Credit Risk Modeling and Scoring Technology

The preoccupation with modelling credit scoring systems including their relevance to predicting and decision making in the financial sector has been with developed countries, whilst developing countries have been largely neglected. The focus of our investigation is on the Cameroonian banking sector with implications for fellow members of the Banque des Etats de L'Afrique Centrale (BEAC) family which apply the same system. We apply logistic regression (LR), Classification and Regression Tree (CART) and Cascade Correlation Neural Network (CCNN) in building our knowledge-based scoring models. To compare various models' performances, we use ROC curves and Gini coefficients as evaluation criteria and the Kolmogorov-Smirnov curve as a robustness test. The results demonstrate that an improvement in terms of predicting power from 15.69% default cases under the current system, to 7.68% based on the best scoring model, namely CCNN can be achieved. The predictive capabilities of all models are rated as at least very good using the Gini coefficient; and rated excellent using the ROC curve for CCNN. Our robustness test confirmed these results. It should be emphasised that in terms of prediction rate, CCNN is superior to the other techniques investigated in this paper. Also, a sensitivity analysis of the variables identifies previous occupation, borrower's account functioning, guarantees, other loans and monthly expenses as key variables in the forecasting and decision making processes which are at the heart of overall credit policy.

The preoccupation with modelling credit scoring systems including their relevance to predicting and decision making in the financial sector has been with developed countries, whilst developing countries have been largely neglected. The focus of our investigation is on the Cameroonian banking sector with implications for fellow members of the Banque des Etats de L'Afrique Centrale (BEAC) family which apply the same system. We apply logistic regression (LR), Classification and Regression Tree (CART) and Cascade Correlation Neural Network (CCNN) in building our knowledge-based scoring models. To compare various models' performances, we use ROC curves and Gini coefficients as evaluation criteria and the Kolmogorov-Smirnov curve as a robustness test. The results demonstrate that an improvement in terms of predicting power from 15.69% default cases under the current system, to 7.68% based on the best scoring model, namely CCNN can be achieved. The predictive capabilities of all models are rated as at least very good using the Gini coefficient; and rated excellent using the ROC curve for CCNN. Our robustness test confirmed these results. It should be emphasised that in terms of prediction rate, CCNN is superior to the other techniques investigated in this paper. Also, a sensitivity analysis of the variables identifies previous occupation, borrower's account functioning, guarantees, other loans and monthly expenses as key variables in the forecasting and decision making processes which are at the heart of overall credit policy.

The goal of this paper is to analyze the role that non-financial variables can play in assessing Smes creditworthiness and to compare their value in predicting business failure with the one of the most commonly used financial ratios. We investigate the importance for banks in modeling credit risk for Smes using non-financial variables able to describe the relationship company-banks. Our findings demonstrate that credit relationship information is better predictor of corporate failure than financial ratios. We also test the models on an out-of-time and out-of-universe sample to prove their accuracy and soundness. Our initial assumptions remained confirmed: non-financial variables allow to improve the accuracy and performance of banks’ credit rating models.

To improve credit risk management, there is a lot of interest in bankruptcy predictive models. Academic research has mainly used traditional statistical techniques, but interest in the capability of machine learning methods is growing. This Italian case study pursues the goal of developing a commercial firms insolvency prediction model. In compliance with the Basel II Accords, the major objective of the model is an estimation of the probability of default over a given time horizon, typically one year. The collected dataset consists of absolute values as well as financial ratios collected from the balance sheets of 14.966 Italian micro-small firms, 13,846 ongoing and 1,120 bankrupted, with 82 observed variables. The volume of data processed places the research on a scale like that used by Moody's in the development of its rating model for public and private companies, Riskcalc TM . The study has been conducted using Gradient Boosting, Random Forests, Logistic Regression and some deep learning techniques: Convolutional Neural Networks and Recurrent Neural Networks. The results were compared with respect to the predictive performance on a test set, considering accuracy, sensitivity and AUC. The results obtained show that the choice of the variables was very effective, since all the models show good performances, better than those obtained in previous works. Gradient Boosting was the preferred model, although an increase in observation times would probably favour Recurrent Neural Networks.

