IMPROVING CREDIT SCORING MODEL OF MORTGAGE FINANCING WITH SMOTE METHODS IN SHARIA BANKING

Improving Credit Scoring by Generalized Additive Model

Logistic Regression has been widely used in the financial service industry for credit scoring models. Despite its advantages in easy interpretation and low computing cost, Logistic Regression is under the criticism of failure to model the nonlinear features of the predictors effect on the dependent variable and therefore might lead to unsatisfactory results. Modern statistical techniques such a...

Credit Scoring Methods

Qualitative Modelling of Credit Scoring: A Case Study in Banking

Several modelling procedures have been suggested in the literature that aim to help credit granting decisions. Most of these utilize statistical, opera tional research and artificial intelligence techniques to identify patterns among past applications, in order to enable a more well-informed assess ment of risk as well as the automation of credit scoring. For some types of loans, we find that t...

Credit Scoring using Semiparametric Methods

Credit scoring methods aim to assess credit worthiness of potential borrowers to keep the risk of credit loss low and to minimize the costs of failure over risk groups. Standard parametric approaches as logistic discrimination analysis assume that the probability of belonging to the group of ”bad” clients is given by P (Y = 1|X) = F (βX), with Y = 1 indicating a ”bad” client and X denoting the ...

Improving credit scoring by differentiating defaulter behaviour

We present a methodology for improving credit scoring models by distinguishing two forms of rational behaviour of loan defaulters. It is common knowledge among practitioners that there are two types of defaulters, those who do not pay because of cash flow problems (‘Can’t Pay’), and those that do not pay because of lack of willingness to pay (‘Won’t Pay’). This work proposes to differentiate th...