An Ensemble Learning Model Based on SOM-SVM Model for Personal Credit Risk

2011 ◽  
Vol 271-273 ◽  
pp. 1286-1290
Author(s):  
Yan Feng Guo ◽  
Na Sun ◽  
Yuan Yao
Keyword(s):  
Credit Risk ◽  
Financial Management ◽  
Financial Risk ◽  
Credit Scoring ◽  
Experimental Result ◽  
Ensemble Model ◽  
Single Model ◽  
Personal Credit ◽  
Svm Model ◽  
Management Area

Credit risk problem is an essential problem in financial management area. People usually employ personal credit scoring to avoid financial risk problem. Although many methods have been proposed for evaluating the personal credit scoring and obtained good effects, most of these methods were called single model types, which would be disturbed by model self-parameter, data noise and other external factors. In order to overcome the weakness of single model, we believe one of best ways is to construct an ensemble model. In this paper, we proposed a new style of ensemble model and employed two public credit datasets to certify the validity of our ensemble model. The experimental result shows that the ensemble SOM-SVM model can overcome the single model weakness and improve the accuracy of classification, which is good for constructing a better credit scoring system in future.

scholarly journals Мінімізація кредитних ризиків у діяльності сільських кредитних спілок із використанням систем скорингу

2015 ◽  
pp. 99-102
Author(s):  
О. В. Орлов
Keyword(s):  
Risk Assessment ◽  
Credit Risk ◽  
Scoring System ◽  
Scientific Method ◽  
Credit Scoring ◽  
Credit Unions ◽  
Single Model ◽  
Rural Credit ◽  
Effective System

У статті розглянуто проблему кредитних ризиківу роботі сільських кредитних спілок та запропонова-но ефективну систему оцінки таких ризиків з викорис-танням системи скорингу. Наведені теоретичніджерела скорингу, як наукового методу. Надані кон-кретні пропозиції щодо розвитку та застосуванняскорингу в роботі сільських кредитних спілок. Акцен-товано увагу на важливості та необхідності засто-сування зазначеного методу в практичній діяльностіданих організацій, доцільності розробки єдиної моде-лі автоматизованої системи кредитного скорингудля мінімізації кредитних ризиків в роботі сільсько-господарських кредитних спілок. The article deals with the problem of credit risk in the rural credit unions and we offer an effective system of risk assessment using the scoring system. Theoretical sources of scoring as the scientific method are adduced. Specific offers for the development and application of scoring in the rural credit unions are given. We emphasize the importance and the need to use this method in practical activities of these organizations, the feasibility of developing of a single model of an automated credit scoring system in order to minimization of credit risk in the rural credit unions.

scholarly journals Weight-Selected Attribute Bagging for Credit Scoring

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Jianwu Li ◽  
Haizhou Wei ◽  
Wangli Hao
Keyword(s):  
Credit Risk ◽  
Prediction Accuracy ◽  
Financial Risk ◽  
Evaluation Method ◽  
Credit Scoring ◽  
Principal Component ◽  
Support Vector ◽  
Training Samples ◽  
Attribute Evaluation ◽  
Bagging Method

Assessment of credit risk is of great importance in financial risk management. In this paper, we propose an improved attribute bagging method, weight-selected attribute bagging (WSAB), to evaluate credit risk. Weights of attributes are first computed using attribute evaluation method such as linear support vector machine (LSVM) and principal component analysis (PCA). Subsets of attributes are then constructed according to weights of attributes. For each of attribute subsets, the larger the weights of the attributes the larger the probabilities by which they are selected into the attribute subset. Next, training samples and test samples are projected onto each attribute subset, respectively. A scoring model is then constructed based on each set of newly produced training samples. Finally, all scoring models are used to vote for test instances. An individual model that only uses selected attributes will be more accurate because of elimination of some of redundant and uninformative attributes. Besides, the way of selecting attributes by probability can also guarantee the diversity of scoring models. Experimental results based on two credit benchmark databases show that the proposed method, WSAB, is outstanding in both prediction accuracy and stability, as compared to analogous methods.

