Using Neural Network Rule Extraction for Credit-Risk Evaluation
نویسندگان
چکیده
UFPR – *Mathematics; **Business Departments CP: 19081; CEP: 81531-990, Curitiba, Paraná, Brazil ***ITA – Computer Sciences Division , Pça. Mal. Eduardo Gomes, 50, Vl. das Acácias CEP: 12228-990, São José dos Campos, São Paulo, Brazil USP – Engineering Production , São Paulo, São Paulo, Brazil PUC-PR – Applied Informatics Graduate Program Av. Imaculada Conceição, 1155, CEP 80215-901, Curitiba, Paraná, Brazil
منابع مشابه
Building Credit-Risk Evaluation Expert Systems Using Neural Network Rule Extraction and Decision Tables
The problem of credit-risk evaluation is a very challenging and important financial analysis problem. Recently, researchers have found that neural networks perform very well for this complex and unstructured problem when compared to more traditional statistical approaches. A major drawback associated with the use of neural networks for decision making is their lack of explanation capability. Wh...
متن کاملThe Comparison of Credit Risk between Artificial Neural Network and Logistic Regression Models in Tose-Taavon Bank in Guilan
One of the most important issues always facing banks and financial institutes is the issue of credit risk or the possibility of failure in the fulfillment of obligations by applicants who are receiving credit facilities. The considerable number of banks’ delayed loan payments all around the world shows the importance of this issue and the necessary consideration of this topic. Accordingly...
متن کاملFuzzy 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. ...
متن کاملFuzzy 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. ...
متن کاملPredicting personal credit ratings using ubiquitous data mining
Ubiquitous data mining (UDM) is a methodology for creating new knowledge by building an integrated financial database in a ubiquitous computing environment, extracting useful rules by using diverse rule-extraction-based data mining techniques, and combining these rules. In this study, we built six credit rating forecasting models using traditional statistical methods (i.e., logistic regression ...
متن کامل