on this topic, follow the link: Default Probability Prediction.
Journal articles on the topic 'Default Probability Prediction'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Default Probability Prediction.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
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.
Full textAbstract:
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 Logit model. A total of 21 ratios, gathered into five financial groups, are utilized to perform univariate logit analysis and multivariate logit analysis for assessing contractor default probability. The empirical results indicate that using multivariate analysis by adding market factor to the liquidity, leverage, activity and profitability factors can increase the accuracy of default prediction more than using only four financial factors. While considering the market factor in the multivariate Logit model, clear incremental prediction performance appears in 1-year evaluation. This study thus suggests that the market factor comprises important information to increase the prediction performance of the model when applied to construction contractors, particularly in short-term evaluation.
2
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.
Full textAbstract:
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 model, the study compares the prediction of default by the support vector with the logistic regression using data from a portfolio of particular bank customers. The results of this study showed that the model based on the Support Vector Machine approach with the Radial Basis Function kernel, performs better in prediction, compared to the logistic regression model, with a value of the ROC curve equal to 98%, against 71.7% for the logistic regression model. Also, this paper presents the conception of a support vector machine-based rating tool designed to classify bank customers and determine their probability of default. This probability has been computed empirically and represents the proportion of defaulting customers in each class.
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.
Full textAbstract:
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 problem by using Random forest, Decision tree, LightGBM, XGBoost, Logistic regression, and Adaboost models to make predictions and compare the results. The outcomes demonstrate that LightGBM algorithm has the most outstanding prediction score, and its AUC value can reach 0.78 and recall rate reaches 0.95.
4
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.
Full textAbstract:
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.
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.
Full text
6
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.
Full textAbstract:
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 government. The model does not only provide the ordinal ranking for the bank sample but also the good early warning prediction for the public. The probability estimation based on the option model can be an innovative model to measure and manage credit risk on the future for predicting probability default in Indonesia.
7
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.
Full textAbstract:
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 default separately through a default discrimination model and a default time estimation model, respectively, and synthesize them through a probabilistic framework. To accommodate various possible patterns of default time and measure the distribution of default probability over successive time intervals, we propose a joint default modeling method to train the default time estimation model. Empirical evaluations at the model (time-to-default prediction performance and discrimination performance) and mechanism (identifiability and discriminability) levels, as well as impact analyses at the application (granting performance and profitability performance) level, show that HACS outperforms the benchmarked survival analysis and multilabel learning methods on all fronts. It can more accurately predict time-to-default and provide financial institutions and investors better decision-support in granting loans and selecting loan portfolios.
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.
Full textAbstract:
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.
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.
Full textAbstract:
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 companies employing a cost leadership strategy tend to have sound financial health, which makes them less prone to default risk. The results show practical implications for financial institutions, creditors, and investors who can check the strategic positioning of a firm before evaluating and arriving at the credit rating, lending, and investment decisions. Based on the findings of this study, investors can provide funds to invest in a firm that implements GBS effectively to ensure high investment returns and fewer credit defaults. The study is expected to contribute to default risk literature by providing evidence that GBS indicators can be experimented for incorporation into new default risk prediction models.
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.
Full textAbstract:
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 default a year before its occurrence, which has shown the highest accuracy of prediction in comparison with the models available in the literature. Relevance. The developed logit model may be of interest to a wide range of users wishing to invest in Russian SMEs due to its wide analytical base, high prediction accuracy, and ease of use and interpretation of results.
11
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.
Full text
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.
Full textAbstract:
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 results indicate that rollover risk is a significant factor in causing default, but the risk was higher for nonfinancial firms around 2000–2001 and considerably less entering the subprime crisis.
13
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.
Full textAbstract:
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, the continuous observations of PD are used as the dependent variable, for the accounting-based models, ratings assigned are the proxy for default (those rated ’D’ are defaulted and rated ‘AAA’ and ‘A’ are solvent). The research findings largely support the hypothesis that solvency, profitability and liquidity ratios do impact the default risk, but adding other covariates improves the predictive ability of the models. Through this study, the author recommends that accounting –based models and market based models are conceptually different. While market-based models are forward looking and inclusion of market data makes the default risk quantifiable; to make the PD more exhaustive, it is important to factor in the information provided in the financial statements. The conclusions drawn are that the disclosures in financial statements can help predict default risk as financial distress risk is likely to evolve over time and will be reflected in financial statements beyond accounting ratios. Moreover this will also help divulge “creative accounting” practices by corporates.
