Credit risk evaluation by using nearest subspace method

AbstractIn this paper, a classification method named nearest subspace method is applied for credit risk evaluation. Virtually credit risk evaluation is a very typical classification problem to identify “good” and “bad” creditors. Currently some machine learning technologies, such as support vector machine (SVM), have been discussed widely in credit risk evaluation. But there are many effective classification methods in pattern recognition and artificial intelligence have not been tested for credit evaluation. This paper presents to use nearest subspace classification method, a successful face recognition method, for credit evaluation. The nearest subspace credit evaluation method use the subspaces spanned by the creditors in same class to extend the training set, and the Euclidean distance from a test creditor to the subspace is taken as the similarity measure for classification, then the test creditor belongs to the class of nearest subspace. Experiments on real world credit dataset show that the nearest subspace credit risk evaluation method is a competitive method

ENTERPRISE CREDIT RISK ASSESSMENT ANALYZING THE DATA OF SHORT TERM ACTIVITY PERIOD

This research investigates the possibility to classify the companies into default and non-default groups analyzing the financial data of 1 year. The developed statistical model enables banks to predict the default of new companies that have no sufficient financial information for the credit risk assessment using other models. The classification and regression tree predicts the default of companies with the 96% probability. The complementary analysis the financial data of 2 years by probit model allows to increase the classification accuracy to 99%. DOI: https://doi.org/10.15544/ssaf.2012.2

A Hybrid Machine Learning Approach for Credit Scoring Using PCA and Logistic Regression

Credit scoring is one mechanism used by lenders to evaluate risk before extending credit to credit applicants. The method helps distinguish credit worthiness of good credit applicants from the bad credit applicants.  Credit scoring involves a set of decision models and with their underlying techniques helps aid lenders in issuing of consumer credit. Logistic regression (LR) is an adjustment of linear regression with flexibility on its preposition of data and is also able to handle qualitative indicators. The major shortcoming of Logistic regression model is the inability to deal with cooperative (over fitting) effect of the variables. PCA is a feature extraction model that is used to filter out irrelevant un-needed features and hence, it lowers model training time and costs and also increases model performance. This study evaluates the shortcomings of simple models and proposes to develop an efficient and robust machine learning technique combining Logistic and PCA models to evaluate firms in the deposit taking SACCO sector. To achieve this, experimental methodology is adopted.  The proposed hybrid model will be two staged. First stage will be to transform the original variables to get new uncorrelated variables. This will be done using Principal Component Analysis (PCA). Stage two is the use of LR on the principal component values to compute the credit scores. Inferences and conclusions were made based on the analysis of the collected data using Matlab.

Combining B&B-based hybrid feature selection and the imbalance-oriented multiple-classifier ensemble for imbalanced credit risk assessment

An ideal model for credit risk assessment is supposed to select important features and process imbalanced data sets in an effective manner. This paper proposes an integrated method that combines B&B (branch and bound)-based hybrid feature selection (BBHFS) with the imbalanceoriented multiple-classifier ensemble (IOMCE) for imbalanced credit risk assessment and uses the support vector machine (SVM) and the multiple discriminant analysis (MDA) as the base predictor. BBHFS is a hybrid feature selection method that integrates the t-test and B&B with the k-fold crossvalidation method to search for a satisfactory feature subset. The IOMCE divides majority samples into several subsets and then combines them with minority samples to construct several training sets for constructing a multiple-classifier ensemble model. We conduct main experiments using a 1:3 imbalanced corporate credit risk data set with continuous features and extended experiments using a 1:5 imbalanced data set with continuous features and a 1:3 imbalanced data set with discrete and nominal features. We combine no feature selection and five feature selection methods (the pure B&B, the factor analysis, the pure t-test, t-test & correlation analysis, and BBHFS) with single-classifier and the IOMCE to construct SVM and MDA models for an empirical comparison. When all features are continuous, the BBHFS-IOMCE method generally outperforms all the other methods. More specifically, BBHFS provides more stable and satisfactory results than the other feature selection methods, and compared with single-classifier models, IOMCE models can significantly enhance the recognition rate for minority samples while incurring a small reduction in the recognition rate for majority samples and maintaining an acceptable overall accuracy. When the features are almost discrete or nominal, the IOMCE method retains its ability to deal with an imbalanced data set, although the five feature selection methods have no significant advantages over no feature selection. This suggests that BBHFS is effective in retaining useful information when reducing the dimensionality of continuous features and that the BBHFS-IOMCE method is an important tool for imbalanced credit risk assessment

Проблемы оценки кредитоспособности корпоративных клиентов коммерческими банками в рамках организации кредитного процесса

Объектом исследования является деятельность банков России в области оценки кредитоспособности корпоративных клиентов. В результате исследования были разработаны мероприятия по совершенствованию методики оценки кредитоспособности корпоративных клиентов для регионального банка города Томск ПАО «Томскпромстройбанк», а также представлены меры, которые позволят повысить эффективность оценки заемщиков коммерческими банками на общероссийском уровне.The object of research is the practice of Russian banks to assess the creditworthiness of corporate clients. The study was developed measures to improve the methodology for assessing the creditworthiness of corporate customers for the regional bank Tomsk PJSC "Tomskpromstroybank" and presented measures that will enhance the evaluation of borrowers by commercial banks at the national level