Credit scoring models, or score cards, can contribute to bank safety and soundness as long as bank management actively participates in the development, implementation, monitoring, and validation of the model. Operational factors, such as accurate implementation of the credit scoring model and adequate training of data input staff, also can have an impact on the effectiveness of models. Changes ...

E-commerce transactions have a rapid development because of its convenience and flexibility. However, compared to the traditional market, the e-commerce transaction still could not get rid of information asymmetry between the online sellers and online buyers. Adverse selection problems brought by information asymmetry have negative impact on e-marketing and reduce the efficiency of the online t...

heretofore, a vast spectrum of pattern recognition methods has been considered by researchers to design credit scoring models. but what has not been considered enough in these studies is the lack of consideration of the needs of this subject which differentiate it from a simple classification. it is necessary to observe the costs of a wrong classification, because practically the cost of improp...

credit scoring is a classification problem leading to introducing numerous techniques to deal with it such as support vector machines, neural networks and rule-based classifiers. rule bases are the top priority in credit decision making because of their ability to explicitly distinguish between good and bad applicants.in a credit- scoring context, imbalanced data sets frequently occur as the nu...

Reliable credit scoring models played a very important role of retail banks to evaluate credit applications and it has been widely studied. The main objective of this paper is to build a hybrid credit scoring model using feature selection approach. In this study, we constructed a credit scoring model based on parallel GBM (Gradient Boosted Model), filter and wrapper approaches to evaluate the a...

In this paper, we investigate the extent to which features derived from bank statements provided by loan applicants, and which are not declared on an application form, can enhance a credit scoring model for a New Zealand lending company. Exploring the potential of such information to improve credit scoring models in this manner has not been studied previously. We construct a baseline model base...

Decision tree modelling, as one of data mining techniques, is used for credit scoring of bank customers. The main problem is the construction of decision trees that could classify customers optimally. This study presents a new hybrid mining approach in the design of an effective and appropriate credit scoring model. It is based on genetic algorithm for credit scoring of bank customers in order ...

With the rapid growth of credit industry, credit scoring model has a great significance to issue a credit card to the applicant with a minimum risk. So credit scoring is very important in financial firm like bans etc. With the previous data, a model is established. From that model is decision is taken whether he will be granted for issuing loans, credit cards or he will be rejected. There are s...

We study the adoption of automated credit scoring at a large auto finance company and the changes it enabled in lending practices. Credit scoring appears to have increased profits by roughly a thousand dollars per loan. We identify two distinct benefits of risk classification: the ability to screen high-risk borrowers and the ability to target more generous loans to lower-risk borrowers. We sho...