14
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.
Full textAbstract:
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 the value of its assets is less than the debt of the company. The causal driver of defaults in structural model will choose to work with variables that help us explain what causes defaults. Default risk is endogenous in the structural model, this is so because the factors that causes defaults within a path are predictable. The structural model is an economic model with focus on options pricing, call option and put option. It provides clarity about the nature of defaults and how the various economic features that are chosen to relate with each other when defaults occur. The reduced form model is mostly concerned with prediction of when does defaults occurs? Default risk is exogenous to the reduced form model, can be caused by random events and most often comes as a surprise. Statistical models are used to observe the variables and help maximise the reduced form model. The empirical result suggests that reduce-form model can better predict the firm’s default risk.
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.
Full text
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.
Full textAbstract:
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 several studies have showed the superiority of neural networks as compared to traditional machine learning algorithms, the results of our study show that ensemble methods consistently outperform Neural Networks and other machine learning algorithms in terms of F1 score and area under receiver operating characteristic curve regardless of balancing the dataset or ignoring the imbalance
17
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.
Full textAbstract:
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 study can benefit both policymakers and market participants.
18
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.
Full textAbstract:
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 oversampling technique and compared the results. It was found that random forest and extreme gradient-boosting decision trees are better classifiers regarding both accuracy and precision. These models provided an essential balance between actual default precision and minimizing false defaults. We also tested neural networks, linear discriminant analysis, support vector machines with linear kernels, and decision trees, but they showed similar results to logistic regression. The result suggested that real gross domestic product (GDP) growth and debt-service-to-income ratio (DSTI) were good predictors of default. This means that a realistic GDP forecast as well as a proper assessment of the borrower’s DSTI through the loan history can predict default on a one-year horizon. Adding other variables such as the borrower’s age and loan interest rate can also be beneficial. However, the residual maturity of mortgage loans does not contribute to default probability, which means that banks should treat both new borrowers equally and those who nearly repaid the loan.
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.
Full textAbstract:
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 variables and macroeconomic factors were analysed in the firm's default prediction logit model. Results prove that the three groups of determinants are relevant to explain firms’ financial difficulties probability. The proposed model presents a success rate (predictive ability to classify as compliant and default) of around 80%. Furthermore, as a test of the robustness of the results, the sample period was divided into two subperiods (2010 to 2014 and 2015 to 2019) with different investment rates in the sector, allowing the conclusion that what determines the default of SMEs in the Portuguese tourism sector depends on the period analysed.
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.
Full textAbstract:
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 and take a glimpse. The data set is a combination of mathematical and non-mathematical elements, with various ranges of values and some missing points. We pre-process the dataset so that the selected ML model meets high expectations.
21
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.
Full text
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.
Full text
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.
Full textAbstract:
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 (LPM), logit, and probit models to assess the predictive power of credit scores on default probability. By utilizing these statistical techniques, the research presents robust empirical evidence on the significance of credit scores in predicting default behavior. Moreover, the research paper systematically analyzes prediction effects with and without control variables. By incorporating control variables such as demographic characteristics, the study adds depth to the understanding of scoring models. Overall, the findings provide valuable guidance for credit risk assessment practices and contribute to the ongoing development of effective credit evaluation frameworks.
24
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.
Full textAbstract:
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 using an ex-ante criterion which allows us to identify signs of financial constraints in advance. Then, the stepwise method is employed to identify which variables are more relevant to explain the default probability. Results show that FRQ measures, namely accruals quality and timeliness, impact firms’ defaulting, supporting their relevance in predicting financial difficulties. Finally, using a logit approach, the accuracy of the model increased when FRQ variables were included. Results are confirmed using “new age” classifiers, namely the random forest methodology. This work is not only relevant to the extant financial distress literature but has also relevant implications for practice since stakeholders can understand the impact of financial reporting quality to prevent additional risks.
25
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.