We aim at developing and improving the imbalanced business risk modeling via jointly using proper
evaluation criteria, resampling, cross-validation, classifier regularization, and ensembling techniques.
Area Under the Receiver Operating Characteristic Curve (AUC of ROC) is used for model comparison
based on 10-fold cross validation. Two undersampling strategies including random undersampling (RUS)
and cluster centroid undersampling (CCUS), as well as two oversampling methods including random
oversampling (ROS) and Synthetic Minority Oversampling Technique (SMOTE), are applied. Three highly
interpretable classifiers, including logistic regression without regularization (LR), L1-regularized LR
(L1LR), and decision tree (DT) are implemented. Two ensembling techniques, including Bagging and
Boosting, are applied on the DT classifier for further model improvement. The results show that, Boosting
on DT by using the oversampled data containing 50% positives via SMOTE is the optimal model and it can
achieve AUC, recall, and F1 score valued 0.8633, 0.9260, and 0.8907, respectively.

Financial institutions use credit scorecards for risk management. A scorecard is a data-driven model for predicting default probabilities. Scorecard assessment concentrates on how well a scorecard discriminates good and bad risk. Whether predicted and observed default probabilities agree (i.e., calibration) is an equally important yet often overlooked dimension of scorecard performance. Surprisingly, no attempt has been made to systematically explore different calibration methods and their implications in credit scoring. The goal of the paper is to integrate previous work on probability calibration, to reintroduce available calibration techniques to the credit scoring community, and to empirically examine the extent to which they improve scorecards. More specifically, using real-world credit scoring data, we first develop scorecards using different classifiers, next apply calibration methods to the classifier predictions, and then measure the degree to which they improve calibration. To evaluate performance, we measure the accuracy of predictions in terms of the Brier Score before and after calibration, and employ repeated measures analysis of variance to test for significant differences between group means. Furthermore, we check calibration using reliability plots and decompose the Brier Score to clarify the origin of performance differences across calibrators. The observed results suggest that post-processing scorecard predictions using a calibrator is beneficial. Calibrators improve scorecard calibration while the discriminatory ability remains unaffected. Generalized additive models are particularly suitable for calibrating classifier predictions.

The primary purposes of binary classification is performance optimization since even the slightest prediction improvements can have signification implications for each field application. Finding the most effective separation of classes is the key success indicator in how well each model performs. When modeling prediction fails, the data entry is placed in either a false positive (incorrectly predicted) or negative (incorrectly failed to predict) category. The consequences for failing to correctly categorize can range from wasting resources and missed opportunities to serious harm caused in medical diagnoses. Optimizing classification performance focuses on reducing misclassification.

Improvements can be identified after applying metrics then in finding ways to increase accuracy and reduce misclassification. Accuracy of classification and AUCROC, area under the Receiver Operating curve, are the most commonly applied metrics, however identifying insights into misclassification requires reviewing Type I and II error metrics. Ensemble methods (bagging, boosting and stacking) are a creative means of resampling and will be utilized to improve the performance of base learners in stacked models. The German and Australian credit risk scoring datasets were run through 9 diverse algorithms, as well as adding bagging and boosting in ensemble stacked models based on strong and weak learner correlations. The results were ranked based on the overall optimal performance.

The Support Vector Machine (SVM) algorithm ranked best for both datasets with no false positives and negatives. The stacked models were in 16 of the top 17 ranked models,however the ranking of specific models was inconsistent. This study found that the stacking ensemble premise did not have a clear delineation in performance improvement in combining the weakest and strongest learners together, however stacking itself proved very successful. The premise is that combining the weakest learners, least correlated, should be more effective than the strong learners since they have the most to benefit from reweighting for resampling that promotes the weakest and demotes the stronger learners helping to focus on the weakest algorithms fixing their misclassifications.

The conclusion regarding model choice is that no assumptions could be made regarding algorithm performance and that empirical testing is needed for model selection.

The key implication of optimizing classification performance is the reduction of misclassification errors and thereby enabling greater classification accuracy which translates into gaining better insights from the risk score modeling process that can be effectively prescribed back in an organization. One of the most important areas of future work continues to be finding solutions to class balancing which improve the quality of performance metrics that are inherently skewed.

Keywords: business risk assessment, classification performance optimization, credit risk scoring, risk scoring analytics

Risk Scoring Classification Performance Optimization. Available from: https://www.researchgate.net/publication/320011901_Risk_Scoring_Classification_Performance_Optimization [accessed Sep 24, 2017].

Logistic regression is a popular statistic modelling algorithm in predicting a binary outcome. Although logistic regression almost always has an intercept, logistic regression without intercept is sometimes appropriate or even necessary. However, logistic regression without intercept has rarely been discussed other than being used explicitly or implicitly. In this paper, we aim at filling this gap by systematically studying logistic regression without intercept. Specifically, we study the 4 most important aspects of logistic regression: (1) Maximum Likelihood Estimate, (2) data configuration (complete separation, quasi-complete separation and overlap) to categorize the existence and uniqueness of maximum likelihood estimate, (3) multicollinearity, and (4) monotonic transformations of independent variables. We adopt an extensional method in that we first present results for logistic regression with intercept and then extend the results to the case of without intercept. Our numerical examples further compare logistic regression with intercept and without intercept.

