Constructing a reassigning credit scoring model

Chuang, Chun‐Ling; Lin, Rong-Ho

doi:10.1016/j.eswa.2007.11.067

Cited by 64 publications

(41 citation statements)

References 34 publications

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“…A reassigning credit scoring model (RCSM) was presented in [15]. The authors constructed a hybrid model using CaseBased Reasoning (CBR) and Artificial Neural Network (ANN) classification techniques.…”

Section: A Related Work In Credit Scoringmentioning

confidence: 99%

Improving Credit Scorecard Modeling Through Applying Text Analysis

Ghailan¹,

Hoda²,

Hegazy³

2016

ijacsa

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Abstract-In the credit card scoring and loans management, the prediction of the applicant's future behavior is an important decision support tool and a key factor in reducing the risk of Loan Default. A lot of data mining and classification approaches have been developed for the credit scoring purpose. For the best of our knowledge, building a credit scorecard by analyzing the textual data in the application form has not been explored so far. This paper proposes a comprehensive credit scorecard model technique that improves credit scorecard modeling though employing textual data analysis. This study uses a sample of loan application forms of a financial institution providing loan services in Yemen, which represents a real-world situation of the credit scoring and loan management. The sample contains a set of Arabic textual data attributes defining the applicants. The credit scoring model based on the text mining pre-processing and logistic regression techniques is proposed and evaluated through a comparison with a group of credit scorecard modeling techniques that use only the numeric attributes in the application form. The results show that adding the textual attributes analysis achieves higher classification effectiveness and outperforms the other traditional numerical data analysis techniques.

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Section: A Related Work In Credit Scoringmentioning

confidence: 99%

Improving Credit Scorecard Modeling Through Applying Text Analysis

Ghailan¹,

Hoda²,

Hegazy³

2016

ijacsa

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“…Conventional statistical techniques including logistic regression have been widely used and compared with non-parametric techniques such as classification and regression tree (CART) in building scoring models (e.g. [7,9,12,13,16,30,39,51,55,58,61] ). Logistic regression deals with a dichotomous dependent variable which distinguishes it from a linear regression model, and makes the assumption that the probability of the dependent variable belonging to any of two different classes relies on the weight of the characteristics attached to it [1,4,5,37,41,48] .…”

Section: Related Studiesmentioning

confidence: 99%

“…CART can be used to analyse either quantitative or categorical data and is widely used in building scoring models (e.g. [10,13,16,32,39,59,60] ).…”

Section: Related Studiesmentioning

confidence: 99%

Predicting Creditworthiness in Retail Banking with Limited Scoring Data

et al. 2016

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a b s t r a c tThe 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.

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“…Some of hybrid models of learning that consider the application of hybrid techniques are as follows: Hybrid neural discriminant technique [38], hybrid model by probit and Classification and Regression Tree (CART) techniques [39], two-stage hybrid model using artificial neural networks and multivariate adaptive regression splines (MARS) [40], hybrid support vector machine technique [41], hybrid reassigning credit scoring model with MARS, ANN and casebased reasoning (CBR) [42], new two-stage hybrid approach by LR and back propagation network (BPN) [43], hybrid model via combining the classification and clustering techniques [4], neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model [44].…”

Section: Literature Reviewmentioning

confidence: 99%