Full textAbstract:
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 the home-credit risk factors using various parameters and compare various methods to try and determine which is more efficient and precise. Keywords: Probability of default, Credit Risk Analysis, Extreme gradient Boosting, Support Vector Machine, Naïve Bayesian, Random Forest
26
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.
Full textAbstract:
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.
27
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.
Full textAbstract:
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.
28
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.
Full textAbstract:
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 speculative and investment grade and statistics on the average annual value of the VIX index for the period from 1990 to 2019. 10 parameters were estimated using the maximum likelihood method: the probability of the onset of a crisis and the probability of overcoming it; the probability of default in a crisis and non-crisis year for counterparties of the investment and speculative rating classes; the mathematical expectation and standard deviation of the VIX value in a crisis and non-crisis year. Two-stage backtesting of these models was carried out based on the obtained values of the parameter estimates. The first step was to check if the number of defaults falls between the quantiles of a mixed distribution consisting of a mixture of two binomials for the number of defaults and a mixture of two Gaussians for the VIX value. The second step was to check whether the number of misses in the first step falls within the two-sided confidence intervals of the binomial distribution. Finally, the number of defaults in 2020 and 2021 was forecasted and a comparison was made of the forecast for 2020 with the real numbers of defaults from the S&P Global report 2020.
29
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.
Full textAbstract:
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.
30
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.
Full textAbstract:
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 constructed to capture the specificities of SEs’ risk profiles. Subsequently, the prediction accuracy rates obtained by this model were compared with those obtained from a second model based on a sample of 3,200 manufacturing firms situated in Central Italy which belong to all dimensional classes. The findings are the following: 1) evaluating the probability of default of SEs separately from that of larger firms improves prediction performance; 2) the predictive power of the discriminant function improves if it takes into account the different profiles of firms operating in different industry sectors; 3) this improvement is much greater for SEs compared to larger firms.
31
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.
Full textAbstract:
<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 extreme values. GBM with Jump is a popular model to capture the extreme values. In this paper, we evaluate a corporate bond which has some extreme condition in their asset value and predicts the default probability in the maturity date. Empirical studies were carried out on bond that is issued by CIMB Niaga Bank that has a payment due in November 2020. The result shows that modelling the asset value is more appropriate by using GBM with Jump rather than GBM modelling. Estimation to CIMB Niaga Bank equity in November 2020 is IDR 246,533,573,844,229.00. The liability of this company is IDR 4,205,751,155,771.00. The prediction of CIMB Niaga Bank default probability is 1.065812 ´ 10<sup>-8</sup> at the bond maturity. It indicates that the company is considered capable of fulfilling the obligations at the maturity date.</p><p><strong>Keywords: </strong>jump diffusion, extreme value, probability default, equity, liability</p>
32
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.
Full text
33
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.
Full textAbstract:
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.
34
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.
Full textAbstract:
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 model. The main results show that, overall, the probabilities of defaulting in the long term are higher than those in the short term, which implies that the future outlook is more pessimistic given the events that occurred during the study period. Egypt seems to be the country with the most fragile economy, especially after 2009, likely because of the political events that marked the country at that time. The economies of Morocco and Saudi Arabia are more resilient in terms of both default probability and credit rating. These findings can help policymakers develop targeted strategies to mitigate economic risks and enhance stability, and they provide investors with valuable insights for managing long-term investment risks in these countries.
35
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.
Full text
36
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.
Full text
37
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.
Full text
38
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.
Full text
39
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.
Full textAbstract:
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 dataset comprising performing loans from the 2020-2022 period. Notably, this research stands out not only for its novel modeling approach but also for its focus on the arrear status of RMBS and SME customers as the target variable. By concentrating on past-due rather than the broader concept of probability of default, this approach enhances understanding of customers' financial stress levels, enabling proactive monitoring and intervention by decision-makers. The ultimate aim of this experimentation is to develop a robust and effective algorithm applicable in real-world scenarios for predicting the likelihood of past-due among individual customers and companies, thereby supporting management decision-making processes. Empirical results demonstrate that the proposed framework surpasses conventional statistical and machine learning algorithms in credit risk modeling, exhibiting robust performance on new data (validated against 2023 data) and thus proving its operational suitability.
40
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.