A Bázel-2 tõkeegyezmény magyarországi bevezetése új lendületet adott a sokválto zós csõd-elõrejelzési módszerek alkalmazásnak és továbbfejlõdésének. A cikk a nemzetközi szakirodalomban és pénzintézeti gyakorlatban leggyakrabban alkalma zott négy csõd-elõrejelzési módszer becslõképességét hasonlítja össze. Empirikus vizsgálattal alátámasztva igyekszik választ találni arra a kérdésre, vajon a kevésbé szigorú alkalmazási feltételeket támasztó szimulációs eljárások megbízhatóbb csõd elõrejelzést, valamint a nem fizetési valószínûségek jobb becslését tesznek-e lehetõ vé, mint a hagyományos matematikai-statisztikai alapú eljárások. Az empirikus vizs gálat eredményei arra is rávilágítanak, hogy a fõkomponens-elemzéssel nem feltétle nül növekszik az elõrejelzõ képesség.* Journal of Economic Literature (JEL) kód: C52, C53, C45, G33.

Kredi notu bankalarda ki kimliğimizdir diyebiliriz. kredi notu düşük olanlar kredi notu yükseltme tekniklerini uygulayarak kolay bir şekilde kredi notu yükseltebilirler. Merkez bankası tarafından hesaplanan kredi notu etkileyen bir çok faktör vardır. Bunlar mevcut kredi ve kredi kartı ödemeleriniz, yeni kredi açılışlarınız, mevcut hesaplarınızın durumu, esnek hesabınızın kullanıp kullanılmadığı, kullanılıyorsa borcunuzu zamanında ödeyip ödeyemediğiniz gibi verilere göre hesaplanır. Bu yazıda detaylı olarak kredi notu yükseltme işlemlerinden bahsedeceğim, kredi notu yükseltme işlemleri için para vermenize gerek yok ve gözünüzde büyütmenize de gerek yoktur. bir kaç basit adımla kredi notunuzu yükseltebilirsiniz. Hadi Başlayalım… Kredi ve Kredi Kartı Ödemelerinizi Düzene Koyun Evet oldukça basit, ödeme alışkanlıkları kredi notunu en çok etkileyen faktördür. Hatta kredi kartı ödemelerinizi zamanında yapıyorsanız bankalar size değer verecektir. ve Kredi kartı yıllık ücretleri müşteri hizmetlerini arayarak iade etmenizi talep etmeniz halinde ödemelerinize bakılır ve eğer düzenli ödüyorsanız sizden kesilen yıllık kart ücreti anında iade edilir. (Tarafımca bugün denenmiş ve yıllık kart ücreti iadesi aldım) Tabi bu sağlam olduğunuz anlamına da gelir. Aynı şekilde kredi ve kredi kartı ödemelerinizi zamanında eksiksiz bir şekilde yapmanız durumunda kredi notunuz kolaylıkla yükselecektir. Bugüne kadar eğer ödemelerinizi düzenli değilse bu aydan başlanmak üzere ödemelerinizi düzene koymaya başlayın ve aynı gün içerisinde kredi notu nasıl yükseldiğini görebilirsiniz.

To improve credit risk management, there is a lot of interest in bankruptcy predictive models. Academic research has mainly used traditional statistical techniques, but interest in the capability of machine learning methods is growing. This Italian case study pursues the goal of developing a commercial firms insolvency prediction model. In compliance with the Basel II Accords, the major objective of the model is an estimation of the probability of default over a given time horizon, typically one year. The collected dataset consists of absolute values as well as financial ratios collected from the balance sheets of 14.966 Italian micro-small firms, 13,846 ongoing and 1,120 bankrupted, with 82 observed variables. The volume of data processed places the research on a scale like that used by Moody's in the development of its rating model for public and private companies, Riskcalc TM. The study has been conducted using Gradient Boosting, Random Forests, Logistic Regression and some deep learning techniques: Convolutional Neural Networks and Recurrent Neural Networks. The results were compared with respect to the predictive performance on a test set, considering accuracy, sensitivity and AUC. The results obtained show that the choice of the variables was very effective, since all the models show good performances, better than those obtained in previous works. Gradient Boosting was the preferred model, although an increase in observation times would probably favour Recurrent Neural Networks.

