Credit scoring or credit risk assessment is an important research issue in the banking industry. The major challenge of credit scoring is to recruit the profitable customers by predicting the bankrupts. The credit scoring carried out by traditional data driven approaches resulted only in an imprecise solution. Also the domain-driven based multiple criteria and multiple constraint (MC2) level pr...

Credit scoring models have been widely studied in the areas of statistics, machine learning, and artificial intelligence (AI). Many novel approaches such as artificial neural networks (ANNs), rough sets, or decision trees have been proposed to increase the accuracy of credit scoring models. Since an improvement in accuracy of a fraction of a percent might translate into significant savings, a m...

credit decisions are extremely vital for any type of financial institution because it can stimulate huge financial losses generated from defaulters. credit scoring models are decision support systems that take a set of predictor variables as input and provide a score as output and creditors use these models to justify who will get credit and who will not. many different credit scoring models ha...

Credit scoring is a kind of binary classification problem that contains important information for manager to make a decision in particularly in banking authorities. Obtained scores provide a practical credit decision for a loan officer to classify clients to reject or accept for payment loan. For this sake, in this paper a data envelopment analysis- discriminant analysis (DEA-DA) approach is us...

The aim of the paper is to discuss credit scoring modeling of a customer revolving credit depending on customer application data and transaction behavior data. Logistic regression, survival analysis, and neural network credit scoring models were developed in order to assess relative importance of different variables in predicting the default of a customer. Three neural network algorithms were t...

Credit scoring models play a fundamental role in the risk management practice at most banks. They are used to quantify credit risk at counterparty or transaction level in the different phases of the credit cycle (e.g. application, behavioural, collection models). The credit score empowers users to make quick decisions or even to automate decisions and this is extremely desirable when banks are ...

U.S. commercial banks are increasingly using credit scoring models to underwrite small business credits. This paper discusses this technology, evaluates the research findings on the effects of this technology on small business credit availability, and links these findings to a number of research and public policy issues. JEL classification: G21, G28, G34, L23

Credit scoring has become very important issue due to the recent growth of the credit industry, so the credit department of the bank faces a large amount of credit data. Clearly it is impossible analyzing this huge amount of data both in economic and manpower terms, so data mining techniques were employed for this purpose. So far many data mining methods are proposed to handle credit scoring pr...