Full textAbstract:
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 listed firms of the United States. In total, 81 financially distressed firms wereextracted from the UCLA-LoPucki Bankruptcy Research Database during 2009–2017. This study found that the relationship of the F-Score and probability of firms going into financial distress is significant. This study also demonstrated that firms which are at risk of distress tend to record a negative cash flow from operations (CFO) and showed a greater decline in return on assets (ROA) in the year prior to default. This study extends the existing literature by supporting a model which has not been widely used in the area of financial distress predictions.
41
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.
Full textAbstract:
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 findings show that the intellectual capital performance is negatively associated with the probability of default. The findings also indicate that the bankruptcy prediction models that include intellectual capital have a superior predictive ability over the standard models. Research limitations/implications This paper contributes to prior research on intellectual capital and firm performance. To the best of the knowledge, this is the first study to show that the benefits of intellectual capital extend from superior performance to long-term financial stability. The research can also contribute to bankruptcy studies. By using a time frame covering decades, the findings suggest that intellectual capital performance measures can be included in bankruptcy prediction models and can effectively complement traditional performance measures. Originality/value This paper highlights that intellectual capital is associated with long-term financial stability and a lower bankruptcy risk. Firms realising the potential of their intellectual capital can produce a virtuous circle between higher performance and greater financial stability.
42
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.
Full textAbstract:
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 robust uncertainty quantification and model complexity management, making it particularly suitable for the nuanced nature of credit risk assessment. Results from the model demonstrate a compelling accuracy rate, substantially aligning with industry benchmarks while providing deeper insights into the probability of default as influenced by various borrower attributes. This research underscores the efficacy of Bayesian MCMC modelling in financial risk management and offers a scalable approach for financial institutions aiming to refine their credit evaluation strategies.
43
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.
Full textAbstract:
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 2016, and some agricultural listed companies only have one or two years of relevant data, this paper creates an unbalanced panel data with 91 sample sizes, which is larger than previous studies. Binary logistic regression and principal component analysis are mainly used to accurately calculate the compliance probability of cooperative customers in agricultural supply chain financial products. Unlike the existing literature, which mainly uses s.t to define whether an enterprise defaults, this paper uses Z value to define the default risk of listed companies in agricultural supply chain finance. In terms of the default risk value of the company, Z value not only has high accurate value but also has advantages in accurate prediction, which effectively complements and improves the existing research on supply chain finance.
44
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).
Full textAbstract:
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 company´s indebtedness. Based on this calculation is done the comparison between results of predictions models and results of indebtedness financial ratio. Findings They tested eight different prediction models and their findings present that best results were achieved by Fulmer, Poznanski and Zmijewski model. Weak results achieved IN05, CH-index and Sharita model. Novelty : This paper provides explanatory ability and success of individual prediction models in Slovak conditions. Type of Paper: Review Keywords: Prediction Models; Financial Health; Bankrupt; Non-Bankrupt; Indebtedness Financial Ratio.
45
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.
Full textAbstract:
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 – or update his prior beliefs – about his diagnoses when a flu epidemic is likely to kick in with a certain estimated probability (likelihood). However, this has not been the case with loan management - there is in fact some consensus that before 2007 a reduction in the standards of idiosyncratic risk assessment by lenders - prior to risks pooling - coupled with a worsening of the systemic risk scenario, is partly to blame for the well known 2007-2008 financial crisis, with some of the blame falling also on the incapacity of actuarial mathematical models (test kits) to update worst case scenarios or be calibrated continuously on the basis of variation in the likelihood of default of the underlying risks pool.The authors of this paper argue that, on the other hand, a standard Bayesian transformation of the ZETA bankruptcy prediction methodology introduced by Altman in 1968-1977 allows for a continuous a posterioriupdate of conditional Type I and II errors due to variation in the systemic likelihood of default. The Bayesian transformation can be used both to condition the loan manager’s prior decision (generally based on Basel II-compliant Internal Rating Based system or Credit Agency’s Rating) and to update such decision on the basis of any posterior hypothesis (based on actuarial frequentist assumptions of conditional hazard rates) regarding the creditworthiness and the probability of default of an underlying pool of securities.At the same time – under a Bayesian framework - the ZETA diagnostic test can be conditioned on the new evidence introduced by other tests to increase the total sensitivity of the default prediction models (IRB ratings, TTC ratings, logit, probit, neural) to update the commercial bank’s lending decisions.A ground-state, static meta-analysis of Altman’s et al. ZETA original article (1977) reveals that the odds of the commercial bank detecting a default after the ZETA score has been introduced (post-test) is 13.2 times more effective than the a priori prediction. Under the same assumptions, the odds of the commercial bank detecting a survival after (post-test) the ZETA score has been introduced is 12.2 times more effective than the a priori. Integration of the ZETA model with other default prediction models reaches a credibility interval of CI ≥ 95% when the updated likelihood of default is equal to 60%. As expected, the Efficiency Comparison Test ECZETA=.00243 is invariant under the Bayesian transformation.
