Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Data mining: concepts and techniques
Data mining: concepts and techniques
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Introduction To Business Data Mining
Introduction To Business Data Mining
Credit Risk Scorecards: Developing And Implementing Intelligent Credit Scoring
Credit Risk Scorecards: Developing And Implementing Intelligent Credit Scoring
Credit scoring with a data mining approach based on support vector machines
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Neural nets versus conventional techniques in credit scoring in Egyptian banking
Expert Systems with Applications: An International Journal
Computational Statistics & Data Analysis
Credit scoring model based on neural network with particle swarm optimization
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Save the best for last? The treatment of dominant predictors in financial forecasting
Expert Systems with Applications: An International Journal
Credit rating using a hybrid voting ensemble
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
The bank loan approval decision from multiple perspectives
Expert Systems with Applications: An International Journal
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Credit scoring model have been developed by banks and researchers to improve the process of assessing credit worthiness during the credit evaluation process. The objective of credit scoring models is to assign credit risk to either a ''good risk'' group that is likely to repay financial obligation or a ''bad risk'' group who has high possibility of defaulting on the financial obligation. Construction of credit scoring models requires data mining techniques. Using historical data on payments, demographic characteristics and statistical techniques, credit scoring models can help identify the important demographic characteristics related to credit risk and provide a score for each customer. This paper illustrates using data mining to improve assessment of credit worthiness using credit scoring models. Due to privacy concerns and unavailability of real financial data from banks this study applies the credit scoring techniques using data of payment history of members from a recreational club. The club has been facing a problem of rising number in defaulters in their monthly club subscription payments. The management would like to have a model which they can deploy to identify potential defaulters. The classification performance of credit scorecard model, logistic regression model and decision tree model were compared. The classification error rates for credit scorecard model, logistic regression and decision tree were 27.9%, 28.8% and 28.1%, respectively. Although no model outperforms the other, scorecards are relatively much easier to deploy in practical applications.