Financial institutions use credit scorecards for risk management. A scorecard is a data-driven model for predicting default probabilities. Scorecard assessment concentrates on how well a scorecard discriminates good and bad risk. Whether predicted and observed default probabilities agree (i.e., calibration) is an equally important yet often overlooked dimension of scorecard performance. Surprisingly, no attempt has been made to systematically explore different calibration methods and their implications in credit scoring. The goal of the paper is to integrate previous work on probability calibration, to reintroduce available calibration techniques to the credit scoring community, and to empirically examine the extent to which they improve scorecards. More specifically, using real-world credit scoring data, we first develop scorecards using different classifiers, next apply calibration methods to the classifier predictions, and then measure the degree to which they improve calibration. To evaluate performance, we measure the accuracy of predictions in terms of the Brier Score before and after calibration, and employ repeated measures analysis of variance to test for significant differences between group means. Furthermore, we check calibration using reliability plots and decompose the Brier Score to clarify the origin of performance differences across calibrators. The observed results suggest that post-processing scorecard predictions using a calibrator is beneficial. Calibrators improve scorecard calibration while the discriminatory ability remains unaffected. Generalized additive models are particularly suitable for calibrating classifier predictions.

The article attempts to synthesize the historical development tendencies of theoretical approaches, methodologies and empirical researches of corporate survival and bankruptcy prediction, laying emphasis on the 30-year development history of Hungarian empirical bankruptcy prediction models. Based on the historical development of Hungarian bankruptcy prediction it can be argued that it has already caught up to the level of international best practice regarding the examined research problems, applied methods and empirical results. Throughout the development history of Hungarian bankruptcy prediction it can be tracked, how the initial, small, cross-sectional sample and classical methodology based bankruptcy prediction has evolved to today's corporate rating systems meeting the requirements of the dynamic, through-the-cycle economic capital calculation models. Contemporary methodological development is characterized by the domination of artificial intelligence, data mining, machine learning and hybrid modelling. The article reveals that Hungarian bankruptcy models are necessary to accomplish efficient bankruptcy prediction in Hungary. Throughout model development, it is essential to consider the sampling problems, the definition of target variable, the proper accomplishment of data preparation and data transformation steps, the dynamization of variables and the inclusion of behavioural variables. In addition the article draws several normative proposals how to select the right bankruptcy prediction method(s).

Preface from Edward I Altman.
Abstract
Scholars and practitioners have known for a long time that risk plays an important, indeed central, role in determining the appropriate discount rate to be used in a sophisticated valuation model. In today's world, however, the very risk of survival, especially for financial institutions, is essential to the health of the world's capital markets and its impact on the global economy.

Risk, Value and Default is a vital text for understanding the interaction between enterprise risk management and corporate valuation and corporate default. The book seeks to explore the interaction between the risk of default and enterprise risk, and their joint impact on firm valuation. It aims to address the problem of how corporations should deal with risk and how they can maximize shareholder value. It also examines various conceptual ways to measure risk, thereby bridging the gap between theoretical concepts and pragmatic application.

The book combines sound conceptual analytics and regional firm data to provide useful information and tangible guidelines for firms as well as for analysts and advisors of these entities. Scholars and professionals with an interest in risk management, and managers, owners, creditors and potential investors in enterprises will find Risk, Value and Default a particularly useful guide to understanding the relationship between risk generation, risk management and corporate value and default from an interdisciplinary perspective.



Readership: Scholars and practitioners with an interest in risk governance, valuation and risk management within the context of the risk management and governance, corporate finance, banking, econometrics, mathematical economics and quantitative finance.

Development in science has enabled the improvement of bankruptcy prediction models through several data-driven and artificial intelligence based methods. One of such promising methods might be the Case-Based Reasoning (CBR) method. The aim of this article is to consider the applicability of CBR on a sample of Hungarian micro enterprises, within the framework of classic bankruptcy prediction, by comparing the classification power of CBR to the three most frequently applied bankruptcy prediction techniques (decision tree, logistic regression, neural networks). The empirical research examined the occurrence of bankruptcy events (initiating bankruptcy, liquidation or forced deregistration procedure) for 1.828 Hungarian micro enterprises in 2017 using financial ratios calculated from 2015 and 2016 annual reports. On the whole it can be concluded that it is worthwhile to consider CBR methodology in solving classification problems similar to bankruptcy prediction. However, on the basis of the empirical research, the predictive power of the developed CBR model underperformed the accuracy of neural networks and logistic regression on observations in the testing sample.