Dairy farm financial performance: firm, year, and size effects

2016 ◽  
Vol 76 (4) ◽  
pp. 532-543 ◽  
Author(s):  
Christopher A. Wolf ◽  
Mark W. Stephenson ◽  
Wayne A. Knoblauch ◽  
Andrew M. Novakovic
Keyword(s):  
Credit Risk ◽  
Financial Performance ◽  
Financial Management ◽  
Financial Risk ◽  
Dairy Farm ◽  
Herd Size ◽  
Financial Ratios ◽  
Management Tools ◽  
Content Type ◽  
Over Time

Purpose The purpose of this paper is to evaluate dairy farm financial performance over time utilizing farm financial ratios from three university business analysis programs. The evaluation includes measures of profitability, solvency, and liquidity by herd size. Design/methodology/approach Financial ratios to reflect profitability (rate of return on assets), solvency (debt to asset ratio), and liquidity (current ratio) were collected from Cornell University, Michigan State University, and the University of Wisconsin for dairy farms from 2000 to 2012. The distribution of farm financial performance using these ratios was examined over time and by herd size. Variance component methods are used to examine the percent of variation due to individual firm and industry aspects. A simple credit risk score is calculated to examine relative farm risk. Findings Dairy farm profitability performance is similar across herd sizes in poor years but larger herds realized significantly more profitability in good years. Findings were similar with respect to liquidity. Large herds consistently carried relatively more debt. Large herds’ financial performance was more uniform than across smaller herds. Larger herds had more financial risk as measured by credit risk scoring but recovered quickly to industry averages in profitable years. Originality/value The variation of dairy farm financial performance in an era of volatile milk and feed price is assessed. The results have important implications for farm financial management and benchmarking farm financial performance. In addition to helping to evaluate the efficacy of various price and income risk management tools, these results have important implications for understanding the benefits of the new federal Margin Protection Program for Dairy that is available to all US dairy farmers.

A novel multi-stage ensemble model with multiple K-means-based selective undersampling: An application in credit scoring

2021 ◽  
Vol 40 (5) ◽  
pp. 9471-9484
Author(s):  
Yilun Jin ◽  
Yanan Liu ◽  
Wenyu Zhang ◽  
Shuai Zhang ◽  
Yu Lou
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Credit Scoring ◽  
Imbalanced Data ◽  
Ensemble Model ◽  
Selective Sampling ◽  
Machine Learning Methods ◽  
Multi Stage ◽  
Proposed Model ◽  
New Feature

With the advancement of machine learning, credit scoring can be performed better. As one of the widely recognized machine learning methods, ensemble learning has demonstrated significant improvements in the predictive accuracy over individual machine learning models for credit scoring. This study proposes a novel multi-stage ensemble model with multiple K-means-based selective undersampling for credit scoring. First, a new multiple K-means-based undersampling method is proposed to deal with the imbalanced data. Then, a new selective sampling mechanism is proposed to select the better-performing base classifiers adaptively. Finally, a new feature-enhanced stacking method is proposed to construct an effective ensemble model by composing the shortlisted base classifiers. In the experiments, four datasets with four evaluation indicators are used to evaluate the performance of the proposed model, and the experimental results prove the superiority of the proposed model over other benchmark models.

scholarly journals Measuring and Managing Credit Risk for Chinese Microfinance Institutions

2018 ◽  
Vol 10 (7) ◽  
pp. 56
Author(s):  
Jie Li ◽  
Zhenyu Sheng
Keyword(s):  
Credit Risk ◽  
Predictive Power ◽  
Direct Method ◽  
Credit Scoring ◽  
Microfinance Institutions ◽  
Linear Discriminant ◽  
Management Level ◽  
Scoring Model ◽  
Subjective Appraisals ◽  
Credit Scoring Model

Chinese microfinance institutions need to measure and manage credit risk in a quantitative way in order to improve competitiveness. To establish a credit scoring model (CSM) with sound predictive power, they should examine various models carefully, identify variables, assign values to variables and reduce variable dimensions in an appropriate way. Microfinance institutions could employ both CSM and loan officer’s subjective appraisals to improve risk management level gradually. The paper sets up a CSM based on the data of a microfinance company running from October 2009 to June 2014 in Jiangsu province. As for establishing the model, the paper uses Linear Discriminant Analysis (LDA) method, selects 16 initial variables, employs direct method to assign variables and adopts all the variables into the model. Ten samples are constructed by randomly selecting records. Based on the samples, the coefficients are determined and the final none-standardized discriminant function is established. It is found that Bank credit, Education, Old client and Rate variables have the greatest impact on the discriminant effect. Compared with the same international models, this model’s classification effect is fine. The paper displays the key technical points to build a credit scoring model based on a practical application, which provides help and references for Chinese microfinance institutions to measure and manage credit risk quantitatively.

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