46
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.
Full textAbstract:
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. First we load and view the dataset. The dataset has a combination of both mathematical and non-mathematical elements, that it contains values from various reaches, in addition to that it contains a few missing passages. We preprocess the dataset to guarantee the AI model we pick can make great expectations. After the information is looking great, some exploratory information examination is done to assemble our instincts. Finally, we will build a machine learning model that can predict if an individual's application for a credit card will be accepted. Using various tools and techniques we then try to improve the accuracy of the model. This project uses Jupyter notebook for python programming to build the machine learning model. Using Data Analysis and Machine Learning, we attempted to determine the most essential parameters for obtaining credit card acceptance in this project. The machine learning model we built gave an 86 % accuracy for predicting whether the credit card will be approved or not, considering the various factors mentioned in the application of the credit card holder. Even though we achieved an accuracy of 86%, we conducted a grid search to see if we could increase the performance even further. However, using both the machine learning models: random forest and logistic regression, the best we could get from this data was 86 percent.
47
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.
Full textAbstract:
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. First we load and view the dataset. The dataset has a combination of both mathematical and non-mathematical elements, that it contains values from various reaches, in addition to that it contains a few missing passages. We preprocess the dataset to guarantee the AI model we pick can make great expectations. After the information is looking great, some exploratory information examination is done to assemble our instincts. Finally, we will build a machine learning model that can predict if an individual's application for a credit card will be accepted. Using various tools and techniques we then try to improve the accuracy of the model. This project uses Jupyter notebook for python programming to build the machine learning model. Using Data Analysis and Machine Learning, we attempted to determine the most essential parameters for obtaining credit card acceptance in this project. The machine learning model we built gave an 86 % accuracy for predicting whether the credit card will be approved or not, considering the various factors mentioned in the application of the credit card holder. Even though we achieved an accuracy of 86%, we conducted a grid search to see if we could increase the performance even further. However, using both the machine learning models: random forest and logistic regression, the best we could get from this data was 86 percent.
48
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.
Full textAbstract:
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, this is referred to as predictive multiplicity i.e. the prevalence of conflicting predictions assigned by near-optimal competing models. In this paper, we present a framework for measuring predictive multiplicity in probabilistic classification (predicting the probability of a positive outcome). We introduce measures that capture the variation in risk estimates over the set of competing models, and develop optimization-based methods to compute these measures efficiently and reliably for convex empirical risk minimization problems. We demonstrate the incidence and prevalence of predictive multiplicity in real-world tasks. Further, we provide insight into how predictive multiplicity arises by analyzing the relationship between predictive multiplicity and data set characteristics (outliers, separability, and majority-minority structure). Our results emphasize the need to report predictive multiplicity more widely.
49
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.
Full textAbstract:
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 in several studies on SME default prediction that employ the hazard model approach, which models the probability of default with respect to the time factor. A better understanding of the factors driving SMEs’ default might help in adopting policies that strengthen their competitiveness. The aim of this study is to introduce a hazard model for EU-28 SMEs and analyse the contribution of macroeconomic indicators and proxies of external financial obstacle factors. This model was derived using the Cox semiparametric proportional model, leaving the baseline hazard unspecified and employing macroeconomic variables as explanatory variables. By analysing a sample of 202,209 European SMEs over the period 2014–2019, the results indicated that factors of employment rate, personal cost per employee, and interest rate play significant roles in determining the survival of SMEs. Adding these macroeconomic variables significantly increased the area under curve values compared to the situation where only accounting variables were used.
50
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.
Full text