In the development of the global economy, today a country should have a strong foundation to create the welfare of society. Food security is one of the strategic issues in the development of a country, especially the developing country, because it has multiple roles as one of the main target of development and one of the main instruments (intermediate goals) of economic development. This system is designed to help the community, especially housewives in buying household appliances, and this is done to raise awareness of the environmental pillars for the welfare of society.Neighborhood Association is part of government structure under within a Village, expected economic power level neighborhood Association can create independence of rural or village. Building the technology model and infrastructure of neigborhood association is part of the rural economic development effort. Where the database system model, application, networking that will be built for stall users and product distribution management for the neighbors is done by the community manager of neigborhood association level. Although using the flexibility method of infrastructure. Keyword : neighborhood Association; technology model and infrastructure; flexibility method of infrastructure;

The goal of this paper is to analyze the role that non-financial variables can play in assessing Smes creditworthiness and to compare their value in predicting business failure with the one of the most commonly used financial ratios. We investigate the importance for banks in modeling credit risk for Smes using non-financial variables able to describe the relationship company-banks. Our findings demonstrate that credit relationship information is better predictor of corporate failure than financial ratios. We also test the models on an out-of-time and out-of-universe sample to prove their accuracy and soundness. Our initial assumptions remained confirmed: non-financial variables allow to improve the accuracy and performance of banks’ credit rating models

The rise and evolution of financial calculative practices play a central role in financial markets and are an important force in the financialization process. Using the case of Moody’s, a major credit rating agency, this article traces a fundamental paradigm shift in rating practices since the late 1980s. These innovations in credit rating were driven by new calculative models, new kinds of analysts (so-called ‘quants’), and an increasingly profit-oriented organizational culture at Moody’s, all shaped by an increasingly financialized context. By studying credit rating agencies’ historical and current methodological publications and interviewing rating analysts, I show that the rating innovations of the 1980s and 1990s do not simply alter the rating methods or models. More importantly, they entail an entirely different set of epistemological assumptions about the calculability and predictability of the future, representing a fundamental paradigm shift in calculating and defining credit risk. In this framework, risk is not a threat that must be minimized, but a neutral variable that can be calculated, managed and used to generate profits. These structured finance rating practices suggest a high level of control over risk, and imply that knowledge about risk is sufficient to render it unproblematic. This specific culture of credit risk assessment enabled the emergence and exponential growth of structured finance markets, fueling a culture of high-risk investments. This article contributes to the literature on financialization by arguing that financialization occurs both at the level of organizational culture and calculative practices.

Bank and rating agencies use widely credit scoring models to assess default probability of the enterprises. In consequence, many borrowers can be interested to manipulate own financial statements in order to improve their credit rating. This problem is actual for the large banks, rating agencies and for regulators which have to use formal techniques of solvency assessment because of large samples of data. The problem can be even more difficult if there are only few large banks which dominate the banking market in the economy. In this case firms may tend to adjust their financial statements to conform to requirements of specific bank or rating agency. So, modern research in the field of default probability is aimed not so much at development of new financial coefficients how many at understanding existing credit scoring methods and its influence on the firm`s financial statements. Many research papers show that the aspiration to improve a credit rating can be one of motives of financial policy of the enterprise.
In this article we analyse credit scoring models of the two biggest state controlled Russian banks Sberbank and VTB. They dominate Russian banking market and have large influence on the economy as a whole. We use simple binary logistic regression model to assess the default probability of a company. In the model we chose as a dependent variable the solvency of the enterprise in the next year (0 – financially safe enterprise, 1 – bankruptcy, the decision on liquidation of the company is made by the government body). As factors were used financial coefficients from the banks credit scoring models (six financial coefficients from each bank).
In our research we analysed more than 100 widely known financial coefficients and found some of them very usefull in forecasting insolvency. We built other logistic regression model which uses as independent variables company size (proxied by logarithm), profitability and turnover ratios. The results are statistically significant and passed all necessary statistical tests. Our conclusions are confirmed also by other authors research results. The offered logistic regression model predicted 99% of solvency cases and about 30% of insolvency cases.
Following the results of research it is recommended to regulators to show more discretion when checking bank credit portfolios, to avoid exclusively formalistic approaches based on regression models and to insist on increase of banks reserves on problem loans. Our research shows also that credit scoring models of big banks can be subject to bigger risk of manipulations than model of small banks.

Key words: probability of default, credit scoring, binary logistic regression, innovative companies, Russia.