A practical approach to bankruptcy prediction for small businesses: Substituting the unavailable financial data for credit card sales information

Yoon, Joonseok; Kwon, Young S.

doi:10.1016/j.eswa.2009.10.029

Cited by 23 publications

(15 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to develop the statistical model it is necessary to find objective criteria for the default prediction such as financial information, income statements, predictive revenue, location and business potential, etc. (Yoon and Kwon 2010). Chen et al (2009) affirm that in case of commercial and industrial lending, applicants are required to submit written profile of business ownership, management team, company literature, historical (generally past 3 years), current as well as future projection of financial statements -balance sheet, income statement, and statements of cash flows.…”

Section: Statistical Methods For the Analysis Of Credit Applicants Datamentioning

confidence: 99%

New Internal Rating Approach for Credit Risk Assessment / Naujas Vidaus Reitingu Modelis Kredito Rizikos Vertinimui

Boguslauskas

Mileris

Adlytė

2011

Technological and Economic Development of Economy

View full text Add to dashboard Cite

The assessment and modeling of the credit risk is one of the most important topics in the field of financial risk management. In this investigation the credit risk assessment model was developed and tested for Lithuanian companies. 20 financial ratios of the companies were calculated for each year of the 3 year period of interest. The analysis of variance (ANOVA) and Kolmogorov-Smirnov test were applied and the set of variables reduced from 60 to 25. Logistic regression was used for the classification of the companies into reliable and not reliable ones. Financial ratios, having the highest correlation to the possibility of default were selected for further investigation and several credit ratings were attributed to the companies according to these variables’ values. The average values of Mahalanobis Distances calculated for the most reliable companies were the lowest and these values increased with a decreased reliability of the company. The differences between Mahalanobis Distances of the companies having different credit ratings confirmed the reliability of the model results. Santrauka Kredito rizikos vertinimas ir modeliavimas – viena iš aktualiausiu temų, kalbant apie finansinės rizikos valdymą. Atlikto tyrimo metu buvo sukurtas kredito rizikos modelis. šis modelis išbandytas 198 Įmonių aibėje, skaičiuojant po 20 finansinių rodiklių 3 analizuojamų metu laikotarpiu. Panaudojus ANOVA metodą ir Kolmogorovo – Smirnovo statistiką, kintamųjų kiekis buvo sumažintas nuo 60 iki 25 rodiklių. Įmonįu klasifikavimui į 2 grupes: patikimus ir nepatikimus banko klientus, atsižvelgiant į jų įsipareigojimų nevykdymo tikimybę, buvo naudojama logistinė regresija. 97 proc. patikimų (nebankrutavusių) ir 82 proc. nepatikimų (bankrutavusių) įmonių suklasifikuotos teisingai. Tolimesniam tyrimui atrinkti 7 finansiniai rodikliai, kurių koreliacinis ryšys su įsipareigojimų nevykdymo tikimybe buvo didžiausias. Atsižvelgiant į šių kintamųjų reikšmės, įmonėms buvo priskirti 9 kredito reitingai. Vidutines Mahalanobio atstumu reikšmes, apskaiČiuotos patikimiausioms kompanijoms buvo mažiausios; šios reikšmės didėjo, mažejantįmonių patikimumui. Skirtingį reitingį įmonėms apskaiČiuoti Mahalanobio atstumų skirtumai, pagrindė modelio rezultatų patikimumą.

show abstract

Section: Statistical Methods For the Analysis Of Credit Applicants Datamentioning

confidence: 99%

New Internal Rating Approach for Credit Risk Assessment / Naujas Vidaus Reitingu Modelis Kredito Rizikos Vertinimui

Boguslauskas

Mileris

Adlytė

2011

Technological and Economic Development of Economy

View full text Add to dashboard Cite

show abstract

“…Van Gestel et al (2006), also developed a Bayesian least squares SVM classifier and tested the rule on a commercial credit data set based on Belgian and Dutch firms. Yoon and Kwon (2010) proposed a method built on credit card sales information using SVMs to solve the missing financial data problem. Same tool was employed by Kim and Sohn (2010) for building a credit risk estimation for Korean small and medium enterprises.…”

Section: Introductionmentioning

confidence: 99%

Advances in credit scoring: combining performance and interpretation in kernel discriminant analysis

Liberati

Camillo

Saporta

2015

Adv Data Anal Classif

View full text Add to dashboard Cite

Due to the recent financial turmoil, a discussion in the banking sector about how to accomplish long term success, and how to follow an exhaustive and powerful strategy in credit scoring is being raised up. Recently, the significant theoretical advances in machine learning algorithms have pushed the application of kernel-based classifiers, producing very effective results. Unfortunately, such tools have an inability to provide an explanation, or comprehensible justification, for the solutions they supply. In this paper, we propose a new strategy to model credit scoring data, which exploits, indirectly, the classification power of the kernel machines into an operative field. A reconstruction process of the kernel classifier is performed via linear regression, if all predictors are numerical, or via a general linear model, if some or all predictors are categorical. The loss of performance, due to such approximation, is balanced by better interpretability for the end user, which is able to order, understand and to rank the influence of each category of the variables set in the prediction. An Italian bank case study has been illustrated and discussed; empirical results reveal a promising performance of the introduced strategy.

show abstract

“…There exist situations where the correct detection of instances of one class (the positive class) is considered to be of greater importance or priority than the other class (the negative class) in binary classification problems. Problems which involve this situation arise: in the medical diagnosis of certain disorders, such as the detection of breast cancer [16,30] or cardiac care [23]; in certain financial problems, such as credit-card fraud detection [29], financial crisis [18], detection of financial statement fraud [24,6], bankruptcy prediction [27,21], prediction of liquefaction potential [28] and bank marketing [20]; and in criminological investigations, among other applications.…”

Section: Introductionmentioning

confidence: 99%

Handling binary classification problems with a priority class by using Support Vector Machines

Abril

Ángulo

Núñez

et al. 2017

Applied Soft Computing

View full text Add to dashboard Cite

© 2017 Elsevier B.V. A post-processing technique for Support Vector Machine (SVM) algorithms for binary classification problems is introduced in order to obtain adequate accuracy on a priority class (labelled as a positive class). That is, the true positive rate (or recall or sensitivity) is prioritized over the accuracy of the overall classifier. Hence, false negative (or Type I) errors receive greater consideration than false positive (Type II) errors during the construction of the model. This post-processing technique tunes the initial bias term once a solution vector is learned by using standard SVM algorithms in two steps: First, a fixed threshold is given as a lower bound for the recall measure; second, the true negative rate (or specificity) is maximized. Experiments, carried out on eleven standard UCI datasets, show that the modified SVM satisfies the aims for which it has been designed. Furthermore, results are comparable or better than those obtained when other state-of-the-art SVM algorithms and other usual metrics are considered.Peer ReviewedPostprint (author's final draft

show abstract

A practical approach to bankruptcy prediction for small businesses: Substituting the unavailable financial data for credit card sales information

Cited by 23 publications

References 13 publications

New Internal Rating Approach for Credit Risk Assessment / Naujas Vidaus Reitingu Modelis Kredito Rizikos Vertinimui

New Internal Rating Approach for Credit Risk Assessment / Naujas Vidaus Reitingu Modelis Kredito Rizikos Vertinimui

Advances in credit scoring: combining performance and interpretation in kernel discriminant analysis

Handling binary classification problems with a priority class by using Support Vector Machines

Contact Info

Product

Resources

About