Rule Induction Methods For Credit Scoring

Credit Scoring Methods

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 ...

Using DEA for Classification in Credit Scoring

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...

Fuzzy Apriori Rule Extraction Using Multi-Objective Particle Swarm Optimization: The Case of Credit Scoring

There are many methods introduced to solve the credit scoring problem such as support vector machines, neural networks and rule based classifiers. Rule bases are more favourite in credit decision making because of their ability to explicitly distinguish between good and bad applicants.In this paper multi-objective particle swarm is applied to optimize fuzzy apriori rule base in credit scoring. ...

Fuzzy Apriori Rule Extraction Using Multi-Objective Particle Swarm Optimization: The Case of Credit Scoring

There are many methods introduced to solve the credit scoring problem such as support vector machines, neural networks and rule based classifiers. Rule bases are more favourite in credit decision making because of their ability to explicitly distinguish between good and bad applicants.In this paper multi-objective particle swarm is applied to optimize fuzzy apriori rule base in credit scoring. ...