Rule Induction Methods For Credit Scoring

Zurada, Jozef

doi:10.19030/rbis.v11i2.4421

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…Desai et al () analyze the usefulness of neural networks and traditional techniques, such as discriminant analysis and logistic regression, in building credit scoring models for credit unions. Zurada () examines and compares the effectiveness of three decision tree algorithms (χ 2 , entropy reduction and Gini reduction) to predict whether a consumer will default or pay off a loan. Stepanova () examines three extensions of Cox's proportional hazards model applied to personal loan data.…”

Section: Previous Studiesmentioning

confidence: 99%

Developing a Risk Group Predictive Model for Korean Students Falling into Bad Debt

Han¹,

Choi²,

Kim³

et al. 2018

Asian Economic Journal

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Using direct loan data for 2012 to 2014 from the Korea Student Aid Foundation, we develop a risk group predictive model for borrowers defaulting on their loans. We used a logistic regression model and the Cox proportional hazards model to develop the risk predictive model. We verified the validity of the models using a receiver operating characteristic curve and a validation dataset. The present study shows that area under the receiver operating characteristic curves is similar for the models and that the major influencing factors for defaulting on their loans are household income, whether a national grant was received, age, whether more than two accounts are overdue, field of study and the monthly repayment amount. The risk group predictive model in this study will be the basis for more efficient management of direct student loans.

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Section: Previous Studiesmentioning

confidence: 99%

Developing a Risk Group Predictive Model for Korean Students Falling into Bad Debt

Han¹,

Choi²,

Kim³

et al. 2018

Asian Economic Journal

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show abstract

“…Bellotti and Crook (2009) use SVM, LR, LDA and kNN on a very large data set (25,000 records) from a financial institution and find that SVM is comparatively successful in classifying credit card debtors who do default; but unlike the other compared models, a large number of support vectors are required to achieve the best performance. Two comparative studies (Zurada, 2007(Zurada, , 2010) use LR, NN, DT, memory-based reasoning (MBR) and an ensemble model using German and SAS-1 data sets. Both found that for some cut-off points and conditions, DTs perform well with respect to classification accuracy and that DTs are attractive tools for decision makers because they can generate easy to interpret if-then rules.…”

Section: Literature Reviewmentioning

confidence: 99%

An Investigation Of The Effect Of Variable Reduction On Classification Accuracy Rates Of Consumer Loans

Zurada

2013

RBIS

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“…The literature on the credit scoring topic is quite abundant and for a more complete literature review, the reader is referred to Zurada (2007). Although we investigated and analyzed a great deal of papers, in this study we only describe several works published recently.…”

Section: Introduction and Prior Literaturementioning

confidence: 99%

A Preliminary Investigation Of Decision Tree Models For Classification Accuracy Rates And Extracting Interpretable Rules In The Credit Scoring Task: A Case Of The German Data Set

Zurada

Lam

2008

RBIS

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For many years lenders have been using traditional statistical techniques such as logistic regression and discriminant analysis to more precisely distinguish between creditworthy customers who are granted loans and non-creditworthy customers who are denied loans. More recently new machine learning techniques such as neural networks, decision trees, and support vector machines have been successfully employed to classify loan applicants into those who are likely to pay a loan off or default upon a loan. Accurate classification is beneficial to lenders in terms of increased financial profits or reduced losses and to loan applicants who can avoid overcommitment. This paper examines a historical data set from consumer loans issued by a German bank to individuals whom the bank considered to be qualified customers. The data set consists of the financial attributes of each customer and includes a mixture of loans that the customers paid off or defaulted upon. The paper examines and compares the classification accuracy rates of three decision tree techniques as well as analyzes their ability to generate easy to understand rules.

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Rule Induction Methods For Credit Scoring

Abstract: Credit scoring is the term used by the credit industry to describe methods used for classifying applicants for credit into risk classes according to their likely repayment behavior (e.g. "default" and "non-default")

Cited by 8 publications

References 17 publications

Developing a Risk Group Predictive Model for Korean Students Falling into Bad Debt

Developing a Risk Group Predictive Model for Korean Students Falling into Bad Debt

An Investigation Of The Effect Of Variable Reduction On Classification Accuracy Rates Of Consumer Loans

A Preliminary Investigation Of Decision Tree Models For Classification Accuracy Rates And Extracting Interpretable Rules In The Credit Scoring Task: A Case Of The German Data Set

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